Dev Tools & APIs

Alchemy is a blockchain development platform that gives teams the APIs, SDKs, and infrastructure they need to build and scale decentralised applications without managing their own nodes. If your organisation is working with Web3 technology — whether that is an NFT marketplace, DeFi protocol, or on-chain data pipeline — Alchemy handles the heavy lifting so your developers can focus on product rather than plumbing. Where Alchemy fits into a broader automation strategy is in its event-driven architecture. Webhooks fire when on-chain conditions are met, which means you can trigger downstream workflows automatically — updating databases, notifying teams, or kicking off reconciliation processes. For businesses running automated data processing pipelines that pull from blockchain sources, Alchemy provides a reliable and fast data layer that replaces brittle public node connections. The platform supports Ethereum, Polygon, Arbitrum, Optimism, and several other networks, with enhanced APIs that outperform standard RPC endpoints on speed and reliability. For development teams already building on-chain, the debugging and monitoring tools cut troubleshooting time significantly. For organisations exploring blockchain integration, Alchemy removes the biggest barrier — infrastructure complexity — so you can validate use cases faster. At Osher, we have worked with clients who need to pipe blockchain data into existing business systems. Our system integration work often involves connecting APIs like Alchemy to internal tools, databases, and notification systems, turning raw on-chain events into actionable business data.

ApiFlash is a screenshot API that lets you capture full-page or viewport screenshots of any website programmatically. It runs on cloud-based Chrome instances, which means your screenshots render exactly as a real browser would display them — including JavaScript-heavy pages, dynamic content, and modern CSS layouts. For businesses, ApiFlash is useful for monitoring website changes, generating visual reports, creating automated previews of web content, and archiving webpage snapshots. Rather than manually taking screenshots, you can build workflows that capture them on a schedule or in response to specific events, then store or distribute them automatically. At Osher, we integrate ApiFlash into automated monitoring and reporting pipelines using n8n. This might mean capturing daily screenshots of competitor websites, generating visual previews for client reporting dashboards, or monitoring your own web properties for unexpected layout changes. Our automated data processing services handle these kinds of visual data workflows alongside traditional document processing. ApiFlash supports custom viewport sizes, full-page capture, CSS injection for styling overrides, geolocation targeting, and response format options including PNG, JPEG, and WebP. For Australian businesses that need reliable, automated website screenshots as part of their operations or reporting, it is a straightforward API that does one job well.

Spondyr is a real-time content personalisation platform that allows businesses to dynamically update website content, emails, and digital assets based on external data sources — without requiring developer intervention for every change. It acts as a bridge between your data and your customer-facing content, enabling live updates triggered by events, schedules, or API calls. For Australian businesses that need content to reflect real-time conditions — pricing changes, inventory levels, event schedules, or location-specific offers — Spondyr removes the bottleneck of waiting for a developer to make manual updates. Marketing and operations teams can set rules that automatically adjust what customers see based on live data. The platform supports integration with APIs, webhooks, and data feeds, making it possible to pull information from your CRM, inventory system, or external data sources and render it directly in your content. This is particularly useful for businesses with high-frequency content changes or multiple product variants. Our custom AI development and data processing teams can help you connect Spondyr to your business systems, setting up dynamic content pipelines that keep your customer-facing materials accurate and up to date without manual intervention.

DarkSky API was one of the most respected weather data APIs available, known for its hyperlocal, minute-by-minute precipitation forecasting and clean developer experience. Apple acquired Dark Sky in 2020 and has since transitioned the API to Apple WeatherKit, which offers similar data through a new interface under Apple’s developer programme. For businesses that built applications, dashboards, or automation workflows on the DarkSky API, the transition to Apple WeatherKit means updating integrations, changing authentication methods, and adapting to a different data format. Some businesses have migrated to alternative weather APIs like OpenWeatherMap, Tomorrow.io, or Visual Crossing instead. Weather data is critical for more industries than people realise. Logistics companies route deliveries around storms. Agricultural businesses schedule irrigation based on precipitation forecasts. Event companies plan outdoor activities around weather windows. Insurance firms assess weather-related risk in real time. The accuracy and reliability of your weather data source directly impacts operational decisions. Osher Digital builds weather data integrations for Australian businesses — whether you are migrating from DarkSky to WeatherKit, switching to an alternative provider, or building new weather-driven data processing and AI agent workflows. We have built weather pipeline integrations for insurance technology companies and other industries that depend on reliable weather data.

Ably is a realtime infrastructure platform that handles the hard parts of building live, event-driven applications — pub/sub messaging, presence detection, push notifications, and data streaming at scale. When your application needs to push updates to thousands of connected users simultaneously, Ably provides the backbone that makes it work reliably. The use cases range from live chat and collaborative editing to IoT data streams, live sports scores, financial market data, and real-time dashboards. Any application where users need to see changes the instant they happen — without refreshing the page — is a candidate for Ably’s infrastructure. Building realtime features is deceptively complex. Connection management, message ordering, delivery guarantees, reconnection handling, and scaling across geographic regions all need to work flawlessly. Ably abstracts these challenges into a developer-friendly API, but integrating it properly with your application architecture, data sources, and backend systems still requires careful planning. Osher Digital integrates Ably into applications and AI agent systems for Australian businesses. Whether you are adding live features to an existing application, building a real-time dashboard, or connecting IoT devices to automated data processing pipelines, we handle the architecture and integration work.

Hybrid Analysis is an advanced malware analysis platform that combines static analysis, dynamic sandboxing and machine learning classification to determine whether files and URLs are malicious. Operated by CrowdStrike, the platform processes suspicious samples in isolated environments that simulate real operating systems, observing the actual behaviour of files — network connections, file system changes, registry modifications, process creation — to provide definitive verdicts that signature-based scanning alone cannot deliver. For Australian organisations dealing with targeted attacks, suspicious email attachments or files from untrusted sources, Hybrid Analysis provides the forensic depth needed to make confident security decisions. The platform generates detailed analysis reports including behavioural indicators, MITRE ATT&CK technique mapping, network indicators of compromise and risk scores that help security teams understand not just whether something is malicious, but what it does and how it operates. The Hybrid Analysis API enables programmatic submission and retrieval of analysis results, making it practical to integrate malware analysis into automated security workflows. Email security gateways can submit attachments for detonation before delivery, SOC playbooks can automatically analyse suspicious files extracted during incident response and threat intelligence teams can enrich indicators with behavioural analysis data. Our consulting team builds these automated analysis pipelines to ensure suspicious content gets evaluated systematically rather than relying on analyst availability. The platform supports analysis of executables, documents, scripts, archives and URLs across Windows, Linux and Android environments, providing broad coverage of the file types and platforms that Australian businesses encounter in their daily operations.

OpenCTI is an open-source threat intelligence platform designed to help organisations collect, store, analyse and share cyber threat intelligence in a structured, actionable format. Built on the STIX 2.1 standard and maintained by Filigran, the platform provides a knowledge graph approach to threat intelligence that maps relationships between threat actors, malware families, attack techniques, indicators of compromise and targeted sectors — giving security teams contextual understanding rather than disconnected indicator lists. For Australian organisations building threat intelligence capabilities, OpenCTI provides a practical foundation without the licensing costs of commercial platforms. The platform ingests intelligence from multiple sources including MISP feeds, TAXII servers, RSS feeds, CSV imports and direct API submissions, normalising everything into a consistent STIX format that enables meaningful correlation and analysis. This multi-source approach lets security teams combine commercial threat feeds, industry sharing groups and internal incident data into a unified intelligence picture. Where OpenCTI becomes particularly valuable is in operationalising threat intelligence — turning curated intelligence into defensive actions. Through its connectors and API, OpenCTI can push indicators to firewalls, SIEM correlation rules, endpoint detection platforms and automated blocking systems. Our consulting team helps organisations build these automated intelligence pipelines so threat data moves from analysis to protection without manual copy-paste operations that introduce delays and errors. The platform includes role-based access control, marking definitions for intelligence classification, and workflow capabilities for managing the intelligence lifecycle from ingestion through analysis to dissemination — essential features for organisations that share intelligence with partners, industry groups or government agencies like the Australian Cyber Security Centre (ACSC).

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 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.

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.

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.

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.

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.

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.

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.

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.

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 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 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.