Dev Tools & APIs

Apify is a web scraping and browser automation platform that lets you extract data from websites, automate browser-based tasks, and run headless browser scripts in the cloud. It provides a library of pre-built scrapers (called Actors) for popular sites like Google Maps, Instagram, Amazon, and LinkedIn, plus the ability to build custom scrapers in JavaScript or Python. Results are delivered via API, webhooks, or direct download in formats like JSON, CSV, and Excel. The problem Apify solves is manual data collection from the web. If your team spends time copying information from websites into spreadsheets, monitoring competitor pricing, gathering business listings, or collecting public reviews, Apify automates that work. It runs scraping tasks on a schedule, handles pagination and anti-bot measures, and delivers clean, structured data ready for analysis or import into your business systems. At Osher, we use Apify as a data source within larger automated workflows built in n8n. A typical setup runs an Apify Actor on a schedule, collects the scraped data via API, transforms it into the right format, and loads it into your CRM, database, or analytics tool automatically. We have built Apify-powered pipelines for competitor price monitoring, lead generation from Google Maps listings, and market research data collection. If you need to extract data from the web at scale, our automated data processing team can design a scraping pipeline that feeds directly into your existing systems. See our BOM weather data pipeline case study for an example of how we build data extraction workflows.

Outscraper is a data extraction service focused on Google Maps, Google Search, and Google Reviews. It lets you pull structured business data (company names, addresses, phone numbers, websites, ratings, reviews) from Google’s platforms through a web interface or REST API. Unlike general-purpose web scrapers, Outscraper is built specifically for extracting Google data at scale, handling Google’s anti-bot protections and delivering clean, structured results. The problem Outscraper solves is manual lead research and market data collection. Sales teams that spend hours searching Google Maps for potential clients, marketing teams that manually track competitor reviews, and research teams that need business listings for specific locations or industries can all automate that work with Outscraper. You define your search parameters (business type, location, keywords), and Outscraper returns a structured dataset of matching businesses with their details and reviews. At Osher, we integrate Outscraper into automated lead generation and market research pipelines using n8n. A typical workflow runs Outscraper queries on a schedule, deduplicates the results against your existing database, enriches each record with additional data points, and loads qualified leads directly into your CRM. We have also built review monitoring systems that track competitor Google Reviews and alert you to changes in rating or review volume. If you need Google data feeding into your business systems automatically, our automated data processing team can build the pipeline. See our talent marketplace case study for an example of how we build data processing workflows.

Nyckel is a machine learning API that lets developers build and deploy custom classification models without needing data science expertise. You provide labelled examples (images, text, or tabular data), Nyckel trains a model automatically, and you get an API endpoint you can call from your application within minutes rather than months. The problem Nyckel solves is the gap between wanting ML classification and actually building it. Training a custom image classifier or text categorisation model traditionally requires a data scientist, significant compute resources, and weeks of development time. Nyckel compresses that process: you upload training samples through a web interface or API, the platform handles model selection, training, and hosting, and you get a production-ready endpoint immediately. Key features include: Custom image, text, and tabular data classification models No-code web interface plus a full REST API for developer integration Models train in minutes from as few as a handful of labelled examples Automatic model improvement as you add more training data over time Hosted inference API with built-in scaling Semantic search and content moderation functions Pay-per-invocation pricing with no upfront model training costs Nyckel is used by development teams that need to add classification to their products quickly: content moderation, document sorting, product categorisation, image tagging, and similar tasks. At Osher Digital, our custom AI development team integrates Nyckel into client applications and automation pipelines, connecting its classification API to business workflows where sorting or categorising data manually creates bottlenecks. We used similar classification approaches in our medical document classification project.

Faros is an engineering operations platform that pulls data from across your development toolchain (Jira, GitHub, GitLab, PagerDuty, CI/CD pipelines) and unifies it into a single analytics layer. Instead of manually stitching together spreadsheets to understand deployment frequency, cycle time, or incident response, Faros gives engineering leaders a consolidated view of how their teams actually ship software. The core problem Faros solves is visibility. Most engineering organisations run dozens of tools, and each one holds a fragment of the picture. Faros connects to these tools through pre-built connectors, normalises the data, and surfaces metrics like DORA (deployment frequency, lead time, change failure rate, mean time to recovery). This makes it useful for engineering managers tracking team performance, CTOs reporting to the board, and platform teams identifying bottlenecks in the delivery pipeline. At Osher, we work with Faros as part of our system integration projects. When clients have fragmented dev toolchains with data sitting in silos, we connect Faros to their existing stack and build dashboards that give leadership real answers about engineering throughput. For teams already using n8n or similar automation platforms, we can also pipe Faros metrics into automated alerting workflows through our automated data processing services.

Google Cloud is one of the three major cloud platforms, alongside AWS and Azure. It gives businesses access to compute, storage, databases, machine learning APIs, and data analytics tools — all running on the same infrastructure Google uses for Search, Gmail, and YouTube. The problem most Australian businesses face with Google Cloud isn’t getting started — it’s knowing which services to actually use. With hundreds of products across compute (Compute Engine, Cloud Run, GKE), data (BigQuery, Cloud SQL, Firestore), and AI (Vertex AI, Document AI, Vision API), it’s easy to overspend on services you don’t need or miss the ones that would save you the most time. Where Google Cloud really stands out is in data and AI. BigQuery can process terabytes of data in seconds without managing any infrastructure, and Vertex AI lets you deploy machine learning models without building everything from scratch. For businesses already using Google Workspace, the integration is straightforward. We connect Google Cloud services into automated workflows using n8n, pulling data from BigQuery, triggering Cloud Functions, or feeding documents through Document AI — then routing the results into your CRM, accounting system, or reporting dashboards. If you’re looking to get more from Google Cloud without hiring a full platform team, our integration services can help you build something practical.

ScrapeNinja is a web scraping API that handles the hard parts of extracting data from websites: JavaScript rendering, anti-bot detection, proxy rotation, and CAPTCHA challenges. You send it a URL, and it returns the page content as HTML or plain text, ready for parsing. Unlike browser-based scraping tools that you run yourself, ScrapeNinja is a cloud API. You make an HTTP request with the target URL and your configuration options, and it fetches the page using residential proxies and real browser rendering. This means it works on sites that block simple HTTP requests or require JavaScript to load content. In an n8n workflow, ScrapeNinja is useful for monitoring competitor pricing, tracking product availability, pulling data from sites without APIs, or aggregating content from multiple sources. You call the ScrapeNinja API from an HTTP Request node, parse the returned HTML, and route the extracted data wherever it needs to go. If you need to build a data collection pipeline that pulls from websites and feeds into your business systems, our automated data processing team can help you set it up.

Verifalia is an email verification and validation service that checks whether email addresses are real, properly formatted, and able to receive mail. If your business sends transactional emails, marketing campaigns, or automated notifications, invalid addresses cost you money through bounced sends, damage your sender reputation, and skew your reporting data. The Verifalia API accepts single or bulk email lists and returns detailed validation results, including whether each address exists, is a disposable inbox, a role address (like info@ or admin@), or has syntax errors. It connects directly to mail servers to confirm deliverability rather than relying on pattern matching alone. For businesses running automated workflows through n8n, Verifalia fits into lead capture pipelines, CRM data hygiene routines, and email campaign pre-send checks. You can trigger validation when a new form submission arrives, before syncing contacts to your email platform, or on a scheduled basis to clean existing databases. If you need help connecting Verifalia to your CRM, marketing tools, or n8n workflows, our system integration services can get it set up properly.

Big Data Cloud is an Australian-based API platform that provides geolocation, IP intelligence, and reverse geocoding services. If your application needs to know where a user is based on their IP address, convert GPS coordinates to a street address, or look up timezone and network information, Big Data Cloud offers a set of REST APIs that handle these lookups. The API suite includes IP geolocation (mapping IP addresses to countries, cities, and postcodes), reverse geocoding (turning latitude/longitude pairs into human-readable addresses), timezone detection, and ASN (Autonomous System Number) lookups for network analysis. There is a free tier for testing and low-volume use, with paid plans for production workloads. Because Big Data Cloud is API-first, it fits cleanly into n8n workflows using HTTP request nodes. You can enrich incoming leads with location data, personalise content based on visitor geography, flag suspicious login locations for fraud detection, or add address information to records that only have coordinates. For Australian businesses, having an Australian-based provider can simplify data residency conversations. Talk to us about data enrichment workflows using Big Data Cloud.

Kibana is the visualisation and dashboarding layer of the Elastic Stack (Elasticsearch, Logstash, Kibana, Beats). It connects directly to Elasticsearch and lets you build interactive dashboards, run ad-hoc queries, create alerts, and explore log, metric, and event data through a web interface. If your organisation uses Elasticsearch for log management, application monitoring, or security analytics, Kibana is how most teams actually interact with that data. The practical challenge with Kibana is that it sits in its own silo. Your dashboards and alerts live inside Kibana, but the actions you need to take based on those insights, like creating a support ticket, notifying a team, or updating a record in another system, happen elsewhere. That gap between “seeing a problem in Kibana” and “doing something about it” is where automation comes in. By connecting Kibana and Elasticsearch to n8n, you can build workflows that query Elasticsearch directly, process the results, and trigger actions in other systems. For example, pull error log counts from Elasticsearch hourly and send a Slack alert if they spike, or query application performance metrics and create a PagerDuty incident when latency exceeds a threshold. If you want to turn your Elastic Stack data into automated responses rather than just dashboards, our system integrations team can help you build those connections.

The crowd.dev Trigger node in n8n fires your automation workflows whenever new community activity is detected in crowd.dev, an open-source platform that aggregates developer community data from GitHub, Discord, Slack, and other sources into a single view. If you run a developer-focused product, community engagement is a leading indicator for product adoption, churn risk, and expansion opportunities. The problem is that this activity is scattered across platforms. Someone opens an issue on GitHub, another person asks a question in Discord, and a third posts in your Slack community. crowd.dev pulls all of that together, and the Trigger node in n8n lets you act on it automatically. Practical uses include alerting your team when a new high-value community member signs up, creating CRM records when someone from a target account engages in your community, or tracking activity trends to identify potential advocates. The node watches for new members, new activities, and new organisations appearing in your crowd.dev data, then passes the details into whatever n8n workflow you have built. For teams that want to connect community signals to their sales and product workflows, see our system integrations services.

The Default Data Loader is a document-loading node in n8n that takes plain text or structured string data and prepares it for AI processing chains. While the Binary Input Loader handles files like PDFs and Word documents, the Default Data Loader works with data that is already in text form, such as content from API responses, database query results, scraped web pages, or text fields pulled from other nodes in your workflow. This node matters in AI workflows because language models and vector databases expect input in a specific format. You cannot just pipe a raw JSON API response into a vector store and hope for the best. The Default Data Loader standardises the text into a document object with content and metadata fields, which downstream nodes like text splitters and vector stores know how to handle. Common uses include loading knowledge base articles from a CMS API, preparing product descriptions from a database for a shopping assistant, or processing scraped documentation for a support chatbot. At Osher, we use it in AI agent development projects whenever the source data is already text rather than binary files. It is a small node, but it fills a critical gap in the RAG (retrieval-augmented generation) pipeline between your data source and the AI model.

LoneScale is a sales intelligence platform that monitors job changes and hiring signals across your target accounts. It tracks when prospects switch companies, when target accounts start hiring for specific roles, and when buying signals emerge, then pushes those signals into your CRM or outbound tools so your sales team can act on them quickly. The problem LoneScale addresses is straightforward: sales teams waste time reaching out to cold prospects when there are warm signals sitting in public data that nobody is monitoring. A former champion moves to a new company and the sales team does not find out for months. A target account starts hiring data engineers, signalling a new project, and nobody notices until a competitor is already in the door. The LoneScale node in n8n lets you pull these signals into automated workflows. You might route job-change alerts into Slack for your BDRs, enrich new signals with company data from Clearbit, or automatically create tasks in your CRM when a former customer surfaces at a new account. If your sales team needs better signal-to-noise on outbound prospecting, this integration is worth exploring. For help building LoneScale into your sales workflows, see our sales automation services.

The Zep Vector Store: Insert node in n8n writes document embeddings into a Zep vector database, which is a purpose-built memory store for AI applications. Zep handles both the embedding generation and storage in a single service, so you do not need to manage a separate embedding model and a separate vector database — Zep does both. Vector stores are the foundation of retrieval-augmented generation (RAG) systems. When you want an AI chatbot or agent to answer questions about your specific business data — your policies, products, support history, or internal documentation — you first embed that data into vectors and store them. When a user asks a question, the system converts the question into a vector, finds the most similar stored documents, and passes those to the LLM as context. The result is an AI that actually knows your content rather than just generating generic responses. In n8n, the Zep Vector Store: Insert node sits at the end of your document ingestion pipeline. A typical flow pulls data from a source (API, database, file), runs it through a document loader and text splitter to create chunks, and then inserts those chunks into Zep. Zep also maintains long-term memory for conversational AI, tracking user sessions and message history, which makes it particularly useful for building chatbots that remember previous interactions. If you are building an AI system that needs to work with your own business data, our AI agent development services can help you set up the full RAG pipeline — from data ingestion through Zep to a working AI agent that your team can query.