Dev Tools & APIs

Google Vertex AI is Google Cloud’s unified machine learning platform, bringing together Google’s AI capabilities — from pre-trained models and AutoML to custom model training — under a single managed environment. It’s built for organisations that need production-grade AI and ML capabilities without managing the underlying infrastructure from scratch. The platform covers the full ML lifecycle: data preparation, model training, evaluation, deployment, and monitoring. You can use pre-trained models for common tasks like natural language processing, image recognition, and speech-to-text, or train custom models on your own data using AutoML (which automates much of the model selection and tuning process). For teams with ML expertise, Vertex AI also supports custom training with frameworks like TensorFlow, PyTorch, and scikit-learn. Where Vertex AI differentiates itself is in the enterprise infrastructure layer. It handles model versioning, A/B testing, prediction serving at scale, model monitoring for drift, and integration with the broader Google Cloud ecosystem — BigQuery, Cloud Storage, Dataflow, and more. For organisations already running workloads on Google Cloud, Vertex AI fits naturally into existing data pipelines. Businesses working on automated data processing at scale often find that Vertex AI provides the ML backbone needed to extract intelligence from their data. Vertex AI is a powerful platform, but it’s also complex. Getting real value from it requires clear problem definition, good data, and ML expertise — whether in-house or through partners. Working with experienced AI consultants helps organisations avoid common pitfalls like training models on poor data or deploying solutions that don’t align with business objectives. For companies building sophisticated AI capabilities, Vertex AI paired with custom AI development services provides an enterprise-grade foundation.

AssemblyAI provides a suite of speech-to-text and audio intelligence APIs built for developers who need accurate transcription at scale. If your business processes recorded calls, meetings, podcasts, or any form of spoken content, AssemblyAI converts that audio into structured, searchable text — with additional layers like sentiment analysis, topic detection, and speaker identification. The real value shows up when you move beyond basic transcription. AssemblyAI’s audio intelligence features can automatically summarise conversations, detect key topics, identify action items, and flag sensitive content. For organisations dealing with hundreds or thousands of audio files, manually reviewing recordings simply doesn’t scale. This is where automated data processing becomes essential. Accuracy matters more than speed in most business contexts. A transcript riddled with errors creates more work than it saves. AssemblyAI consistently ranks well on accuracy benchmarks, particularly for English-language audio, and supports custom vocabulary to handle industry-specific terminology — useful in fields like healthcare, legal, or finance where jargon is unavoidable. Integrating AssemblyAI into existing workflows typically requires some development work. The API is well-documented and straightforward, but connecting it to your CRM, project management tools, or data warehouse benefits from working with experienced AI consultants who understand both the technical integration and the business process around it. For teams building broader voice-enabled applications, pairing this with AI agent development can unlock powerful automation opportunities.

Airparser is an AI-powered document parsing tool that extracts structured data from emails, PDFs, invoices, forms, and other unstructured documents. Instead of manually copying data from incoming documents into spreadsheets or databases, Airparser reads the content, identifies the relevant fields, and outputs clean, structured data ready for your downstream systems. The tool works by letting you define extraction templates — you tell Airparser what fields to look for (invoice number, date, line items, total, sender details) and it applies AI to extract those fields from each incoming document. It handles variation in document formats, so you do not need a separate template for every supplier or sender. This flexibility sets it apart from rigid OCR solutions that break when layouts change. For Australian businesses processing high volumes of documents — invoices, purchase orders, receipts, application forms, or data submissions — Airparser removes a repetitive manual step. When connected to automation platforms like n8n or Zapier, extracted data can flow directly into accounting software, CRMs, or databases without human intervention. Osher Digital’s automated data processing services build exactly these kinds of document-to-database pipelines, as demonstrated in our patient data entry automation case study. If your team spends hours each week manually entering data from documents into systems, Airparser is worth evaluating. Our AI consultants can assess your document processing volumes and recommend whether Airparser, a custom-built extraction pipeline, or our RPA services would deliver the best return for your specific situation.

Kraftful is an AI-powered product research tool built for product managers and UX teams. It analyses user feedback from multiple sources — app reviews, support tickets, surveys, and user interviews — and distils it into actionable product insights. Instead of manually reading through thousands of reviews or tagging feedback in spreadsheets, Kraftful does the heavy lifting of categorisation and sentiment analysis. The platform excels at surfacing patterns that humans miss when dealing with high volumes of qualitative data. It identifies recurring feature requests, common pain points, and sentiment shifts over time. Product teams can then prioritise their roadmap based on what users are actually saying rather than relying on gut instinct or the loudest voices in the room. For software companies and digital product teams in Australia, Kraftful fits into the broader challenge of making data-driven product decisions without drowning in manual analysis. When combined with other tools in your product stack, insights from Kraftful can feed into sprint planning, stakeholder reports, and customer success workflows. Osher Digital’s AI consulting team helps product organisations integrate tools like Kraftful into their decision-making processes, ensuring insights translate into action. If your product team is spending days each month manually analysing user feedback, Kraftful can compress that work significantly. Our custom AI development team can also build bespoke feedback analysis pipelines for organisations with unique data sources or requirements that go beyond what Kraftful offers out of the box. See how we approach similar challenges in our automated data processing work.

Browserless is a headless browser service that runs Chrome instances in the cloud for web scraping, automated testing, PDF generation, and screenshot capture. Instead of managing your own Chromium infrastructure — dealing with memory leaks, version updates, and scaling headaches — Browserless handles the browser runtime so your code just sends requests and gets results back. Developers and automation teams use Browserless for tasks that require a full browser environment: scraping JavaScript-rendered pages, running Puppeteer or Playwright scripts at scale, generating PDFs from web content, and capturing page screenshots for monitoring or reporting. It exposes a simple API, so you can swap out a local browser instance for a cloud-hosted one with minimal code changes. For organisations running data collection, price monitoring, or content extraction workflows, Browserless removes a significant infrastructure burden. When connected to automation platforms like n8n, it becomes part of a larger pipeline — scrape a page, extract data, transform it, and push it into a database or dashboard. Osher Digital’s automated data processing services frequently involve browser-based extraction as one step in a multi-stage data pipeline, similar to the work we did in our BOM weather data pipeline project. If your team is wrestling with unreliable scraping infrastructure or needs browser automation at scale, Browserless is a solid managed option. Our system integration specialists can wire it into your existing workflows and ensure extracted data flows cleanly into your downstream systems.

Airbrake is an error monitoring and performance tracking platform used by development teams to catch, diagnose, and resolve application errors before they affect end users. Rather than sifting through log files after a customer complaint, Airbrake surfaces errors in real time — grouping duplicates, tracking frequency, and showing the exact code path that triggered each issue. For businesses running production software, undetected errors cost money. A broken checkout flow, a failed API call, or a crashing mobile screen can go unnoticed for hours without proper monitoring. Airbrake acts as an early warning system, sending alerts the moment something breaks and giving developers the stack trace, environment data, and deployment context they need to fix it fast. Our custom development team builds monitoring into every application we deliver. Beyond error tracking, Airbrake includes application performance monitoring (APM) that highlights slow queries, memory leaks, and throughput bottlenecks. This dual focus — catching what is broken and what is slow — gives engineering teams a more complete picture of application health. When paired with system integrations, Airbrake alerts can automatically create tickets in Jira, post to Slack channels, or trigger incident response workflows. If your development team is reactive rather than proactive about application issues, Airbrake provides the visibility layer that changes that dynamic. Combined with a solid AI agent development approach, you can even build automated triage systems that classify and route errors based on severity and component.

Confluent is a data streaming platform built on Apache Kafka that enables real-time data flow between systems, applications, and databases. For businesses dealing with high data volumes that need to move between systems instantly — transactions, sensor readings, logs, customer interactions — Confluent makes data available the moment it is created rather than hours later in batch processes. The core problem Confluent solves is data latency. Traditional batch ETL waits then moves data in bulk, meaning downstream systems work with stale information. With Confluent, data streams continuously. Inventory updates across warehouses in real time, customer events trigger instant responses, and dashboards reflect what is happening now. Our automated data processing team works with real-time architectures for clients who cannot afford batch delays. Confluent adds enterprise features on top of open-source Kafka — managed infrastructure, schema registry, connectors for popular databases, and governance tools. Paired with n8n, it enables event-driven architectures where business processes react to data changes instantly. If your organisation is ready to move from batch processing to real-time streaming, our system integrations specialists can design the architecture. Talk to our AI consultants about building real-time data infrastructure that keeps your systems synchronised.

Datumbox is a machine learning API platform offering pre-built natural language processing capabilities. It provides sentiment analysis, topic classification, spam detection, language detection, keyword extraction, and readability scoring — all through a REST API. For businesses with large volumes of unstructured text, Datumbox extracts structured insights without building custom ML models. The applications are wide-ranging. Support teams can classify tickets by sentiment and topic, routing negative feedback to senior staff. Marketing teams can analyse social mentions to gauge brand sentiment. Content teams can score readability and extract keywords. Connected to n8n, these analyses run continuously. Our automated data processing team builds these text analysis pipelines for clients across industries. What makes Datumbox practical for mid-sized businesses is that it requires no ML expertise. You send text to an endpoint and receive a structured classification. The model complexity is abstracted away. For organisations needing text intelligence without a data science team, this is a sensible starting point. If your business needs to classify text at scale — feedback, support tickets, survey responses — our AI agent development team can integrate Datumbox into your workflows. Talk to our AI consultants about building an automated text analysis pipeline for your data.

Freshping is a website and server uptime monitoring service that checks whether your web properties are online and responding correctly. It sends HTTP, HTTPS, TCP, UDP, and ICMP checks at regular intervals and alerts your team immediately when downtime is detected. For businesses depending on their website for revenue or customer service, hearing about outages from customers rather than your monitoring system is an avoidable failure. Freshping runs checks from multiple global locations, detecting regional outages invisible from a single point. It tracks response times, uptime percentages, and incident history — data that matters for SLA reporting and vendor accountability. Connected to n8n, Freshping alerts trigger incident response workflows rather than just sending notification emails. Our business automation team builds automated incident response pipelines for clients needing structured responses to downtime. Beyond uptime checks, Freshping provides status pages shareable with clients or internal teams. This transparency reduces support enquiry volume during outages. For businesses managing multiple web properties, centralised monitoring keeps everything visible in one place. If your organisation needs uptime monitoring connected to automated alerting and incident management, our system integrations specialists can wire Freshping into your operations tools. Talk to our AI consultants about catching problems before your customers do.

Oxylabs is a web scraping infrastructure provider that supplies proxy networks, scraping APIs, and pre-built data collection tools for businesses that need to extract information from websites at scale. The problems it addresses are the technical barriers that make large-scale web scraping unreliable — IP blocking, CAPTCHAs, geo-restrictions, and constantly changing website structures that break custom scrapers. The platform offers residential and datacenter proxy pools, a web scraper API that handles JavaScript rendering and anti-bot detection, and ready-made scrapers for common targets like ecommerce sites, search engines, and real estate listings. For businesses using automated data processing workflows, Oxylabs provides the raw data collection layer that feeds into downstream processing, analysis, and reporting pipelines. Where Oxylabs adds significant value is in competitive intelligence, pricing monitoring, and market research. Ecommerce companies tracking competitor pricing, real estate platforms aggregating listings, or research firms collecting public data all rely on this kind of infrastructure. Osher’s custom AI development team can build systems that combine Oxylabs data collection with AI-powered analysis to extract structured insights from raw scraped content. If your business depends on web data but your current scraping setup is fragile, rate-limited, or blocked frequently, Oxylabs provides the infrastructure backbone. Connected through system integrations using n8n, scraped data can flow directly into your databases, dashboards, or AI models without manual file handling or broken pipelines.

OCRSpace is an optical character recognition API that extracts text from images, scanned documents, and PDFs programmatically. For businesses dealing with paper-based documents, handwritten forms, or image-based files, OCRSpace converts unstructured visual content into machine-readable text that can be searched, analysed, and fed into digital workflows. The business problem is straightforward: data trapped inside scans cannot be used by your other systems. Invoice details locked in PDF scans need manual retyping into accounting software. Patient forms arrive as photographed documents someone has to transcribe. OCRSpace eliminates that manual step by extracting text accurately and returning it in structured formats. OCRSpace supports multiple OCR engines, handles over 20 languages, and processes everything from clean printed text to handwritten content. Its API-first design makes it easy to embed into existing workflows — send an image, get text back in seconds. This pairs well with automated data processing pipelines that ingest documents at scale. Osher Digital builds document processing workflows for Australian organisations using tools like OCRSpace. Our AI agent development team pairs OCR with AI classification models to go beyond raw text — identifying document types and pulling specific fields automatically. See our medical document classification case study for a similar approach.

Deepgram is a speech-to-text and audio intelligence API that converts spoken language into accurate, structured text using deep learning models. For organisations dealing with call recordings, meeting transcripts, voicemail processing, or audio content, Deepgram provides fast and accurate transcription that outperforms many traditional speech recognition engines, particularly on noisy audio or domain-specific vocabulary. The applications go well beyond simple transcription. Once audio is converted to text, it can be searched, analysed for sentiment, summarised, or fed into downstream workflows that extract action items, flag compliance issues, or update customer records. Osher Digital’s AI agent development team builds intelligent pipelines that take Deepgram’s output and do something useful with it — routing calls, summarising meetings, or triggering follow-ups based on what was said. Deepgram supports real-time streaming transcription and batch processing of recorded files. It handles multiple languages, speaker diarisation (identifying who said what), and punctuation. For organisations processing large volumes of audio, our automated data processing specialists design pipelines that handle ingestion, transcription, and downstream analysis at scale. If your organisation has audio data that is currently going unanalysed — customer service calls sitting in a folder, meeting recordings nobody reviews — Deepgram combined with smart automation can unlock the value in that content. Osher Digital’s custom AI development team can build a solution tailored to your audio processing needs.

StatusCake is an uptime monitoring and performance testing platform that tracks whether your websites, servers, and APIs are online and responding correctly. It checks your endpoints from multiple global locations at configurable intervals and sends alerts via email, SMS, Slack, or webhook when something goes down. For any business that relies on its web presence for revenue, knowing about outages before your customers do is critical. Beyond basic uptime checks, StatusCake monitors page speed, SSL certificate expiry, domain registration status, and server resource usage. This gives operations teams a comprehensive view of infrastructure health without piecing together data from multiple tools. When integrated with incident management through system integrations, StatusCake alerts can automatically create tickets, page on-call engineers, and trigger diagnostic workflows. For organisations running multiple websites or managing client infrastructure, StatusCake’s multi-site monitoring and status pages are particularly valuable. Connecting StatusCake to workflow automation via an n8n consultant enables sophisticated incident response — automatically restarting services, scaling resources, or routing traffic when performance degrades. This kind of business automation reduces mean time to recovery and keeps human involvement focused on complex issues. StatusCake also provides historical uptime data and performance trends, useful for SLA reporting, capacity planning, and identifying recurring infrastructure problems before they escalate.

CrowdPower is a customer engagement and behavioural automation platform that helps businesses send targeted messages based on what users actually do within their product. Rather than blasting generic emails to your entire list, CrowdPower tracks user actions — signups, feature usage, purchases, inactivity — and triggers personalised messages at exactly the right moment. The real value of CrowdPower shows up when it is connected to the rest of your technology stack. Through system integrations, user behaviour data from your application can flow into CrowdPower to trigger onboarding sequences, re-engagement campaigns, upgrade prompts, and churn prevention messages. Each customer gets a tailored experience based on their actual usage patterns, not assumptions about what segment they belong to. For SaaS companies and digital product businesses, combining CrowdPower with business automation creates a complete customer lifecycle engine. New users receive onboarding guidance based on which features they have explored. Active users get notified about advanced features. At-risk users receive retention offers before they cancel. All of this runs automatically once configured, freeing your team to focus on strategy rather than manual outreach. CrowdPower also provides analytics on message performance and engagement trends. Working with an AI consultant can help build predictive models on top of this data — identifying which behaviours indicate conversion or churn, and refining messaging accordingly.

ISN (Inspection Support Network) is a contractor and supplier management platform used by hiring clients in oil and gas, manufacturing, construction, and utilities to verify that their contractors meet safety, insurance, and compliance requirements. ISN collects and validates contractor documentation — safety records, insurance certificates, training credentials, and regulatory compliance data — giving hiring organisations a centralised view of contractor risk. For organisations managing large contractor networks, the manual process of collecting and tracking compliance documents is enormously time-consuming. ISN automates much of this by requiring contractors to submit documentation through the platform, where it is reviewed against specific requirements. When combined with automated data processing, ISN data can feed into compliance dashboards and procurement systems without manual re-entry. Integrating ISN with existing business systems through system integrations creates a more connected contractor management process. Compliance status changes can automatically update approved vendor lists, trigger alerts when certifications expire, and block non-compliant contractors from project assignments. This reduces the risk of engaging contractors who do not meet safety or regulatory standards. For organisations building more sophisticated contractor risk assessment, working with an AI consultant can help analyse patterns in safety data — identifying leading indicators of risk and prioritising audit efforts based on data rather than guesswork.