Data & Analytics

  • Cisco Meraki

    Cisco Meraki

    Cisco Meraki delivers cloud-managed networking infrastructure — wireless access points, switches, security appliances and mobile device management — all controlled through a single web-based dashboard. For Australian organisations with distributed sites, branch offices or retail locations, the platform eliminates the need for on-premises network management servers and gives IT teams centralised visibility across every location from one console. The Meraki dashboard provides real-time network analytics, device health monitoring and automated alerting that makes it practical for lean IT teams to manage complex multi-site environments. Built-in security features including content filtering, intrusion detection and automatic firmware updates reduce the operational overhead of maintaining network security across geographically dispersed infrastructure. Where Meraki becomes particularly powerful for business automation is through its comprehensive REST API. Network events, client analytics, location data and device telemetry can all be consumed by external systems — feeding into automated data processing workflows, business intelligence dashboards and operational alerting systems. Our integration specialists regularly connect Meraki infrastructure data to broader business platforms, turning network telemetry into actionable operational insights. For organisations considering smart building deployments, retail analytics or IoT infrastructure, Meraki provides the network foundation with built-in support for high-density wireless, Bluetooth beaconing and environmental sensors — capabilities that extend well beyond basic connectivity into genuine business data collection.
  • Sekoia

    Sekoia

    Sekoia is a European-born cybersecurity platform that combines threat intelligence, SIEM capabilities and automated detection and response (XDR) in a single cloud-native solution. For Australian organisations looking beyond the traditional US-centric security vendor landscape, Sekoia offers a compelling alternative with strong threat intelligence curation and a modern architecture built for API-driven security operations. The platform continuously ingests threat intelligence from its own research team, open-source feeds and industry sharing communities, then correlates that intelligence against your security telemetry in real time. This approach means detection rules are continuously updated based on emerging threat campaigns rather than relying solely on static signatures or historical patterns. For organisations dealing with sophisticated threat actors or operating in targeted industries, this intelligence-led approach provides materially better detection coverage. What makes Sekoia particularly interesting from an automation perspective is its playbook engine and comprehensive API. Security detection, investigation and response workflows can be codified as automated playbooks that execute consistently every time — eliminating the variability that comes with manual incident handling. Our consulting team helps organisations design these automated security playbooks, connecting Sekoia to broader operational workflows including ticketing systems, communication platforms and compliance reporting tools. The platform supports log ingestion from a wide range of sources including cloud infrastructure, endpoint protection, network devices and SaaS applications, making it practical for organisations with heterogeneous technology environments that need unified security visibility without vendor lock-in.
  • QRadar

    QRadar

    QRadar is IBM’s security information and event management (SIEM) platform that collects, correlates, and analyses security data from across your IT infrastructure. The n8n QRadar node connects your SIEM data to automated workflows — letting you pull offences, search events, manage reference sets, and trigger response actions without manually navigating the QRadar console. SIEM platforms generate an overwhelming volume of security events. QRadar does an excellent job correlating those events into actionable offences, but the steps between detecting an offence and responding to it are still largely manual in most organisations. An analyst sees the alert, opens QRadar, investigates the details, copies indicators into other tools, creates a ticket, and notifies the team. Each of those steps takes time that matters during an active incident. The n8n QRadar node automates those manual steps. You can build workflows that pull new offences on a schedule or via webhook, enrich them with external threat intelligence, create investigation tickets automatically, notify the right team members, and even trigger containment actions in other security tools — all within seconds of the offence being created. Osher Digital builds security automation and system integration workflows for Australian businesses. If your SOC team is spending too much time on manual triage and wants to accelerate incident response with n8n and QRadar, our business automation team can design and implement the right workflows for your security operations.
  • ZScaler ZIA

    ZScaler ZIA

    Zscaler ZIA (Zscaler Internet Access) is a cloud-native secure web gateway that inspects all internet traffic to enforce security policies, block threats, and prevent data loss — regardless of where your users are located. The n8n ZScaler ZIA node lets you automate URL categorisation lookups, policy management, and security reporting without manually logging into the ZIA admin portal. Traditional web security relied on on-premises proxy appliances that only worked when users were in the office. ZIA shifts that enforcement to the cloud, inspecting traffic from any location. But managing URL categories, investigating blocked requests, maintaining custom allow and block lists, and generating compliance reports still requires manual admin work — work that scales poorly as your organisation and policy complexity grow. With n8n and the ZIA node, you can build workflows that automatically update URL category overrides based on threat intelligence feeds, generate scheduled reports on blocked traffic and policy violations, investigate user-reported access issues by looking up URL classifications, and sync security policies across ZIA and other tools in your stack. The automation handles the repetitive administration while your security team focuses on policy decisions. Osher Digital helps Australian businesses automate security operations and system integrations using n8n. If your team manages Zscaler ZIA and wants to reduce manual policy administration or improve threat response times, our business automation specialists can build the workflows to make it happen.
  • Cisco Secure Endpoint

    Cisco Secure Endpoint

    Cisco Secure Endpoint (formerly AMP for Endpoints) is a cloud-managed endpoint detection and response (EDR) platform that monitors file activity, process behaviour, and network connections across your organisation’s devices to detect and block threats. The n8n Cisco Secure Endpoint node lets you automate threat response, event retrieval, and endpoint management tasks that would otherwise require manual work in the Secure Endpoint console. Endpoint security is a volume problem. Every device in your organisation generates telemetry — file executions, network connections, process trees, and behavioural signals — all of which needs monitoring. When Cisco Secure Endpoint detects something suspicious, someone on your team has to review the event, assess the threat, investigate related indicators, and take action. For organisations with hundreds or thousands of endpoints, this manual process cannot keep pace with the alert volume. The n8n node automates the response chain. You can build workflows that pull new threat events from Cisco Secure Endpoint, enrich them with context from other security tools, automatically isolate compromised hosts, update internal tracking systems, and notify your response team — all within seconds of detection. The platform handles the heavy lifting while your analysts focus on genuine investigations. Osher Digital builds security automation and system integration workflows for Australian businesses using n8n. If your security team needs faster endpoint threat response or wants to reduce manual alert triage, our business automation team can connect Cisco Secure Endpoint to the rest of your security operations.
  • AlienVault

    AlienVault

    AlienVault, now part of AT&T Cybersecurity, provides unified security management and threat intelligence capabilities that many Australian organisations rely on for their security operations. The platform combines SIEM (Security Information and Event Management), intrusion detection, vulnerability assessment and behavioural monitoring into a single console — reducing the need to manage half a dozen disconnected security tools. For businesses dealing with growing compliance obligations under frameworks like the Australian Privacy Act or Essential Eight, AlienVault offers built-in correlation rules and reporting templates that map directly to regulatory requirements. The open-source OSSIM version gives smaller teams a practical entry point, while USM Anywhere extends those capabilities with cloud-native deployment and managed threat intelligence feeds from the Open Threat Exchange (OTX) community. Where AlienVault becomes particularly valuable is in environments where security events need to flow into broader business workflows. By connecting AlienVault to platforms like n8n or custom middleware, organisations can automate incident triage, escalation and compliance reporting — turning raw security telemetry into actionable responses without manual intervention. Our AI consulting team regularly helps businesses build these automated security pipelines, drawing on real project experience like our insurance tech data pipeline work. The platform supports integration with a wide range of third-party tools through its REST API and plugin architecture, making it a practical foundation for organisations that want centralised visibility without ripping out existing security investments.
  • Aggregate

    Aggregate

    Aggregate is an n8n utility node that collects multiple items from previous steps and combines them into a single output. When your workflow processes data in batches or loops — pulling records from an API, iterating through spreadsheet rows, or handling webhook payloads — Aggregate gathers all those individual items so downstream nodes can work with the complete dataset at once. This matters more than it sounds. Many workflow steps produce one item at a time, but the next step needs all of them together. You might need to compile a summary report from individual API responses, merge rows before writing to a database, or collect results from parallel branches into one payload. Without Aggregate, you end up with fragmented data that is difficult to process as a whole. Developers and data teams use Aggregate as the glue between collection and action. It pairs naturally with Split In Batches, Loop Over Items, and HTTP Request nodes that return paginated results. Once everything is collected, you can sort, filter, or transform the combined dataset before sending it wherever it needs to go. Osher Digital builds automated data processing workflows for Australian businesses using n8n. If you are working with data from multiple sources and need it consolidated reliably, our n8n consulting team can design a workflow that handles the heavy lifting.
  • Embeddings Cohere

    Embeddings Cohere

    Embeddings Cohere is an n8n node that generates vector embeddings from text using Cohere’s language models. Vector embeddings convert words, sentences, or documents into numerical representations that capture semantic meaning — making it possible for machines to understand similarity, relevance, and context in ways that keyword matching simply cannot. This node is essential for building AI-powered search, recommendation systems, and retrieval-augmented generation (RAG) pipelines. Instead of searching for exact keyword matches, embeddings let you find content that is conceptually related. A customer asking about “reducing operational costs” would match documents about “efficiency improvements” and “process optimisation” — even if those exact words never appear in the query. In practice, Embeddings Cohere slots into workflows where you need to index content for semantic search, classify text by topic, or feed context into a large language model. It pairs well with vector databases like Pinecone, Qdrant, or Weaviate, and works alongside n8n’s AI agent nodes for building intelligent retrieval systems. Osher Digital builds AI agent systems and custom AI solutions for Australian businesses using tools like Cohere embeddings. If you need semantic search, document retrieval, or an AI assistant that actually understands your data, get in touch with our AI consulting team.
  • Recorded Future

    Recorded Future

    Recorded Future is a threat intelligence platform that aggregates and analyses data from across the open, deep, and dark web to identify cybersecurity threats before they reach your organisation. The n8n Recorded Future node lets you pull threat intelligence data directly into automated workflows — turning raw threat feeds into actionable alerts, enriched incident reports, and automated response actions. Security teams are drowning in data. Vulnerability disclosures, malware indicators, compromised credentials, and threat actor activity reports pour in from dozens of sources. Without automation, analysts spend most of their time collecting and correlating information rather than actually responding to threats. The Recorded Future node changes that dynamic by feeding curated intelligence straight into your workflow engine. In practice, this node powers workflows that enrich security alerts with threat context, automatically flag high-risk indicators of compromise (IOCs), cross-reference internal logs against known threat actors, and generate prioritised reports for your security operations centre. Combined with other n8n nodes for Slack, email, or ticketing systems, you can build end-to-end threat response pipelines. Osher Digital helps Australian businesses build automated data processing and security intelligence workflows using n8n. If your security team needs faster threat detection and response, our systems integration specialists can connect Recorded Future to your existing security stack.
  • Carbon Black

    Carbon Black

    Carbon Black is an endpoint security platform from Broadcom (formerly VMware) that provides threat detection, investigation, and response capabilities across your organisation’s devices. The n8n Carbon Black node connects this endpoint telemetry to your automated workflows — enabling security teams to respond to threats faster by removing the manual steps between detection and action. Endpoint security generates enormous volumes of data. Every process execution, network connection, file modification, and registry change on every managed device creates telemetry that needs monitoring. Security teams cannot manually review all of it, which is exactly why automated workflows matter. The Carbon Black node lets you pull alerts, query device status, isolate compromised endpoints, and retrieve investigation data without leaving your workflow engine. Practical use cases include automated alert enrichment (pulling Carbon Black alert details and combining them with other threat intelligence), endpoint isolation workflows that quarantine compromised machines the moment a critical alert fires, and compliance reporting that aggregates endpoint health data on a schedule. When seconds matter during an active incident, having these responses automated rather than manual can be the difference between containment and breach. Osher Digital builds security automation workflows for Australian businesses using n8n. If your team needs faster incident response or wants to automate routine endpoint security operations, our systems integration and business automation specialists can connect Carbon Black to your broader security operations.
  • Cisco Umbrella

    Cisco Umbrella

    Cisco Umbrella is a cloud-delivered security platform that provides DNS-layer protection, secure web gateway capabilities, and threat intelligence across your network. The n8n Cisco Umbrella node lets you automate DNS security operations — managing domain block lists, investigating DNS activity, and responding to threats without manual intervention in the Umbrella dashboard. DNS is the first step in almost every internet connection, which makes it an ideal enforcement point for security. Cisco Umbrella inspects DNS requests before they resolve, blocking connections to known malicious domains, phishing sites, and command-and-control servers. But managing policies, investigating blocked requests, and maintaining custom block lists manually takes time that security teams rarely have. With n8n, you can build workflows that automatically add newly discovered malicious domains to your Umbrella block lists, pull DNS activity reports on a schedule, investigate suspicious domains flagged by other security tools, and generate compliance reports showing blocked threat categories. It integrates cleanly with the rest of your security stack through n8n’s node ecosystem. Osher Digital helps Australian businesses automate their security operations with n8n. Whether you need DNS security policy management, automated data processing for security logs, or end-to-end system integrations across your security stack, our team builds workflows that keep your defences current without constant manual effort.
  • Convert to File

    Convert to File

    The Convert to File node in n8n transforms structured data from your workflows into downloadable files. It takes JSON data, spreadsheet rows, text content, or other structured formats and converts them into files like CSV, XLSX, JSON, HTML, or plain text. This is the node you reach for when your workflow needs to produce a report, export data for another system, or generate a file attachment for an email. Reporting and data export are among the most common automation needs across every industry. Instead of manually exporting data from dashboards, copying rows into spreadsheets, and emailing files to stakeholders, the Convert to File node handles the entire output step programmatically. Your workflow collects the data, processes it, and produces a ready-to-use file — all without human intervention. This node is especially valuable in automated data processing pipelines where data needs to move between systems in specific file formats. Whether you are generating weekly sales reports from CRM data, exporting compliance records as CSV files, or creating invoices from order data, the Convert to File node handles the format conversion. We built a similar file generation pipeline for a property inspection company that needed automated report generation from field data. If your team spends time manually exporting and formatting data, our business automation team can build workflows that generate the files you need on schedule or on demand — no manual steps required.
  • Embeddings AWS Bedrock

    Embeddings AWS Bedrock

    The Embeddings AWS Bedrock node in n8n generates vector embeddings using Amazon’s Bedrock service, which provides access to foundation models from providers like Amazon (Titan), Cohere, and others through a unified AWS API. For organisations already running infrastructure on AWS, this node keeps your AI workloads within the same cloud ecosystem — no need to send data to third-party embedding APIs outside your existing security perimeter. Data residency and security are the primary reasons teams choose AWS Bedrock for embeddings over standalone API providers. Bedrock runs within your AWS region, your data stays within your VPC boundaries, and access is controlled through IAM policies. For industries like finance, healthcare, and government where data handling rules are strict, this matters more than marginal differences in embedding quality between providers. From a technical standpoint, Bedrock embeddings work the same way as any other provider in n8n — you feed in text, get back a vector, and store it in your chosen vector database for semantic search or AI agent retrieval. The difference is operational: billing goes through your existing AWS account, access logs feed into CloudTrail, and the infrastructure scales automatically through AWS’s managed service layer. If your organisation is committed to AWS and needs to build custom AI solutions that comply with your security policies, our team can help you architect RAG pipelines and AI workflows that run entirely within your AWS environment — from embedding generation through to vector storage and inference.
  • Extract from File

    Extract from File

    Extract from File is an n8n utility node that pulls structured data out of documents — PDFs, spreadsheets, CSVs, and other file formats that would otherwise need manual handling. Instead of copying and pasting values from invoices, reports, or data exports, this node reads the file and returns clean, usable fields your workflow can act on immediately. Most businesses deal with incoming files daily. Supplier invoices arrive as PDFs, clients send spreadsheets, and internal teams export CSVs from various platforms. Without automation, someone has to open each file, find the relevant data, and re-enter it somewhere else. Extract from File eliminates that manual step entirely, feeding parsed data straight into downstream nodes for processing, storage, or analysis. When combined with other n8n nodes, Extract from File becomes the starting point for document-driven workflows. You might pull line items from purchase orders and push them into your accounting system, or extract contact details from uploaded forms and create CRM records automatically. The node handles the parsing so the rest of your workflow can focus on what to do with the data. If your team spends time manually pulling information from files, this is where that stops. Osher Digital helps Australian businesses build automated data processing pipelines that start with nodes like Extract from File and end with hours saved every week. Talk to us about business automation that actually fits your operations.
  • Zep Vector Store: Load

    Zep Vector Store: Load

    The Zep Vector Store: Load node in n8n retrieves stored vector embeddings from a Zep memory server, making them available for similarity search and retrieval-augmented generation (RAG) workflows. If you have already indexed documents, conversation histories, or knowledge bases into Zep, this node lets you query that data programmatically within your automation pipelines. Businesses building AI assistants or internal knowledge tools often hit the same wall: the language model knows nothing about your company. Zep solves this by storing your documents as vector embeddings that can be searched semantically. The Load node is the retrieval half of that equation — it pulls relevant context from Zep so your AI can answer questions grounded in your actual data rather than generic training knowledge. This node is especially valuable for teams running AI agent workflows that need long-term memory or access to large document collections. Pair it with an embeddings node and a language model to build RAG pipelines that retrieve the most relevant chunks of text before generating a response. The result is an AI that can reference your internal policies, product documentation, or client records accurately. Our custom AI development team has built RAG pipelines for clients across healthcare, insurance, and professional services — including a medical document classification system that needed precise retrieval from thousands of clinical documents.
  • Supabase Vector Store

    Supabase Vector Store

    The Supabase Vector Store node in n8n connects your workflows to Supabase’s pgvector extension, letting you store, retrieve, and search vector embeddings directly within a PostgreSQL database. For teams already using Supabase as their backend, this node removes the need for a separate vector database — your embeddings live alongside your application data in one place. Vector search is the backbone of retrieval-augmented generation (RAG) and semantic search applications. Instead of relying on exact keyword matches, you store text as mathematical representations (embeddings) and search by meaning. The Supabase Vector Store node handles both the indexing and retrieval sides, making it straightforward to build AI workflows that understand context rather than just matching strings. This is particularly useful for organisations building AI agents that need to reference internal knowledge bases, product catalogues, or support documentation. By embedding your content into Supabase and querying it through n8n, you can build assistants that pull the right information before generating a response. Our team used a similar approach when building an AI application processing system for a talent marketplace, where accurate document retrieval was essential. If you are evaluating vector database options and already run Supabase, this node keeps your architecture simple. Need help designing a RAG pipeline? Talk to our AI development team about building a solution that fits your existing stack.
  • Wolfram|Alpha

    Wolfram|Alpha

    The Wolfram|Alpha node in n8n brings computational knowledge into your automation workflows. Wolfram|Alpha is not a search engine — it computes answers from structured data across mathematics, science, geography, finance, and dozens of other domains. This node lets you query that computational engine programmatically, which is useful when your workflows need precise factual answers rather than generated text. Where AI language models sometimes hallucinate numbers or get calculations wrong, Wolfram|Alpha returns verified, computed results. Need to convert currencies at today’s rate? Calculate compound interest? Look up the population of a city or the molecular weight of a chemical compound? This node handles all of that reliably, making it a strong complement to AI-driven workflows where accuracy matters. For businesses building AI agents, Wolfram|Alpha fills a critical gap. Language models are good at reasoning and language, but they struggle with real-time data and precise computation. By adding this node to your agent’s tool chain, you give it the ability to answer quantitative questions accurately — financial calculations, unit conversions, statistical lookups, and more. Teams working in finance, engineering, logistics, and data processing find this node particularly valuable. If you need help integrating computational tools into your automation stack, our AI consulting team can design a workflow that combines the reasoning power of language models with the precision of Wolfram|Alpha.
  • Embeddings Google Gemini

    Embeddings Google Gemini

    The Embeddings Google Gemini node in n8n converts text into vector embeddings using Google’s Gemini embedding models. These embeddings are numerical representations of meaning — they capture what your text is about, not just the words it contains. This is the foundation for semantic search, retrieval-augmented generation (RAG), and any workflow where you need to compare or cluster text by meaning rather than keywords. Google Gemini’s embedding models are fast, cost-effective, and produce high-quality vectors that work well across a range of use cases. Whether you are indexing a knowledge base for an AI assistant, building a document similarity engine, or classifying incoming support tickets by topic, this node handles the text-to-vector conversion step within your n8n pipeline. For teams building AI agents or custom AI solutions, embeddings are a core building block. You generate embeddings for your documents once, store them in a vector database like Supabase, Pinecone, or Zep, and then query them at runtime to give your AI access to relevant context. The Gemini embedding models offer a strong balance of quality and speed, particularly for organisations already using Google Cloud services. If you are building a RAG pipeline or need help choosing the right embedding model for your use case, our AI consulting team can help you design an architecture that balances accuracy, speed, and cost.
  • Embeddings Mistral Cloud

    Embeddings Mistral Cloud

    The Embeddings Mistral Cloud node in n8n generates vector embeddings using Mistral AI’s cloud-hosted embedding models. Mistral has built a reputation for producing efficient, high-quality models, and their embedding offering is no exception — it delivers strong semantic representations at competitive pricing, making it an attractive option for teams that want quality embeddings without the cost overhead of larger providers. Embeddings are the building blocks of semantic search, document classification, and retrieval-augmented generation (RAG). When you convert text into a vector embedding, you capture its meaning in a format that machines can compare mathematically. This lets your workflows find relevant documents, cluster similar content, or match user queries to the right knowledge base articles — all based on meaning rather than exact keyword matches. For organisations building AI agents or internal search tools, Mistral embeddings offer a practical middle ground. They are fast enough for real-time applications, accurate enough for production RAG pipelines, and priced competitively for high-volume use. Teams running automated data processing workflows that need to classify or route documents by content find them particularly effective. Choosing the right embedding model affects the quality of everything downstream — search accuracy, agent reliability, and user experience. If you want guidance on which model fits your use case and budget, our consulting team can benchmark options against your actual data and recommend the best fit.
  • Embeddings Google PaLM

    Embeddings Google PaLM

    The Embeddings Google PaLM node in n8n generates vector embeddings using Google’s PaLM (Pathways Language Model) embedding models. While Google has since released newer Gemini models, PaLM embeddings remain available and are still used in production workflows that were built on the PaLM API. This node converts text into dense vector representations that capture semantic meaning, enabling similarity search, clustering, and retrieval-augmented generation within your n8n pipelines. If your organisation adopted Google’s PaLM API early and has existing vector collections built with PaLM embeddings, this node ensures compatibility. Switching embedding models mid-project means re-indexing your entire document collection, so there is real value in maintaining consistency with the model you originally used for indexing. The PaLM embeddings produce reliable vectors for most common use cases including document search, FAQ matching, and content recommendation. For new projects, you may want to evaluate the newer Gemini embedding models alongside PaLM, as Google continues to improve embedding quality and reduce costs with each generation. However, PaLM remains a solid choice for teams with established pipelines that are working well. Whether you are maintaining an existing PaLM-based system or evaluating your options for a new build, our custom AI development team can help you make the right architectural decisions. We have built RAG pipelines across multiple embedding providers and can advise on trade-offs between migration effort and performance gains.
  • Execution Data

    Execution Data is an n8n node that gives your workflows access to metadata about the current execution — the execution ID, workflow name, mode (manual or production), and whether it was triggered by a webhook, schedule, or another workflow. This might sound like a background utility, but it solves real operational problems: logging which workflow run processed a particular record, building audit trails, creating unique file names, and routing logic based on whether you are testing or running in production. In practice, teams use Execution Data to make their automations self-aware. A document processing workflow can tag each output file with the execution ID so you can trace any result back to the exact run that created it. An error-handling branch can include the workflow name and execution URL in alert notifications, so your team clicks straight through to the failed run instead of hunting through logs. Conditional logic can check the execution mode to behave differently during manual testing versus scheduled production runs. This node is particularly valuable as your n8n environment grows from a handful of workflows to dozens or hundreds. Without execution metadata baked into your logging and error handling, debugging becomes a guessing game. Our n8n consultants include Execution Data in every production workflow we build — it is a small addition that pays off enormously when something goes wrong at 2am and you need to trace exactly what happened. If you are scaling your business automation and want proper observability, this node is non-negotiable.
  • In-Memory Vector Store

    In-Memory Vector Store

    In-Memory Vector Store is an n8n node that creates a temporary vector database directly in your workflow process memory. You feed it text, it generates embeddings, and you can immediately run semantic similarity searches against it — all without setting up Pinecone, Supabase, or any external database. The data lives only for the duration of the workflow execution and disappears when the run completes. This makes it perfect for prototyping RAG (retrieval-augmented generation) workflows, processing document batches where you need to cross-reference content within a single run, and testing embedding strategies before committing to a production vector database. A common pattern is loading a set of documents at the start of a workflow, searching them based on user queries or extracted criteria, and then discarding the vectors when the job is done. No infrastructure costs, no database management, no credentials to configure. The trade-off is clear: no persistence. When the workflow finishes, the vectors are gone. For production systems that need to retain and query data across executions, you would move to Pinecone or Supabase. But for rapid iteration, batch processing, and proof-of-concept builds, In-Memory Vector Store removes every barrier to getting started. Our AI consulting team regularly uses it during discovery workshops to demonstrate RAG capabilities to clients before designing the production architecture.
  • In Memory Vector Store Insert

    In Memory Vector Store Insert

    In Memory Vector Store Insert is the write-side companion to the In-Memory Vector Store node in n8n. While the vector store itself provides the search capability, this insert node handles the loading — taking your text data, passing it through an embedding model, and adding the resulting vectors to the in-memory store so they can be queried later in the same workflow execution. The typical workflow pattern is: trigger fires, documents are fetched from a source (files, APIs, database), the insert node embeds and stores them, and then a retriever node searches them based on a query. All of this happens in a single execution cycle with no external database involved. This makes it the fastest way to build a working RAG prototype or run a batch analysis job where you need to cross-reference a set of documents against each other or against specific criteria. Because the store is ephemeral — data disappears after the execution — this node is best suited for development, testing, and single-run batch processing. For production systems that need to retain vectors across runs, you would swap in Supabase: Load or a Pinecone insert node with minimal workflow changes. Our n8n consultants typically start client projects with in-memory inserts for fast iteration, then migrate to persistent storage once the retrieval logic is validated and the workflow is ready for production deployment.
  • Pinecone Vector Store

    Pinecone Vector Store

    Pinecone Vector Store is an n8n node that connects your workflows to Pinecone — a purpose-built, fully managed vector database designed for production-scale semantic search. Unlike in-memory stores that vanish after each run, Pinecone persists your vectors indefinitely, handles billions of embeddings, and delivers sub-second query responses. If you are building AI applications that need to search large, growing datasets reliably, Pinecone is the infrastructure-grade option. In n8n, this node handles both writing and reading. You can insert new vectors (documents, product listings, support articles), update existing ones, and run similarity searches — all from within your workflow. Pinecone manages the underlying infrastructure: indexing, replication, scaling, and backups. Your team focuses on what goes into the database and what to do with the results, not on managing servers or tuning database performance. Businesses with production RAG systems, large-scale recommendation engines, or customer-facing AI search typically land on Pinecone after outgrowing lighter options. Our AI agent development team has deployed Pinecone-backed systems for clients needing reliable retrieval across large knowledge bases — from insurance data pipelines to enterprise document search. If uptime, scale, and retrieval speed matter to your business, Pinecone is built for exactly that.
  • Vector Store Retriever

    Vector Store Retriever

    Vector Store Retriever is an n8n node that pulls relevant documents from a vector database based on semantic similarity. Rather than relying on exact keyword matches, it converts your query into a numerical embedding and finds the closest stored vectors — returning the most contextually relevant results. This matters for any business sitting on large volumes of unstructured data: internal knowledge bases, support ticket archives, product catalogues, or compliance documentation. In practice, teams use Vector Store Retriever as the backbone of retrieval-augmented generation (RAG) workflows. A customer support chatbot, for instance, can query your vector store to pull the three most relevant help articles before generating a response. The result is grounded, accurate answers instead of hallucinated guesswork. It pairs with vector databases like Pinecone, Supabase, and in-memory stores within n8n. If you are building AI agents or conversational interfaces that need to reference your own data, Vector Store Retriever is essential plumbing. Our AI agent development team has deployed RAG pipelines across industries — from medical document classification to insurance data retrieval. Whether you need a simple lookup or a multi-step reasoning chain, this node handles the retrieval layer so your language model can focus on generating useful output.
  • Supabase: Load

    Supabase: Load

    Supabase: Load is an n8n node that writes data into a Supabase vector store for later semantic retrieval. It takes text content, converts it into vector embeddings using a connected embedding model, and inserts those vectors into your Supabase database. This is the ingestion side of any retrieval-augmented generation (RAG) pipeline — without properly loaded and indexed data, your AI has nothing meaningful to search through. Businesses use this node to keep their vector stores current. When new support articles are published, product specs change, or internal policies get updated, Supabase: Load pushes those changes into the vector database automatically. Combined with an n8n trigger, you get a self-maintaining knowledge base that your AI agents can query in real time. Supabase is particularly appealing because it bundles vector storage with a full PostgreSQL database, so you can handle structured and unstructured data in one platform. If your team is exploring AI agent development or building internal search tools, this node is a practical starting point. Our n8n consultants have built vector ingestion pipelines for clients across healthcare, insurance, and professional services — including a medical document classification system that needed reliable, real-time data loading. Supabase: Load handles the heavy lifting so your AI always works with fresh, accurate information.
  • Zep

    Zep

    Zep is a long-term memory store for AI agents and chatbots, available as an n8n node. Unlike simple buffer memory that forgets everything when a session ends, Zep persists conversation history, extracts key facts, and lets your AI recall relevant context from days, weeks, or months ago. This transforms a basic chatbot into an assistant that genuinely remembers your users — their preferences, past issues, and ongoing projects. Under the hood, Zep stores messages and automatically generates summaries of older conversations. When your AI agent receives a new message, Zep retrieves the most relevant historical context using semantic search rather than dumping the entire chat log into the prompt. This keeps token usage manageable while giving the model access to important details from past interactions. It also supports user-level memory, so each customer or team member gets their own persistent context. For businesses building customer-facing AI or internal assistants that handle repeat interactions, Zep solves the biggest complaint users have with chatbots: “I already told you this.” Our AI agent development team has integrated Zep into support workflows, onboarding assistants, and account management bots. Combined with vector retrieval for knowledge base lookups, Zep-backed agents deliver the kind of personalised, context-aware experience that builds trust and reduces escalations to human staff.
  • SerpApi (Google Search)

    SerpApi (Google Search)

    SerpApi (Google Search) is an n8n node that lets your workflows query Google Search programmatically and receive structured results. Instead of scraping search pages — which is fragile, slow, and violates terms of service — SerpApi provides a clean API that returns organic results, featured snippets, knowledge panels, and related questions in a structured format your automation can immediately process. Businesses use this node to power a range of workflows: competitive monitoring that tracks where rivals rank for target keywords, lead enrichment that researches companies before outreach, content research that identifies trending topics and gaps, and AI agents that can search the web to answer questions with current information. The node slots into larger n8n workflows alongside data processing, AI, and notification nodes — so you can build end-to-end pipelines that search, analyse, and act without manual intervention. If you are building AI agents that need access to live web data, SerpApi is one of the most reliable ways to give them that capability. Our AI agent development team has used it in research assistants, market intelligence tools, and sales automation workflows that need real-time competitive data. Combined with automated data processing, search results can be filtered, enriched, and routed to the right people or systems without anyone manually Googling anything.
  • Postgres Trigger

    Postgres Trigger

    Postgres Trigger is an n8n node that starts workflows automatically when data changes in a PostgreSQL database. It detects inserts, updates, and deletes on specified tables, giving you real-time automation that responds to database events without polling or manual intervention. For businesses that run on PostgreSQL — and many do, from SaaS platforms to e-commerce systems — this trigger turns your database into an event source. When a new order is inserted, a customer record is updated, or a row is deleted, the trigger fires and your workflow takes over. No more scheduled jobs that check for changes every few minutes and miss the context of what actually happened. Our system integration team at Osher Digital uses the Postgres Trigger extensively for clients who need their business logic to react to data changes in real time. We built a workflow for an insurance technology client where database updates from their claims processing system triggered automated notifications and data pipeline refreshes — part of the work described in our BOM weather data pipeline case study. It’s a pattern we apply across industries wherever PostgreSQL sits at the centre of operations.
  • Embeddings Hugging Face Inference

    Embeddings Hugging Face Inference

    Embeddings Hugging Face Inference is an n8n AI node that converts text into numerical vector representations using models hosted on Hugging Face’s inference API. These embeddings capture the semantic meaning of your text, enabling similarity search, document clustering, and the retrieval component of RAG (Retrieval-Augmented Generation) systems. If you’re building any kind of AI-powered search or knowledge system in n8n, you need an embeddings node to turn your documents into vectors that a vector store can index. Hugging Face offers a wide range of embedding models — from lightweight options for simple use cases to specialised multilingual models — and this node gives you access to all of them without hosting your own infrastructure. Our AI agent development team at Osher Digital uses this node when clients want Hugging Face models for their embedding pipeline, often for cost or data sovereignty reasons. For projects where we need full control over the embedding model — like our medical document classification work — Hugging Face provides the flexibility to choose domain-specific models that outperform general-purpose alternatives. Our custom AI development team handles model selection and pipeline configuration.
  • Embeddings Ollama

    Embeddings Ollama

    Embeddings Ollama is an n8n AI node that generates text embeddings using models running locally through Ollama — an open-source tool for running large language models on your own hardware. This means your text data never leaves your infrastructure, making it the go-to choice for organisations with strict data privacy requirements or those who want to eliminate per-request API costs. The node works the same way as cloud-based embedding options: it converts text into numerical vectors for similarity search, document retrieval, and RAG systems. The difference is that everything runs on your own servers. For businesses processing sensitive data — healthcare records, legal documents, financial information — this local-first approach removes the compliance headache of sending data to third-party APIs. At Osher Digital, we recommend the Ollama embedding node for clients who need to keep data on-premises or who process enough volume that API costs become significant. We’ve deployed self-hosted embedding pipelines for healthcare clients where patient data privacy is non-negotiable, including work similar to our patient data entry automation project. Our AI agent development team handles the full setup from hardware sizing to model selection.
  • Limit

    Limit

    Limit is an n8n utility node that controls data flow by restricting how many items pass through a workflow at any given point. When you’re processing large datasets or pulling records from APIs, Limit lets you cap the output to a specific number of items — useful for testing, pagination, or preventing downstream systems from being overwhelmed. For businesses dealing with high-volume data pipelines, the Limit node is a small but important piece of the puzzle. It stops runaway processes, keeps API calls within rate thresholds, and gives you precise control over batch sizes. Whether you’re feeding data into a CRM, syncing records between platforms, or running nightly ETL jobs, Limit helps you manage throughput without writing custom logic. At Osher Digital, we use the Limit node regularly when building automated data processing workflows for clients. It’s particularly handy during initial testing — you can process just 10 records instead of 10,000 while you iron out the kinks. We’ve also used it in production workflows where APIs enforce strict rate limits, such as our BOM weather data pipeline project.
  • Token Splitter

    Token Splitter

    The Token Splitter node in n8n divides text into chunks based on token count rather than character count. This distinction matters because large language models process and bill by tokens, not characters. By splitting on token boundaries, you get precise control over how much content you send to an AI model in each request, which directly affects both cost and output quality. Token-based splitting is essential when building retrieval-augmented generation (RAG) pipelines, processing long documents through AI models, or preparing text for embedding generation. If you split by characters, you might accidentally cut through the middle of a token, which can produce garbled embeddings or incomplete context. The Token Splitter avoids this by respecting the tokenisation rules of the model you are targeting. This node works hand-in-hand with vector store nodes and summarisation chains. You feed it a long document, it breaks it into token-counted chunks with configurable overlap, and each chunk flows downstream for embedding, summarisation, or classification. The overlap setting ensures important context at chunk boundaries is not lost, which improves retrieval accuracy in search-based workflows. If your team is building AI workflows that process documents and you need help getting the chunking strategy right, our AI consultants can advise on the best approach for your specific data types and use cases. Chunking strategy has a measurable impact on the quality of custom AI systems.
  • Pinecone: Insert

    Pinecone: Insert

    The Pinecone Insert node in n8n writes vector embeddings into a Pinecone index, which is one of the most widely used managed vector databases for AI applications. Once your text has been chunked, embedded, and inserted into Pinecone, you can perform fast semantic searches across millions of vectors. This node handles the insertion step, making it straightforward to keep your vector index up to date as part of an automated pipeline. Pinecone is purpose-built for similarity search at scale. Unlike the in-memory vector store, Pinecone persists your data across workflow runs, supports concurrent access from multiple applications, and can handle datasets that would not fit in memory. The Insert node lets you push new embeddings into your index whenever new data arrives — whether that is new documents, updated product descriptions, or fresh support articles. Teams building production retrieval-augmented generation (RAG) systems typically use Pinecone as their vector store because it handles the infrastructure complexity. You do not need to manage servers, tune indexes, or worry about scaling. The n8n integration means you can automate the entire pipeline: ingest data, chunk it, embed it, and insert it into Pinecone without writing custom code. Our medical document classification project used a similar approach to index and retrieve clinical documents at scale. If you are building a vector search system and need help with architecture decisions, our AI agent development and system integration teams can design a pipeline that scales with your data.
  • Recursive Character Text Splitter

    Recursive Character Text Splitter

    The Recursive Character Text Splitter node in n8n breaks long text documents into smaller chunks by recursively splitting on natural text boundaries — paragraphs first, then sentences, then words. This hierarchical approach produces cleaner chunks than fixed-length splitting because it respects the structure of the original text. The result is chunks that are more coherent and more useful for downstream AI processing. When preparing documents for embedding generation, summarisation, or classification, chunk quality directly impacts the quality of your results. Splitting in the middle of a sentence produces fragments that lose meaning in isolation. The recursive approach avoids this by trying the largest separators first (double newlines for paragraphs) and only falling back to smaller separators (single newlines, spaces) when a chunk still exceeds the target size. This gives you the best balance between chunk size consistency and content coherence. This node is a fundamental building block in retrieval-augmented generation (RAG) pipelines. After splitting, each chunk is typically passed through an embedding model and stored in a vector database for semantic search. The quality of your splits directly affects retrieval precision — well-formed chunks that represent complete thoughts or sections produce more relevant search results. Our patient data entry automation project used careful text processing to extract and classify medical information accurately. If you are building document processing workflows and want guidance on chunking strategies for your specific data, our AI consulting team and data processing specialists can help you design an approach that maximises the quality of your AI outputs.
  • Custom Code Tool

    Custom Code Tool

    Custom Code Tool is an n8n node that lets you write JavaScript or Python code and expose it as a tool your AI agent can call. While n8n provides built-in tools for common tasks like web search and calculations, many business processes require custom logic that no pre-built node covers. This node bridges that gap — you write the specific function your agent needs, and it becomes a callable tool within the agent workflow. The use cases are broad. You might write a custom tool that validates Australian Business Numbers (ABNs), formats data according to your company standards, queries a proprietary internal API, or applies business rules that are too specific for generic nodes. The agent receives a description of what your custom tool does, decides when to call it based on the task at hand, and uses the returned result in its reasoning and output. At Osher Digital, Custom Code Tool is where we implement the business-specific logic that makes each AI agent project unique to the client. In our BOM weather data pipeline, custom code handled the specific data transformation logic that no standard node could. For custom AI development and system integrations, this node is often the key to connecting AI agents with legacy systems or proprietary data formats. If you have unique business logic that needs to be accessible to an AI agent, our n8n consulting team can build and test custom tools that fit your exact requirements.