Discovery & Feasibility
Two-week technical deep-dive: data audit, constraint mapping, and a quantified success case. We walk away if AI is not the right tool.
Solutions
Deep expertise where models meet machinery
Three core practices, one standard: production-grade AI with measurable outcomes, full observability, and honest engineering from prototype to fleet.
Solution 01
Enterprise LLM Fine-tuning
Your proprietary knowledge, encoded into a model you control. We take foundation models and turn them into domain experts — with evaluation harnesses that prove the improvement.
Our fine-tuning practice combines supervised instruction tuning, preference optimisation (DPO/RLHF), and retrieval-augmented generation to build language systems grounded in your data, terminology, and compliance regime.
Every deployment ships with automated red-teaming, hallucination benchmarks, cost-per-token telemetry, and guardrail layers — so your model is auditable from the first pilot to full rollout.
Discuss this solution4.2×
median accuracy lift over off-the-shelf models on domain benchmarks
- Domain-adaptive pre-training & LoRA fine-tuning
- Retrieval-augmented generation over enterprise knowledge bases
- Agentic workflows with tool use and human-in-the-loop review
- Automated eval harnesses, red-teaming & guardrails
- Private, VPC-isolated or on-premise inference
Solution 02
Industrial Automation
Vision and predictive systems that keep lines running, defects out, and workers safe — validated against your throughput targets before a single sensor is installed.
We deploy computer vision for surface-defect detection, assembly verification, and PPE/safety compliance at line speed, alongside predictive maintenance models that forecast equipment failure from vibration, thermal, and SCADA telemetry.
Our industrial systems integrate with MES/ERP platforms and degrade gracefully: every model has a deterministic fallback so production never waits on inference.
Discuss this solution31%
average reduction in unplanned downtime across manufacturing clients
- Line-speed defect detection & classification
- Predictive maintenance from multimodal sensor fusion
- Safety-zone monitoring & PPE compliance vision
- MES / SCADA / ERP integration layer
- Deterministic fallbacks & fail-safe design
Solution 03
Edge-device AI Deployments
Production inference where there is no datacentre: quantised, accelerated models running on constrained hardware in vehicles, devices, rigs, and remote sites.
We compress and compile models — quantisation, pruning, distillation, TensorRT/ONNX compilation — to hit strict latency, memory, and power budgets without sacrificing accuracy that matters in the field.
Fleet management, OTA model updates, drift monitoring, and shadow-mode rollouts are part of every edge engagement, so thousands of devices stay in sync and in spec.
Discuss this solution<8ms
typical p95 inference latency on edge-class hardware
- Quantisation, pruning & knowledge distillation
- TensorRT / ONNX / CoreML compilation & benchmarking
- Sub-10ms latency on constrained NPUs & GPUs
- OTA model updates & fleet telemetry
- Drift detection with shadow-mode validation
How We Work
From feasibility to fleet in four phases
Rapid Prototype
A working model against your real data within six weeks, benchmarked against agreed metrics and your current baseline.
Production Hardening
Evaluation harnesses, observability, guardrails, fail-safes, and security review. Nothing ships without an ops runbook.
Deploy & Compound
Staged rollout with drift monitoring, then continuous retraining. Your team is trained to own the system — we make ourselves optional.
Not sure which practice fits your problem?
Send us the constraint — latency, compliance, throughput, cost — and we'll map it to the right architecture. NDA available before any discussion.
Book a Technical Consultation