Do we need a data scientist on our team first?

No. We deliver the full stack and train your people as we go. Most clients take over day-two operations within a quarter; the ones who keep us on do so for model improvement, not keeping the lights on.

Can the model run without sending data to a third party?

Yes. We deploy open-weight models on your own infrastructure — on-premise, in your VPC, or fully air-gapped. This is the default for mining, government and financial-services clients.

How do you stop the assistant from inventing answers?

Every response is grounded in retrieved source documents and cites them. Anything the retriever cannot support is refused rather than guessed, and low-confidence cases are routed to a human.

What does it cost to run?

We model inference cost per transaction before we build, and set hard token and spend ceilings in the code. You see projected monthly cost in the proposal, not after the first invoice.

Models that earn their keep

Artificial Intelligence

Applied AI that plugs into the systems you already run — forecasting, computer vision, document intelligence and assistants grounded in your own data.

Discuss a artificial intelligence project What this includes

Faster document processing: 84%; Median reduction in manual handling time across document-intelligence deployments.
Forecast accuracy uplift: 31%; Average improvement over the incumbent statistical baseline.
Time to first production model: 9 weeks; From kickoff to a monitored model serving live traffic.

Our position

How we think about it

Most AI projects stall because they are built as demonstrations rather than as production systems. We start from a measurable business decision, work backwards to the data required to improve it, and ship a model behind a monitored API with a human review path. Every engagement includes evaluation harnesses, drift detection and a retraining runbook, so the system keeps performing after the launch announcement.

What you receive

Opportunity assessment with ranked, costed use cases
Data readiness audit and remediation plan
Working prototype evaluated against a labelled benchmark
Production API with authentication, rate limiting and observability
Evaluation harness and regression suite
Monitoring dashboards for accuracy, latency, cost and drift
Operating runbook and team enablement sessions

Capabilities

What we actually build

Retrieval-augmented generation over your internal knowledge base
Computer vision for inspection, safety and asset condition monitoring
Demand, yield and maintenance forecasting
Document intelligence — extraction, classification and validation
Conversational assistants with tool access and escalation to humans
Speech-to-text and text-to-speech for field and call-centre workflows
Model evaluation, guardrails and red-teaming
On-premise and air-gapped deployment for regulated environments

Technologies we use

Python
PyTorch
Hugging Face
LangChain
pgvector
Qdrant
Ollama
ONNX Runtime
FastAPI
Ray
MLflow
NVIDIA Triton

Process

How a artificial intelligence engagement runs

Each phase ends with something you can look at and a decision you can make.

01
Frame the decision
We identify the specific decision the model will improve and agree the metric that proves it worked. No metric, no project.
1 week
02
Assess the data
Volume, quality, labelling, lineage and access controls. We report honestly if the data is not ready and what it costs to make it so.
1–2 weeks
03
Prototype and evaluate
A working model measured against a held-out benchmark and the human baseline it is meant to beat.
3–4 weeks
04
Productionise
API, authentication, guardrails, caching, cost controls, observability and a rollback path.
3–4 weeks
05
Operate and improve
Drift monitoring, scheduled re-evaluation, retraining triggers and a quarterly performance review.
Ongoing

Where this applies

Questions