We Build the AI That Runs on Your Data

We wire large language models into the software you already run — grounded in your documents and records, not the open web. Retrieval-augmented and cache-augmented generation over your own corpus, citation-backed answers, structured output contracts so downstream systems can trust the response, prompt and system-message governance, streaming chat interfaces, and a provider abstraction so moving between a local model, Claude, or GPT is a configuration change rather than a rewrite.

We also build the part most teams skip: evaluation. A held-out benchmark set, a regression corpus, and a scored pass/fail gate, so “the AI got worse” becomes a number on a dashboard instead of an argument in a meeting.

If you hold a dataset nobody else has — inspection images, field reports, claims history, sensor traces, decades of domain documents — a general-purpose foundation model will never be as good on your problem as a model adapted to it. We do the whole path: dataset curation and labelling strategy, train/validation/test splits that do not leak, parameter-efficient fine-tuning and LoRA adapters, domain embedding models, quantization for the hardware you actually own, and benchmark scoring against the base model so the improvement is measured rather than asserted.

Where the data is restricted, training happens on infrastructure you control, with a de-identification stage ahead of it. Your corpus is never used to train anything we ship to anyone else.

Multi-agent orchestration where a planner decomposes the work, specialist agents execute against real tools, and a verifier checks the result before anything is committed. We build on graph-based planning — the subject of one of our patent-pending filings — with Model Context Protocol servers so each of your tools is wired once instead of once per agent, human-in-the-loop approval gates on any action with consequences, and full run traces so you can answer “why did it do that” three months later.

We are blunt about where agents belong. Deterministic code beats an agent for deterministic work, and we will tell you when the honest answer is a cron job.

Detection, classification, segmentation, OCR and document understanding — trained on your imagery and evaluated against your defect rates, not a public benchmark. Defect and anomaly detection for production lines, object counting and presence verification, condition and damage assessment, safety and PPE compliance, document and form vision, and label or serial capture.

The engineering that makes it survive contact with a real line: an annotation workflow your own staff can run, class-imbalance handling for the defect you only see twice a month, confidence thresholding tuned to your cost of a false negative versus a false positive, and a drift monitor that tells you when the process changed and the model did not.

Some data should never leave the building — because the link is unreliable, because the latency budget is milliseconds, because bandwidth costs more than the hardware, or because the regulator says so. We take models that work in the lab and make them run on constrained devices: quantization and pruning, TensorRT and ONNX Runtime compilation, NVIDIA Jetson-class and industrial-PC targets, and accelerator selection matched to your thermal and power envelope rather than to a spec sheet.

Then the operational half: over-the-air model rollout with staged deployment and rollback, store-and-forward for disconnected operation, fleet health telemetry, and a hybrid split where the edge decides in real time and the centre only ever sees the aggregate.

This is our home ground. Scans, photographs, faxes, PDFs and forms go in; validated, typed, queryable records come out. Ingestion and format normalization, OCR with layout and table awareness, deterministic field extraction ahead of any language model so numbers are parsed rather than generated, schema mapping and unit normalization, confidence scoring with an exception queue for human review, and sensitive-data scrubbing before anything reaches a model.

And the piece that decides whether the whole thing is trustworthy: a regression corpus. Every extraction rule is scored against a fixed set of known-correct documents, so a change that quietly breaks one field type is caught before it reaches production instead of after.