AI that ships, not AI that demos
The gap between an impressive prototype and a system your business can rely on is where most AI projects die. We work on the far side of that gap: evaluation, guardrails, latency, cost, and the unglamorous data plumbing underneath.
AI that survives production
The gap between an impressive prototype and a system your business can rely on is where most AI projects die. We work on the far side of it.
Retrieval & RAG Systems
Answering questions from your own corpus — contracts, tickets, policies, claims — with citations back to the source document.
- Document ingestion and chunking
- Vector and hybrid search
- Citation and grounding
- Access control that respects permissions
Extraction & Classification
Turning unstructured documents into structured rows, with low-confidence cases routed to a human instead of guessed.
- Field extraction from PDFs and forms
- Document classification and routing
- Confidence thresholds and human review
- Full audit trail to the source line
Model Deployment & MLOps
Getting the model out of the notebook and keeping it healthy once real traffic hits it.
- Deployment in your own cloud and VPC
- Latency and cost optimisation
- Drift and quality monitoring
- Safe rollback on regression
Evaluation Harnesses
The part everyone skips. If you cannot measure whether your system got worse, you cannot safely change it.
- Golden datasets built from real inputs
- Automated scoring on every change
- Regression gates in CI
- Human review workflows
Guardrails & Safety
Keeping the system inside the boundaries your business and your regulator require.
- Prompt injection defences
- PII detection and redaction
- Refusal and escalation paths
- Output validation
Automation & Agents
Where a human is doing something repetitive and rules alone cannot capture it.
- Triage and routing
- Assisted drafting with human review
- Workflow automation
- Tool-using agents with hard limits
Why choose our AI team
Most AI budgets get spent on things a database query would have solved.
We build the eval first
Before we build the feature, we build the way to measure it. It is the difference between an AI system you can maintain and one you are afraid to touch.
Your data stays yours
We can deploy entirely within your cloud account and VPC, with nothing leaving your boundary. Where it goes is your decision, not a side effect of our architecture.
Cost and latency are features
A model that costs $0.40 a call and takes nine seconds is a demo, not a product. We right-size the model per task and tell you honestly when a smaller one does the job.
Our AI delivery process
From a use case to a system your team can actually run.
Use Case Triage
Is this genuinely an AI problem, or a query? We will say so.
Evaluation Set
Real inputs, expected outputs, scored automatically.
Build & Iterate
Retrieval, prompting, guardrails — measured on every change.
Deploy & Monitor
Into your cloud, with drift and cost monitoring from day one.
Where AI genuinely pays
We have watched a lot of AI budgets get spent on things that a database query would have solved. Here is where it earns its keep:
- Retrieval over your own corpus — contracts, tickets, policies, claims. High volume, high cost of a human reading it.
- Extraction and classification — turning unstructured documents into structured rows.
- Triage and routing — getting the right item to the right human faster.
- Assisted drafting — where a human still reviews, but starts from 80% instead of a blank page.
The part everyone skips: evaluation
If you cannot measure whether your system got worse, you cannot ship changes to it safely. We build an evaluation set before we build the feature — real inputs, expected outputs, scored automatically on every change. It is the difference between an AI system you can maintain and one you are afraid to touch.
Cost and latency are product decisions
A model that costs $0.40 per call and takes nine seconds is not a feature, it is a demo. We optimise the retrieval, cache aggressively, right-size the model per task, and tell you honestly when a smaller model does the job.
Questions we get asked
Do we need our own model?
Almost certainly not. The overwhelming majority of business value comes from good retrieval and good prompting over a hosted model. Fine-tuning is a later optimisation, not a starting point.
Where does our data go?
Wherever you decide. We can deploy entirely within your cloud account and VPC, with no data leaving your boundary.
How long to a working system?
A useful, evaluated internal system in 6–10 weeks is realistic. Anyone promising two weeks is showing you a demo.
Have a use case in mind?
Tell us what a human is doing repetitively. That is usually where the real answer is.