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Dev Tools & APIs

Articles, blogs, guides and how-tos about business process automation.

  • Pinecone: Load

    Pinecone: Load

    Pinecone: Load is an n8n node that lets you insert vector embeddings into Pinecone, a fully managed vector database. When building AI applications that need to search through your own documents, product catalogues, or knowledge bases, you need somewhere to store the embeddings that represent your data. This node handles that storage step, taking vectors from your n8n workflow and loading them into a Pinecone index for later retrieval. Pinecone is popular because it removes the operational overhead of managing vector infrastructure. You do not need to worry about indexing, sharding, or scaling — Pinecone handles all of that. The n8n node supports batch upserts with metadata, namespace isolation, and configurable vector dimensions, making it straightforward to build production-grade RAG pipelines entirely within n8n. Our team at Osher Digital has implemented Pinecone-backed search systems for clients who prefer managed infrastructure over self-hosted options. For custom AI development projects involving document retrieval or semantic search, Pinecone paired with n8n provides a reliable foundation. We also use it in AI agent development where agents need to reference large knowledge bases. If you need help designing your vector storage architecture or choosing between managed and self-hosted options, get in touch with our team.
    Pinecone: Load consultants
  • Contextual Compression Retriever

    Contextual Compression Retriever

    The Contextual Compression Retriever node makes your AI retrieval workflows sharper by filtering and compressing retrieved documents before they reach your language model. Standard vector store retrieval often pulls back chunks that are mostly irrelevant — maybe only one sentence in a 500-word passage actually answers the question. This node strips away the noise, keeping only the parts that matter for the current query. For businesses building retrieval-augmented generation (RAG) systems in n8n, this is a practical upgrade. Instead of stuffing your LLM context window with loosely related text and hoping it figures out what is relevant, the Contextual Compression Retriever pre-processes the results. The language model receives focused, relevant excerpts, which means better answers, fewer hallucinations, and lower token costs per request. This matters most when you are working with large knowledge bases — internal documentation, product catalogues, compliance manuals, or client records. Australian businesses running AI agent systems for customer support or internal knowledge management see a direct improvement in answer quality when they add contextual compression to their retrieval pipeline. The difference is especially noticeable when questions are specific and the knowledge base is broad. The node works by wrapping an existing retriever (like a vector store retriever) and applying a compression step powered by a language model. You configure it once, connect it to your existing RAG chain, and every retrieval query automatically benefits from tighter, more relevant context. It is a relatively small change to your workflow that produces a measurable lift in output quality.
    Contextual Compression Retriever consultants
  • Read/Write Files from Disk

    Read/Write Files from Disk

    The Read/Write Files from Disk node gives your n8n workflows direct access to the server file system. It reads files into your workflow for processing and writes output files back to disk — handling everything from CSVs and JSON files to images, PDFs, and binary data. If your automation needs to pick up files from a directory, transform them, and save the results somewhere, this is the node that makes it happen. For businesses running self-hosted n8n instances, this node unlocks a whole category of automations that cloud-only platforms struggle with. Process invoices dropped into a shared folder, read configuration files that control workflow behaviour, generate reports and save them to a network drive, or create backup copies of important data. The node works with any file type and supports both reading single files and scanning entire directories. Australian companies managing automated data processing pipelines find this node essential for bridging the gap between file-based systems and API-driven workflows. Many legacy systems — accounting software, inventory management, government reporting tools — still rely on file exports and imports. This node lets n8n sit in the middle, reading those exports, transforming the data, and writing it back in the format the next system expects. The node is also valuable for AI development workflows where you need to read training data, save model outputs, or log results to disk for later analysis. Combined with n8n’s scheduling capabilities, you can build fully automated file processing pipelines that run on a timer without any manual intervention.
    Read/Write Files from Disk consultants
  • JWT

    JWT

    The JWT node in n8n handles JSON Web Token operations — creating, signing, decoding, and verifying JWTs within your automation workflows. If your systems use token-based authentication (and most modern APIs do), this node lets you generate and validate tokens without writing custom code or maintaining separate authentication microservices. JWTs are the backbone of secure API communication. They carry encoded claims about a user or system, signed with a secret or key pair so the receiving party can verify authenticity. The JWT node brings this capability directly into n8n, which means your workflows can authenticate with external APIs that expect bearer tokens, generate tokens for webhooks you expose, or validate incoming tokens from third-party systems. For Australian businesses managing system integrations across multiple platforms, the JWT node solves a common pain point: how to handle authentication between systems that do not share a native integration. Need to call a partner API that requires a signed JWT? Generate it on the fly. Building a webhook endpoint that should only accept requests from verified senders? Validate the incoming token before processing the payload. Connecting a legacy system that uses custom JWT claims? Decode and extract what you need. The node supports HMAC (HS256, HS384, HS512) and RSA (RS256, RS384, RS512) signing algorithms, covering the vast majority of real-world JWT requirements. It handles both symmetric and asymmetric key scenarios, so whether you are working with shared secrets or public/private key pairs, the node has you covered. Combined with n8n’s credential management, your signing keys stay secure and separate from your workflow logic.
    JWT consultants
  • Redis Chat Memory

    Redis Chat Memory

    The Redis Chat Memory node gives your n8n AI workflows persistent conversation memory using Redis as the storage backend. When you build chatbots or AI agents, the language model has no built-in memory between requests — every interaction starts fresh. This node solves that by storing and retrieving conversation history from Redis, so your AI can reference what was said earlier in the conversation and respond with full context. Redis is a natural fit for chat memory because it is fast, lightweight, and designed for exactly this kind of ephemeral-but-important data. Conversation histories do not need the overhead of a relational database, but they do need low-latency read and write access. Redis delivers sub-millisecond response times, which means your AI agent can load a full conversation history without adding noticeable delay to the user experience. For Australian businesses deploying AI agents for customer support, internal helpdesks, or sales qualification, conversation memory is not optional — it is what separates a useful assistant from a frustrating one. Customers expect the AI to remember their name, their issue, and what has already been discussed. The Redis Chat Memory node makes this work reliably, even across multiple workflow executions and server restarts. The node supports session-based memory with configurable keys, so you can maintain separate conversation threads for different users, channels, or topics. Set a TTL (time to live) to automatically expire old conversations and keep your Redis instance lean. It integrates directly with n8n’s AI agent and chain nodes, requiring minimal configuration to add persistent memory to any conversational workflow.
    Redis Chat Memory consultants
  • Workflow Retriever

    Workflow Retriever

    The Workflow Retriever node lets your AI agents and chains pull information from other n8n workflows as if they were knowledge sources. Instead of connecting to a vector database or external API for retrieval, this node calls a separate n8n workflow that returns the relevant documents or data. It turns any workflow into a retrievable knowledge source for your RAG (retrieval-augmented generation) pipelines. This opens up retrieval patterns that are not possible with standard vector store approaches. Your retriever workflow can query a database, call an API, read from a spreadsheet, filter results based on business logic, or combine data from multiple sources — all before returning the results to your AI chain. The flexibility is significant: you are not limited to similarity search against embeddings. You can build any retrieval logic you want. For businesses running complex system integrations, this is a practical way to give AI agents access to live business data. An AI customer support agent could retrieve the latest order status from your ERP, current stock levels from your warehouse system, and relevant policy documents from your knowledge base — all through separate retriever workflows that each handle their own data source and transformation logic. The pattern also keeps your workflows modular and maintainable. Each retriever workflow is self-contained with its own error handling, credentials, and logic. When a data source changes its API or schema, you update one retriever workflow without touching your main AI agent workflow. Teams at Osher use this pattern extensively in n8n consulting projects where AI agents need access to multiple business systems simultaneously.
    Workflow Retriever consultants
  • Supabase: Insert

    Supabase: Insert

    The Supabase Insert node writes data directly from your n8n workflows into Supabase tables. Supabase is an open-source Firebase alternative built on PostgreSQL, and this node gives you a clean, code-free way to push records into it. Whether you are capturing form submissions, logging webhook events, storing processed data, or building up a dataset from multiple sources, this node handles the database write operation without requiring SQL knowledge. What makes this node valuable in automation contexts is its simplicity. You map your workflow data fields to Supabase table columns, and the node handles the insert operation including type conversion and error handling. Combine it with n8n’s data transformation nodes and you can clean, validate, and enrich data before it hits your database — ensuring data quality at the point of entry rather than cleaning up afterwards. For Australian businesses using Supabase as their backend, this node bridges the gap between external events and your database. A customer fills out a form on your website — the data flows through n8n, gets validated and enriched, and lands in your Supabase table ready for your application to use. An AI agent classifies incoming support tickets — the classifications get written to Supabase where your support dashboard picks them up. These are the kinds of automated data processing pipelines that save hours of manual data entry every week. The node supports single-row and batch inserts, and works alongside other Supabase nodes for reading, updating, and deleting data. Combined, they give you full CRUD capabilities over your Supabase database from within n8n, making it straightforward to build complete data management workflows without a custom backend. Teams using Supabase as part of their AI development stack will find this node essential for persisting model outputs and application state.
    Supabase: Insert consultants
  • Cohere Model

    Cohere Model

    The Cohere Model node in n8n connects your workflows to Cohere’s language AI platform. Cohere specialises in enterprise-grade text understanding — classification, embeddings, reranking, and retrieval-augmented generation — built for production reliability rather than conversational novelty. The node handles API authentication and request formatting so you can plug Cohere models into your business automation workflows directly. Where Cohere stands apart from general-purpose chat models is its focus on search and retrieval quality. The Cohere Rerank model is particularly valuable in RAG pipelines — it takes a set of candidate documents retrieved from a vector store and reorders them by actual relevance to the query, dramatically improving the accuracy of AI-generated answers. If your retrieval pipeline returns ten documents but only three are truly relevant, Cohere Rerank surfaces those three at the top. For data processing workflows, Cohere’s classification and embedding models are strong choices. The classification endpoint lets you categorise text with just a few training examples, which is faster to set up than fine-tuning a model. The embedding models produce high-quality vector representations for semantic search, clustering, and deduplication tasks across large document sets. If you are building search, classification, or document processing workflows and want to evaluate whether Cohere’s specialised models could improve your results compared to general-purpose alternatives, our AI consulting team can run a comparison on your actual data.
    Cohere Model consultants
  • Embeddings OpenAI

    Embeddings OpenAI

    The Embeddings OpenAI node converts text into numerical vector representations using OpenAI’s embedding models. These vectors capture the semantic meaning of your text, enabling similarity search, clustering, and retrieval-augmented generation (RAG) across your data. Every RAG workflow in n8n that uses OpenAI for embeddings runs through this node — it is the bridge between your raw text and your vector database. In practical terms, embeddings power the “search” side of any AI knowledge base or question-answering system. When you load documents into a vector store, the Embeddings OpenAI node converts each chunk of text into a vector. When a user asks a question, the same node converts that question into a vector. The vector store then finds the document chunks closest in meaning to the question, and those chunks get fed to an AI model as context for generating an answer. The current model, text-embedding-3-small, offers strong performance at low cost. For workflows processing thousands of documents, embedding costs are typically a fraction of the generation costs. We have used this pattern in data pipeline projects and document classification systems where the quality of embeddings directly affects how well the AI finds and uses relevant information. If you are building a knowledge base, document search, or RAG system and need the embedding layer set up properly, our custom AI development team can design the vector pipeline from document ingestion through to accurate retrieval.
    Embeddings OpenAI consultants
  • Zep Vector Store

    Zep Vector Store

    The Zep Vector Store node in n8n connects your workflows to Zep’s purpose-built memory and vector storage platform. Zep handles both long-term document storage for RAG systems and conversation memory for AI agents, making it a two-in-one solution for workflows that need both capabilities. The node manages document insertion, vector search, and memory retrieval without requiring separate infrastructure for each function. What sets Zep apart from general-purpose vector databases is its focus on AI application needs. It automatically handles document chunking, embedding generation, and metadata indexing — tasks that typically require separate nodes in your n8n workflow. When you store a document in Zep, it processes and indexes the content on the server side, reducing the complexity of your workflow and the number of API calls to embedding providers like OpenAI. For teams building AI agents that need both knowledge base access and conversation memory, Zep simplifies the architecture considerably. Instead of connecting separate vector store, embedding, and memory nodes, you connect one Zep node that handles all three roles. We have found this approach particularly effective for internal knowledge bots and customer support agents where the agent needs to search company documents while maintaining conversation context. If you are building a RAG system or conversational AI agent and want to reduce infrastructure complexity, our n8n consulting team can help you evaluate whether Zep is the right fit for your workflow architecture.
    Zep Vector Store consultants
  • Embeddings Azure OpenAI

    Embeddings Azure OpenAI

    The Embeddings Azure OpenAI node generates text embeddings through Microsoft’s Azure-hosted OpenAI service. It provides the same embedding models as OpenAI — text-embedding-3-small and text-embedding-3-large — but runs them within your Azure subscription where you control the region, networking, and access policies. For organisations that already use Azure or have enterprise agreements with Microsoft, this node keeps your AI workloads consolidated under one cloud provider. The primary reason businesses choose Azure OpenAI over the standard OpenAI API is control. Your data stays within your Azure tenant and the region you select. Network traffic can stay on private endpoints rather than traversing the public internet. Access is governed by Azure Active Directory rather than a simple API key. These characteristics matter for regulated industries and organisations with strict IT governance requirements. In n8n, the node works identically to the standard Embeddings OpenAI node — it converts text into vectors for storage in vector databases and powers the retrieval side of RAG pipelines. The difference is purely in where the model runs and how authentication works. Pair it with any vector store node in n8n to build knowledge bases, document search systems, and AI agent memory layers that comply with your organisation’s Azure policies. If your organisation runs on Azure and needs to build AI-powered search or document processing workflows within your existing cloud governance, our custom AI development team can architect a solution that meets your compliance requirements while delivering practical business results.
    Embeddings Azure OpenAI consultants
  • Auto-fixing Output Parser

    Auto-fixing Output Parser

    The Auto-fixing Output Parser node solves one of the most common headaches in AI-powered workflows: getting structured data out of a language model that insists on returning messy, inconsistent responses. When you ask an LLM to return JSON or follow a specific schema, it often adds extra text, misses fields, or wraps the output in markdown code fences. This node catches those errors and automatically corrects them before your downstream nodes choke on bad data. In production n8n workflows, unreliable AI output is not just annoying — it breaks entire automations. A single malformed JSON response can halt a pipeline that processes customer orders, routes support tickets, or updates your CRM. The Auto-fixing Output Parser acts as a safety net, intercepting the raw model output and using a secondary LLM call to repair it against your defined schema. Your workflow keeps running even when the AI gets creative with formatting. This node is particularly valuable for Australian businesses running automated data processing pipelines where accuracy matters. Think invoice extraction, lead qualification, or medical form parsing — tasks where the output needs to conform to an exact structure every single time. Instead of building elaborate error handling logic, you let the parser handle the messiness so your team can focus on what happens with the clean data. Pair it with any chat model node (OpenAI, Anthropic, Groq) and a structured output parser, and you have a robust chain that delivers reliable structured data from free-text AI responses. It is one of those nodes that does not look exciting on paper but saves you hours of debugging in practice.
    Auto-fixing Output Parser consultants
  • AWS Bedrock Chat Model

    AWS Bedrock Chat Model

    The AWS Bedrock Chat Model node connects your n8n workflows to Amazon’s managed AI model service. Instead of running your own inference infrastructure, you call models from Anthropic, Meta, Mistral, and Amazon directly through the Bedrock API. The node handles authentication, request formatting, and response parsing so you can focus on what the model actually does inside your automation pipeline. Bedrock is the go-to choice for organisations that already run infrastructure on AWS or have strict data residency requirements. Your prompts and responses stay within your chosen AWS region, which matters for regulated industries like finance, healthcare, and government. We helped an Australian healthcare organisation use Bedrock-hosted models for document classification precisely because the data never left the ap-southeast-2 region. In n8n, the Bedrock Chat Model node plugs into any workflow that needs language understanding or generation. Pair it with a Chat Trigger for conversational agents, chain it with document loaders for retrieval-augmented generation, or use it standalone for tasks like summarisation, extraction, or content drafting. You choose which foundation model to call — Claude, Llama, or Mistral — and configure parameters like temperature and token limits per node. If your team needs to deploy AI capabilities within AWS guardrails, our custom AI development practice can architect a solution that meets your compliance and performance requirements.
    AWS Bedrock Chat Model consultants
  • GitHub Document Loader

    GitHub Document Loader

    The GitHub Document Loader node in n8n pulls files directly from GitHub repositories into your workflow. It reads source code, documentation, configuration files, and any other text-based content stored in a repo, then passes that content downstream for processing. This is the node you reach for when your automation needs to work with code or documentation that lives in version control. The most common use case is building retrieval-augmented generation (RAG) systems that answer questions about your codebase. Feed repository contents through the GitHub Document Loader into a vector store, then let an AI model search that store when developers or stakeholders ask questions. Instead of digging through repos manually, your team gets answers from a chat interface backed by your actual code and docs. Beyond RAG, the loader is useful for automated code review pipelines, documentation generators, and compliance checks that need to scan repository contents on a schedule. Pair it with AI model nodes to analyse code quality, check for security patterns, or generate summaries of recent changes. We have built similar pipelines for teams that need to keep technical documentation in sync with their codebase using system integration workflows. If your development team is drowning in context-switching between repositories and wants to automate how they access and process code, our AI agent development team can build a solution that fits your workflow.
    GitHub Document Loader consultants
  • Hugging Face Inference Model

    Hugging Face Inference Model

    The Hugging Face Inference Model node in n8n gives your workflows access to thousands of open-source AI models hosted on the Hugging Face Hub. Instead of deploying and managing models yourself, you call them through Hugging Face’s Inference API and get results back in your workflow. The node supports text generation, classification, summarisation, translation, and other natural language tasks depending on which model you choose. The real value of Hugging Face in an n8n context is model diversity. Need a specialised model for sentiment analysis in a particular industry? There is probably one on the Hub trained on relevant data. Need a multilingual model that handles Japanese and English in the same workflow? You can find that too. This flexibility makes Hugging Face a strong choice when off-the-shelf models from OpenAI or Google do not quite fit your use case. For data processing workflows, Hugging Face models are particularly useful for classification and extraction tasks where a purpose-built model outperforms a general-purpose one. We have seen teams use specialised NER (named entity recognition) models to pull structured data from unstructured documents with higher accuracy than prompting a general chat model. The node also supports custom models deployed to Hugging Face Inference Endpoints for production workloads. If you want to explore whether a specialised open-source model could improve your automation accuracy, our custom AI development team can evaluate the options and integrate the best fit into your n8n workflows.
    Hugging Face Inference Model consultants
  • Motorhead

    Motorhead

    The Motorhead node in n8n provides a managed memory backend for conversational AI workflows. When you build chat-based automations, the AI model needs to remember what was said earlier in the conversation. Motorhead stores and retrieves that conversation history, so your AI agent can reference previous messages without you building a custom database layer for session management. Without a proper memory layer, every message in a conversation is treated as a brand new interaction. The AI has no idea what the user said two messages ago, which breaks any workflow that involves multi-turn conversations — support chats, onboarding flows, data collection sequences, and advisory interactions all fall apart. Motorhead solves this by maintaining a persistent memory store keyed to each conversation session. In n8n, Motorhead connects to AI agent and chain nodes as a memory provider. When the AI model processes a new message, it pulls the conversation history from Motorhead, includes that context in its prompt, and then stores the new exchange back. This happens automatically within the workflow without extra code. The node works alongside any AI model node — OpenAI, Google Gemini, or AWS Bedrock. If you are building conversational AI agents that need to handle multi-step interactions reliably, our AI agent development team can design the memory architecture and workflow logic to keep conversations coherent and useful.
    Motorhead consultants
  • Flow Trigger

    Flow Trigger

    Flow Trigger is the event-listening counterpart to the Flow node in n8n. While the Flow node lets you send data to Microsoft Power Automate, Flow Trigger starts your n8n workflow when something happens in a Power Automate flow. This means actions taken inside the Microsoft ecosystem — such as a new SharePoint file upload, a Teams message, or an Outlook event — can automatically kick off processing in n8n. This trigger is essential for organisations that want n8n to react to Microsoft 365 events without polling. Instead of periodically checking for changes, Flow Trigger receives a push notification from Power Automate the moment something happens. For example, when a new document lands in a SharePoint folder, Power Automate can instantly notify n8n to extract data from that document, process it through AI nodes, and update an external database — all within seconds of the upload. At Osher, we’ve built automated data processing workflows that use Flow Trigger as the entry point from Microsoft environments. One common pattern is document processing: a file arrives in SharePoint, triggers a Power Automate flow, which calls n8n via Flow Trigger, and n8n handles the heavy lifting — OCR, data extraction, validation, and routing to the appropriate system. It’s a clean separation of responsibilities that keeps both platforms doing what they do best.
    Flow Trigger consultants
  • Citrix ADC

    Citrix ADC

    Citrix ADC (Application Delivery Controller, formerly NetScaler) is an enterprise-grade networking and application delivery platform used for load balancing, traffic management, and application security. The n8n Citrix ADC node lets you interact with your ADC infrastructure programmatically — managing SSL certificates, configuring server resources, and monitoring system status from within your automation workflows. For IT and DevOps teams managing Citrix ADC deployments, this node eliminates the need to manually log into the management console for routine operations. Certificate renewals, server pool updates, and configuration changes can be automated and triggered by events from other systems. When combined with monitoring alerts, you can build workflows that automatically respond to infrastructure events — for example, adding a server to a load-balanced pool when demand spikes, or rotating SSL certificates before they expire. Our robotic process automation team at Osher has worked with enterprise IT departments to automate infrastructure management tasks that were previously handled through manual console interactions or custom scripts. The Citrix ADC node in n8n provides a clean, no-code interface to these operations, making infrastructure automation accessible to a broader team rather than being limited to those comfortable writing API calls against the Citrix NITRO API directly.
    Citrix ADC consultants
  • AWS ELB

    AWS ELB

    AWS Elastic Load Balancing (ELB) distributes incoming traffic across multiple targets — EC2 instances, containers, and IP addresses — to keep your applications available and responsive. The n8n node lets you interact with your AWS ELB configuration programmatically, checking health statuses, registering and deregistering targets, and retrieving load balancer details from within your automation workflows. Where this gets practical is in deployment and incident response automation. During a deployment, n8n can deregister an instance from the load balancer, wait for connections to drain, trigger the deployment on that instance, run health checks, and re-register it — all as a rolling update workflow. If a health check fails, n8n can automatically isolate the unhealthy instance, alert your ops team, and spin up a replacement. For teams running infrastructure on AWS, load balancer management is often handled through the console or CLI scripts that someone has to remember to run. Wrapping those operations in n8n workflows makes them repeatable, auditable, and easy to trigger from other events. We helped a client build infrastructure automation for a data pipeline that included similar health-check and failover logic. If your team manages AWS load balancers and wants to automate deployments, health monitoring, or scaling operations, the n8n AWS ELB node gives you the building blocks. Our systems integration team can design infrastructure automation workflows that fit your AWS architecture and operational processes.
    AWS ELB consultants
  • Affinity Trigger

    Affinity Trigger

    Affinity Trigger lets you launch n8n workflows automatically when changes happen in your Affinity CRM. Whether a new organisation is added, a deal moves to a new stage, or a contact’s details are updated, this trigger node detects the event and starts your automation immediately. It’s the real-time connection between your relationship intelligence platform and the rest of your operational stack. Affinity is popular with venture capital firms, private equity teams, and professional services businesses that manage high-value relationships. The Trigger node is particularly valuable for these teams because deal flow moves fast — when a prospect enters a new pipeline stage, you often need to update multiple systems, notify team members, or generate documents without delay. Connecting Affinity to n8n through this trigger means those downstream actions happen automatically. At Osher, we’ve built AI-powered agent workflows that use Affinity Trigger as their starting point — for example, automatically enriching new contacts with public data, scoring deals based on custom criteria, or syncing pipeline changes to external reporting dashboards. If your team relies on Affinity for relationship management and you’re spending time manually keeping other tools in sync, this trigger node is the foundation for eliminating that overhead.
    Affinity Trigger consultants
  • TravisCI

    TravisCI

    TravisCI is a continuous integration and delivery platform, and the n8n TravisCI node lets you interact with your CI/CD pipelines directly from automation workflows. You can trigger builds, check build statuses, cancel running jobs, and pull build logs — all without switching between tools or writing custom API scripts. It brings your deployment pipeline into the same automation layer as the rest of your business operations. For development teams, the value is in connecting CI/CD events to broader operational workflows. When a build fails, you can automatically notify the right people on Slack, create a bug ticket in Jira, or pause a deployment pipeline until the issue is resolved. When a build succeeds, you can trigger downstream processes like updating a staging environment, notifying QA, or logging the release in a changelog system. We’ve worked with development teams who use n8n to bridge the gap between their code pipelines and their business tools. TravisCI integration is a common piece of that puzzle — especially for teams who want to reduce context-switching and ensure that build events are acted on immediately rather than buried in a dashboard that someone might check an hour later. If you’re running TravisCI and want to connect it to the rest of your stack, this node makes that straightforward.
    TravisCI consultants
  • Venafi TLS Protect Datacenter

    Venafi TLS Protect Datacenter

    Venafi TLS Protect Datacenter manages the lifecycle of TLS/SSL certificates across your infrastructure — discovery, issuance, renewal, and revocation. The n8n node lets you automate certificate operations, pulling certificate details, requesting new certificates, and triggering renewal workflows programmatically instead of manually tracking expiry dates in spreadsheets. Certificate management is one of those things that works fine until it does not. An expired certificate takes down a production service, and suddenly your team is scrambling at 2 AM. Automating the lifecycle through n8n means you can build workflows that check expiry dates weeks in advance, request renewals automatically, deploy the new certificates to your servers, and notify the security team — all without someone remembering to check a dashboard. For organisations with compliance requirements, the audit trail matters as much as the automation. n8n workflows can log every certificate action to your SIEM or compliance platform, creating a verifiable record of who requested what and when it was issued. We have helped clients in regulated industries build similar compliance-integrated automation that satisfies auditors without slowing down operations. If your infrastructure team is managing certificates manually or you have had outages caused by expired certs, automating through Venafi and n8n removes human error from the equation. Our custom development team can build certificate lifecycle workflows tailored to your infrastructure and compliance needs.
    Venafi TLS Protect Datacenter consultants
  • Cloudflare

    Cloudflare

    Cloudflare provides DNS management, CDN, DDoS protection, and web application firewall services for websites and APIs. The n8n node lets you manage DNS records, purge caches, and interact with Cloudflare’s API programmatically — turning manual dashboard operations into automated, repeatable workflows. The most common use case is automated DNS management. When you spin up a new service, environment, or subdomain, n8n can create the DNS records in Cloudflare automatically as part of your deployment pipeline. When you decommission something, the records get cleaned up. No more tickets to the infrastructure team for DNS changes that should take seconds, not days. Cache management is another area where automation pays off. After deploying a content update, n8n can purge the relevant Cloudflare cache so users see the new version immediately. You can also schedule regular cache purges, monitor for specific security events, or automate firewall rule updates in response to detected threats. One of our clients had a data pipeline project where cache management was critical to ensuring fresh data reached downstream consumers. If your team manages multiple domains or frequently updates DNS records and firewall rules, automating Cloudflare through n8n removes a repetitive manual step from your operations. Our systems integration team can build Cloudflare automation into your existing deployment and infrastructure management workflows.
    Cloudflare consultants
  • Pushcut Trigger

    Pushcut Trigger

    Pushcut Trigger connects iOS device actions and notifications to n8n workflows. When a notification is tapped, a widget is activated, or a Shortcuts automation runs on your iPhone or iPad, the trigger fires and passes the context into n8n for processing. It bridges the gap between Apple’s ecosystem and server-side automation. This is particularly handy for teams that need human-in-the-loop approvals on mobile. Picture a workflow where a purchase order lands in your system, n8n sends a Pushcut notification to the approver’s phone, they tap “Approve” or “Reject,” and the trigger fires the next step — updating the database, notifying the requester, and logging the decision. No app switching, no email chains. Developers and ops teams also use it to trigger server-side tasks from their phone. Run a deployment, kick off a data sync, or restart a service — all from a notification action that feeds into n8n. It turns your phone into a lightweight control panel for backend processes. If you are building workflows that need mobile input or iOS-triggered actions, Pushcut Trigger gives you a clean way to connect those moments to the rest of your automation stack. Our AI agent development team has integrated similar mobile-triggered workflows for clients who need rapid human decisions woven into automated processes.
    Pushcut Trigger consultants
  • CircleCI

    CircleCI

    CircleCI is a continuous integration and delivery platform that automates building, testing, and deploying code. The n8n CircleCI node lets you interact with CircleCI pipelines programmatically — triggering builds, checking pipeline status, fetching job results, and pulling artefact information directly from your automation workflows. For development teams, the value is in connecting CI/CD events to the rest of your operational stack. When a build fails, n8n can automatically create a Jira ticket, post to a Slack channel with the failure details, and page the on-call engineer. When a deployment succeeds, it can update your release tracker, notify stakeholders, and trigger downstream smoke tests — all without anyone logging into the CircleCI dashboard. We have seen this pattern work well in teams that manage multiple repositories or microservices. Rather than each developer watching their own builds, n8n aggregates the CI/CD events and routes them intelligently. Our systems integration team built a similar pipeline monitoring setup for a client managing dozens of services, cutting their incident response time significantly. If your engineering team spends time manually checking build statuses or copying deployment results between tools, wiring CircleCI into n8n can reclaim those hours. Our n8n consultants can design CI/CD automation workflows tailored to your development process and toolchain.
    CircleCI consultants
  • Venafi TLS Protect Cloud

    Venafi TLS Protect Cloud

    Venafi TLS Protect Cloud is a certificate lifecycle management platform that gives security and DevOps teams visibility and control over their TLS/SSL certificates across cloud, hybrid, and on-premise environments. It automates the discovery, issuance, renewal, and revocation of certificates — reducing the risk of outages caused by expired certificates and the security gaps that come from unmanaged cryptographic assets. Infrastructure teams, security engineers, and compliance officers rely on Venafi TLS Protect Cloud to maintain an accurate certificate inventory, enforce policy compliance, and automate renewal workflows before certificates expire. With the shift toward shorter certificate lifespans and zero-trust architectures, manual certificate management simply does not scale. Osher integrates Venafi TLS Protect Cloud into operational workflows using n8n, connecting certificate events to alerting systems, ticketing platforms, and compliance dashboards. When a certificate is nearing expiry, when a policy violation is detected, or when a new certificate is issued, automated workflows ensure the right people are notified and the right actions are taken without delay. Learn more about our AI agent development capabilities or explore our system integration services.
    Venafi TLS Protect Cloud consultants
  • AWS Certificate Manager

    AWS Certificate Manager

    AWS Certificate Manager (ACM) is Amazon’s managed service for provisioning, managing, and deploying TLS/SSL certificates used with AWS services and internal resources. ACM handles certificate issuance and automatic renewal for certificates attached to Elastic Load Balancers, CloudFront distributions, API Gateway endpoints, and other AWS services — removing the operational burden of manual certificate management. DevOps engineers, cloud architects, and infrastructure teams use ACM to secure their AWS-hosted applications without worrying about certificate expiry or renewal logistics. Public certificates issued through ACM are free, and the automatic renewal feature eliminates one of the most common causes of production outages — forgotten certificate renewals. Osher integrates AWS Certificate Manager into broader infrastructure and compliance workflows using n8n. We build automations that monitor certificate status across your AWS accounts, alert teams when manual intervention is required (such as DNS validation for new certificates), and log certificate lifecycle events into compliance dashboards. This is especially valuable for organisations managing certificates across multiple AWS accounts or regions. Learn more about our system integration services or explore our custom development capabilities.
    AWS Certificate Manager consultants
  • Mocean

    Mocean

    Mocean is a cloud communications API platform that enables businesses to send and receive SMS, voice calls, and verification messages programmatically. It provides a straightforward API for integrating messaging capabilities into applications, workflows, and automated processes — without the complexity of building carrier-level infrastructure from scratch. Development teams, operations managers, and customer-facing businesses use Mocean to power transactional SMS (order confirmations, appointment reminders, OTP codes), voice notifications, and two-way messaging. Its API-first design makes it a practical choice for businesses that need messaging baked into their systems rather than sent manually from a web dashboard. At Osher, we integrate Mocean into automated workflows using n8n, turning messaging into a seamless part of your business processes. Order confirmations go out the moment a purchase is completed. Appointment reminders are sent at configurable intervals. Verification codes are generated and delivered without developer involvement each time. We connect Mocean to your CRM, booking system, or custom application so messaging happens automatically at exactly the right moment. Learn more about our business automation services or see how we approach custom development projects.
    Mocean consultants
  • Venafi TLS Protect Cloud Trigger

    Venafi TLS Protect Cloud Trigger

    Venafi TLS Protect Cloud Trigger is a workflow automation node that monitors Venafi’s cloud-based TLS certificate management platform for certificate lifecycle events. When certificates are issued, renewed, revoked, or approach expiration, the trigger fires and initiates an automated workflow. If your organisation manages TLS certificates across multiple domains and services, this trigger turns certificate events into automated actions that keep your security posture tight. Venafi TLS Protect Cloud is used by IT security teams, DevOps engineers, and compliance officers in organisations that manage large certificate estates. Financial services, healthcare, government, and any industry with strict compliance requirements relies on tools like Venafi to prevent certificate-related outages and security gaps. Manual certificate management at scale is a recipe for missed renewals and downtime — automation eliminates that risk. At Osher, we integrate Venafi TLS Protect Cloud Trigger into n8n workflows that connect certificate events to your IT operations. For example, when a certificate nears expiration, the workflow can create a Jira ticket, notify the responsible team via Slack, and log the event in your compliance tracking system — all automatically. This kind of event-driven security automation reduces the chance of expired certificates causing service outages. If your team is manually tracking certificate renewals or struggling to maintain visibility across your certificate estate, our system integration and custom development teams can build automated workflows that keep your certificates — and your compliance — under control.
    Venafi TLS Protect Cloud Trigger consultants
  • CrateDB

    CrateDB

    CrateDB is a distributed SQL database built for machine data, time-series data, and real-time analytics at scale. It combines the familiarity of SQL with the scalability of a NoSQL architecture, making it a practical choice for teams that need to query large volumes of structured and semi-structured data without giving up standard SQL syntax. If your business generates high-volume data from IoT sensors, application logs, or operational systems and you need fast query performance, CrateDB is designed for exactly that workload. Manufacturing companies, IoT platform operators, logistics firms, and data engineering teams are among CrateDB’s core users. The database handles millions of inserts per second and supports full-text search, geospatial queries, and aggregations — all through standard SQL. It runs on-premise or in the cloud, and its distributed architecture means you can scale horizontally as your data volumes grow. At Osher, we integrate CrateDB into data pipelines and automation workflows using n8n. That might mean feeding IoT sensor data into CrateDB for real-time monitoring, syncing operational data from multiple sources into a central CrateDB instance for reporting, or triggering alerts when query results cross defined thresholds. We built a similar real-time data pipeline for an insurance tech company processing weather data at volume. If your team is struggling with slow queries on large datasets or needs a scalable database for time-series and machine data, our data processing and system integration teams can help you deploy and integrate CrateDB into your stack.
    CrateDB consultants
  • PostHog

    PostHog

    PostHog is an open-source product analytics platform that gives engineering and product teams full visibility into how users interact with their software. It covers event tracking, session recordings, feature flags, A/B testing, and funnel analysis — all self-hostable so your data stays on your own infrastructure. If you are tired of sending user behaviour data to third-party analytics vendors, PostHog lets you keep everything in-house. Product-led SaaS companies, developer tool makers, and data-conscious startups are PostHogs core audience. The platform is particularly popular with teams that want granular control over their analytics pipeline without being locked into proprietary tools. Its open-source model means you can inspect the code, extend it, and deploy it however you like. At Osher, we integrate PostHog into broader data and automation workflows using n8n. That could mean piping product usage events into a CRM for sales follow-up, triggering alerts when key metrics drop, or feeding PostHog data into custom dashboards. We built a similar data pipeline for an insurance tech company that needed real-time data processing across multiple sources. If your product team needs better analytics without handing data to a third party, our data processing and system integration teams can set up PostHog and connect it to your existing stack.
    PostHog consultants
  • AMQP Sender

    AMQP Sender

    AMQP Sender is a messaging node used in workflow automation platforms like n8n to send messages to AMQP-compatible message brokers such as RabbitMQ, Apache Qpid, and Azure Service Bus. It implements the Advanced Message Queuing Protocol, which is the industry standard for reliable, asynchronous message passing between distributed systems. If your architecture relies on message queues to decouple services, AMQP Sender is how you push messages into those queues from automated workflows. Engineering teams working with microservices, event-driven architectures, and data pipelines are the primary users of AMQP. It is especially common in fintech, logistics, healthcare IT, and any domain where systems need to communicate reliably without tight coupling. When one service publishes a message, other services consume it at their own pace — which prevents bottlenecks and improves fault tolerance. At Osher, we use AMQP Sender within n8n workflows to connect business automation with backend infrastructure. For example, a form submission might trigger an n8n workflow that validates the data, enriches it with an API call, and then publishes a message to a RabbitMQ queue for downstream processing. We built a similar event-driven pipeline for an insurance tech company processing weather data from the Bureau of Meteorology. If your team needs to connect business workflows to message queues without writing custom code, our system integration and n8n consulting teams can design and deploy the right architecture.
    AMQP Sender consultants
  • Microsoft Graph Security

    Microsoft Graph Security

    Microsoft Graph Security is a unified API that aggregates security alerts and threat intelligence from Microsoft’s security products — including Microsoft Defender, Azure Sentinel, and Intune — into a single interface. It gives IT and security teams a centralised way to query, manage, and respond to security events across their Microsoft ecosystem. The problem for most security teams is not a lack of alerts — it is too many of them. Security events pour in from multiple sources, each with different severity levels and formats. Analysts spend their time triaging alerts manually, copying data between tools, and chasing false positives instead of responding to real threats. When response time matters, manual processes are a liability. Osher connects Microsoft Graph Security to your incident response workflow using n8n. We build automations that filter and prioritise alerts based on severity, route critical events to the right team via Slack or PagerDuty, enrich alerts with context from other data sources, and create tickets in your ITSM tool automatically. This means your security team spends less time triaging and more time responding to the threats that actually matter. If your security operations involve too much manual alert handling, our AI agent development services and automated data processing workflows can help you build a faster, smarter response pipeline. We have experience building similar data pipelines for clients like the insurance tech company that needed real-time data processing from BOM.
    Microsoft Graph Security consultants
  • MSG91

    MSG91

    MSG91 is a cloud communication platform that provides SMS, email, voice, and WhatsApp messaging APIs for businesses. It is widely used by companies in Australia, India, and Southeast Asia for transactional messaging — OTP verification, order confirmations, delivery updates, appointment reminders, and marketing campaigns. MSG91 handles the delivery infrastructure so your development team can focus on building the product. The problem most businesses face with messaging is not sending a single SMS — it is orchestrating messages across multiple channels based on real-time events. A customer places an order, and they need an email confirmation, an SMS with tracking, and a WhatsApp update when it ships. When these are triggered manually or through disconnected systems, messages arrive late, get duplicated, or do not go out at all. Osher integrates MSG91 with your order management, CRM, support, and internal systems using n8n workflows. We build automations that trigger the right message on the right channel at the right time — OTP codes sent instantly on sign-up, delivery updates pushed via WhatsApp when shipping status changes, and appointment reminders sent 24 hours before a booking. Multi-channel messaging becomes a single automated workflow instead of a collection of manual triggers. If your customer communication involves multiple channels and manual coordination, our custom development services and business automation solutions can help you build a messaging system that runs itself.
    MSG91 consultants
  • Webex by Cisco Trigger

    Webex by Cisco Trigger

    Webex by Cisco is a video conferencing and team collaboration platform used by enterprises and mid-sized businesses for meetings, messaging, and webinars. The Webex Trigger node in n8n lets you react to events inside Webex — new messages, meeting starts, participant joins, and more — and kick off automated workflows based on those events. The problem most teams face with Webex is that it generates a constant stream of information — meeting recordings, chat messages, action items — but none of it flows automatically into the tools where work actually happens. Meeting notes stay buried in Webex, action items never make it into your project management tool, and follow-ups get missed because nobody manually copied them over. Osher uses the Webex Trigger node in n8n to build event-driven automations that connect Webex to your project management, CRM, and communication tools. When a meeting ends, action items can be extracted and pushed to Asana or Jira. When a message mentions a client name, a note can be logged in your CRM automatically. These workflows turn Webex from a standalone communication tool into a connected part of your operations. If your team spends time manually transferring information out of Webex, our business automation services can help you eliminate that busywork.
    Webex by Cisco Trigger consultants
  • Cockpit

    Cockpit

    Cockpit is a self-hosted, open-source headless CMS that gives developers full control over content structures, APIs, and data storage. Unlike traditional CMS platforms, Cockpit provides a flexible backend for managing structured content that can be delivered to any frontend — websites, mobile apps, digital signage, or internal tools — through a clean API. Developers and agencies use Cockpit when they need a lightweight, customisable content management backend without the overhead of platforms like WordPress or Drupal. It supports custom collections, singletons, forms, and asset management, making it well-suited for projects where content structures do not fit neatly into a blog-and-pages model. At Osher, we integrate Cockpit into automated content workflows using n8n. When content is created or updated in Cockpit, workflows can automatically push that content to your website, sync it with other platforms, generate notifications, or trigger downstream processes. This is especially useful for businesses managing content across multiple channels. Learn more on our system integrations page. If your team manages content in Cockpit and manually copies it to other systems or triggers manual processes when content changes, we can automate those handoffs and keep all your channels in sync without the manual effort.
    Cockpit consultants