Off-the-shelf AI products are built for the average case. When your use case involves proprietary data formats, complex decision logic, or precision requirements that general tools cannot meet, you need a system built from the ground up. We design, build, test, and deploy every component — with full documentation and 30-day post-launch support included.
Generic AI integrations can solve generic problems. But when your workflow involves proprietary data schemas, multi-step reasoning, domain-specific classification, or strict latency requirements, adapting a general model rarely works well enough to justify deployment. We build each component from specification: data ingestion, model selection and fine-tuning, API layer, and monitoring instrumentation.
Development runs in two-week sprints with working demos at each checkpoint. You review real behaviour against real data — not mock-ups — and provide feedback that directly shapes the next iteration. We work on your infrastructure or a dedicated isolated environment, with all GDPR-relevant data processing documented and contractually bounded.
We work in short iterations with regular demos. You see working software early and often — not a black box delivered at the end.
Document QCustom language model integrations for document Q&A, summarisation, classification, and content generation — fine-tuned on your data.amp;A systems, summarisation pipelines, and classification engines built using LangChain, LlamaIndex, or direct API integration with OpenAI, Anthropic, or self-hosted models — fine-tuned on your domain data where accuracy requires it.
Multi-step AI agents that plan, execute, and adapt — built with defined tool boundaries, failure handling, and human-in-the-loop escalation paths where your compliance or risk requirements demand them.
Image and video analysis using PyTorch or TensorFlow-based architectures, with inference optimised for your hardware constraints and throughput requirements — covering defect detection, quality inspection, and document extraction.
Forecasting and scoring models trained on your historical data: demand planning, churn prediction, credit risk scoring, and dynamic pricing. Every model ships with performance benchmarks, explainability outputs, and a defined retraining trigger.
REST or gRPC endpoints designed for sub-100ms response at production traffic volumes using Triton, TorchServe, or vLLM — with version management integrated directly into your existing application or ERP environment.
Automated test suites covering model accuracy, edge case behaviour, regression on labelled datasets, and load performance — written to run in your CI/CD pipeline so every change is validated before promotion.
Define the full system design — models, data flows, APIs, and infrastructure. Validate the approach with a working prototype before full build.
Build in sprints with demos every 1–2 weeks. Test continuously. Refine based on your feedback until the system performs exactly as specified.
Production deployment with monitoring, alerting, and documentation. We stay available for 30 days post-launch to handle any issues.
Bring your specific problem — data format, current process, accuracy requirements, integration constraints. In 30 minutes we'll give you an honest view of what's buildable, what the main technical risks are, and what a realistic engagement looks like.
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