Data & Analytics

Search And Save is a data extraction tool that helps businesses capture and organise information from web searches and online directories. Instead of manually copying details from search results into a spreadsheet, Search And Save automates the collection process — pulling contact information, business listings, and other structured data into formats you can actually work with. For sales and marketing teams, this kind of tool solves an everyday problem: building prospect lists from publicly available information takes hours of tedious manual work. Search And Save reduces that to minutes. But the real efficiency gains come when you connect it to the rest of your sales stack. Osher Digital’s sales automation services help businesses wire tools like Search And Save into automated pipelines that capture, clean, enrich, and route leads without manual intervention. The data you extract is only as useful as what you do with it. Raw search results need deduplication, formatting, and enrichment before they’re ready for outreach. Connecting Search And Save to your CRM through workflow automation means every extracted record gets processed and scored before it reaches your sales team. Osher Digital’s automated data processing capabilities are built for exactly this kind of data pipeline work. If your team is still copying and pasting from Google search results into spreadsheets, you’re burning hours that could go toward actual selling. The right combination of extraction tools and system integrations can automate the entire front end of your prospecting workflow.

TiDB Cloud is a fully managed distributed SQL database service built on the open-source TiDB project. It offers MySQL compatibility with horizontal scalability, which means businesses can run familiar SQL workloads while handling data volumes and traffic spikes that would bring traditional single-node databases to their knees. The real-world problem TiDB Cloud solves is the scaling wall. Many organisations start with MySQL or PostgreSQL, and things work fine until data grows into the tens of millions of rows or traffic spikes become unpredictable. At that point, you’re looking at sharding (painful), read replicas (complicated), or migrating to a new database entirely (expensive). TiDB Cloud sidesteps this by offering transparent horizontal scaling — you keep writing standard SQL while the platform handles distribution under the hood. For data-intensive businesses, TiDB Cloud fits naturally into a broader automated data processing architecture. It handles the storage and querying layer while automation tools manage the pipelines feeding data in and pulling insights out. Our AI consulting team works with businesses to design these kinds of scalable data architectures, ensuring your database choice supports both current workloads and future growth. Whether you’re dealing with high-volume transactional data, real-time analytics, or hybrid workloads that need both, TiDB Cloud is a strong contender. If you’re weighing your database options or planning a migration from an existing system, our custom development team can help you evaluate the trade-offs and implement the right solution for your specific requirements.

Clarifai is an AI platform specialising in computer vision, natural language processing, and audio recognition. It provides pre-built models and tools for building custom AI applications that can understand images, video, text, and audio — tasks that would take months to develop from scratch using raw machine learning frameworks. The practical applications are broad. Retail businesses use Clarifai for product image tagging and visual search. Healthcare organisations apply it to medical image analysis. Manufacturing companies use it for visual quality inspection on production lines. The common thread is converting unstructured visual or textual data into structured, actionable information that systems can act on. Clarifai really comes into its own when integrated into automated workflows rather than used as a standalone tool. For example, connecting Clarifai’s image classification to an n8n workflow can automate document processing, product categorisation, or content moderation at scale. Our AI agent development team builds these kinds of intelligent automation systems, and our custom AI development services can help you train Clarifai models on your specific data for higher accuracy. We’ve done similar work in medical document classification using AI-powered recognition. If your business has processes that depend on humans looking at images, reading documents, or categorising content manually, Clarifai can likely automate a significant portion of that work. Our AI consulting team can help you identify the highest-value opportunities and build a roadmap for implementation.

Morph is a data transformation and analytics platform that brings together SQL, Python, and visual tools in a single workspace. It’s designed for teams that need to analyse, transform, and visualise data without constantly switching between disconnected tools or waiting on engineering resources to build pipelines from scratch. The core appeal of Morph is flexibility. Analysts can write SQL queries, data scientists can run Python notebooks, and less technical team members can use visual interfaces — all within the same project. This removes the friction that typically slows down data work, where insights get stuck behind tool access or skill gaps. For organisations looking to get more value from their data, Morph works well as part of a broader automated data processing strategy. Rather than treating analytics as a one-off activity, you can build reusable transformation pipelines that feed dashboards, reports, and downstream systems. Our AI consultants frequently help businesses design these kinds of integrated data workflows. If your team is spending too much time cleaning and reshaping data before anyone can actually analyse it, Morph is worth a look. Combined with workflow automation from tools like n8n — something our n8n consulting team specialises in — you can build end-to-end data pipelines that run on schedule and surface the insights your business actually needs.

Roboflow is a computer vision platform that makes it practical for development teams to build, train, and deploy image recognition models without needing deep expertise in machine learning infrastructure. It covers the full pipeline — from labelling training images to deploying a model that can classify, detect, or segment objects in real time. For businesses that need visual inspection, object counting, or image-based quality control, Roboflow removes much of the engineering overhead. The platform provides tools for dataset management, image augmentation, and model training. Users upload images, annotate them with bounding boxes or labels, and train models directly within the interface. Roboflow supports popular model architectures and allows export to multiple deployment targets including edge devices, cloud APIs, and mobile applications. This flexibility matters for Australian businesses operating in environments where internet connectivity is not always reliable. Computer vision projects often stall because the gap between a working prototype and a production deployment is significant. Roboflow helps close that gap. Osher Digital’s custom AI development team has experience building vision-based solutions for clients, and our AI consulting services can help you assess whether computer vision is the right approach for your specific use case — or whether a simpler solution would do the job. Whether you are inspecting products on a manufacturing line, monitoring assets in the field, or automating visual classification tasks, Roboflow provides the tooling to move from idea to deployed model. Our system integrations team can connect the outputs into your existing operational workflows.

Teknoir is an AI development platform built for deploying machine learning models at the edge and in the cloud. It provides the infrastructure for teams to build, train, and run AI applications without managing the underlying compute and orchestration complexity themselves. For businesses that need AI running on devices, sensors, or distributed systems — not just in a data centre — Teknoir offers a practical path to production. The platform handles model lifecycle management, from development through deployment and monitoring. This includes container orchestration, device management, and model versioning, which are the operational tasks that often stall AI projects long after the initial proof of concept works. Teknoir packages this infrastructure so that development teams can focus on the models themselves rather than the plumbing around them. Australian organisations building AI products or embedding intelligence into physical operations can benefit from pairing Teknoir with experienced AI agent development support. Getting models from a laptop to production — especially at the edge — requires careful architecture decisions. Working with a custom AI development partner ensures those decisions are made well, and connecting Teknoir with existing business systems through proper system integrations keeps everything running together reliably. Teknoir is aimed at technical teams building IoT applications, computer vision systems, predictive maintenance solutions, and other AI use cases that require edge deployment. If your AI models need to run close to the data source rather than in a central cloud, this platform is worth serious consideration.

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.