Most AI programmes do not fail at the model. They fail because the data underneath was never good enough to answer the question being asked of it.
We treat data engineering and applied AI as one practice, staffed by the same people. The engineer who builds your pipeline is in the room when the model is evaluated — which is the only reliable way to establish early that the ground truth is wrong.
Every engagement can begin in discovery and graduate into platform build, GenAI and agentic delivery, or vision AI — drawing on a shared engineering core.
Rapid prototyping against a defined question, with stakeholder alignment and success criteria agreed before any build begins.
Cloud-native data and feature pipelines, reproducible training and a model registry, deployment, monitoring and observability.
Retrieval-augmented generation, agentic workflows with evaluation, and custom domain models where they earn their place.
Multi-class detection and OCR, video analytics and scene understanding, edge deployment and continuous learning.
So that each new use case draws on existing infrastructure rather than rebuilding it.
Ingest · transform · serve
Source systems into a data factory, curated data assets, and a feature store the rest of the platform builds on.
Train · validate · deploy
Curated tables through training pipelines to a model registry and served endpoints, with drift detection and retraining loops that survive their author leaving.
Orchestrate · trace · govern
Multi-provider model access, agentic workflows, and end-to-end observability across every call.
Consume · integrate · serve
Front-end applications and services consuming AI endpoints over private networking.
Route to the appropriate model at the appropriate cost, coordinate multi-agent workflows, and trace every call. Production systems, not notebook experiments.
A central layer managing all providers — deploy, version and route across proprietary and open-source models, selecting the least expensive model that meets the quality bar.
Guardrails for prompt-injection prevention, input and output validation, and conversation memory built in from the outset.
End-to-end tracing on every call: prompts, completions, latency, token usage, cost, and reasoning logs for complex chains.
Per-provider credentials held in managed vaults with secure rotation. No keys in code.
Agentic systems fail in ways a single accuracy score will not surface, so we evaluate on four axes rather than one.
Agent-routing correctness, coordination order, and tool-call dependency.
Retrieval judged against the source knowledge base, with citation coverage measured.
Response latency, turns to resolution, and satisfaction proxy metrics.
Escalation correctness, and refusal on personal data or out-of-policy requests.
Retro-fitting governance onto a working AI system costs materially more than building it in. Six layers, with clear separation between internal teams and vendor access.
Managed identity for all services, conditional access, zero-trust from the outset. No shared keys.
Hub-and-spoke networking with private link, no public internet exposure, and per-environment isolation.
Centralised vaults with private endpoints and automatic rotation for all credentials and tokens.
Project-level workspace isolation with clearly scoped access for internal teams and vendors.
Guardrails enforced at subscription level, threat protection enabled, centralised audit logging.
Data encrypted at rest with platform-managed keys; all traffic encrypted in transit.
Camera analytics, multi-class detection, OCR and scene understanding, with quantised models for on-device inference where bandwidth or privacy rules out the cloud.
A second-pass model suppresses false positives before any action is triggered, and operator feedback retrains on misclassified events over time. Every false dispatch carries a real per-incident cost, so reducing false positives is usually where the return sits.
Person, vehicle, object and intrusion classes with confidence calibration.
A second-pass model on video, suppressing false positives before escalation.
Operator feedback retraining models on misclassified events.
Discovery sprint on your production systems, ending in a go / no-go decision and an ROI analysis.
Phased delivery of the data and MLOps foundation.
A POD accountable for a defined outcome, or squad augmentation where that is what is required.
WorkWeave and BetterSDLC, deployed against your estate.
We will also say when a problem does not require machine learning. A rules engine that can be read and audited is often the better answer.