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.
- 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.
- 011 week
Frame the decision
We identify the specific decision the model will improve and agree the metric that proves it worked. No metric, no project.
- 021–2 weeks
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.
- 033–4 weeks
Prototype and evaluate
A working model measured against a held-out benchmark and the human baseline it is meant to beat.
- 043–4 weeks
Productionise
API, authentication, guardrails, caching, cost controls, observability and a rollback path.
- 05Ongoing
Operate and improve
Drift monitoring, scheduled re-evaluation, retraining triggers and a quarterly performance review.
Questions
Artificial Intelligence — common questions
If yours is not here, ask Nova or send it to us directly.
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.