Service · Models that earn their place in production.
Applied AI &machine learning
We design, train and ship machine-learning and generative-AI systems that sit inside a real product and move a measurable number — not demos that stall after the pilot.
The problem
Most AI work dies between the notebook and production. The model scores well offline, then nobody can deploy it, monitor it, or explain what it changed.
Who it’s for
Teams with real operational data — support queues, documents, transactions, fleet telemetry — who want a working system rather than a proof of concept.
Overview
We start from the decision you want to improve, not the model. That fixes the target metric, the data you actually need, and whether machine learning is even the right tool — sometimes a rules engine wins and we will say so.
From there we build the full path: data pipeline, training and evaluation, an inference service with sane latency and cost, and the monitoring that tells you when quality drifts. Retrieval-augmented generation is used where answers must be grounded in your own documents rather than a model's memory.
Every system ships with an evaluation set and a rollback plan. If a new model version is worse, you can see it and revert it the same day.
Models that earn their place in production.
What’s included
Discovery & feasibility
We map the decision, the data available, and the honest success criteria before any model is trained.
Data pipeline
Ingestion, cleaning, labelling workflow and a reproducible training set with versioning.
Model development
Fine-tuning, classical ML or retrieval-augmented generation — chosen on evidence, not fashion.
Evaluation harness
A held-out set and scoring you can re-run on every change, so quality is a number and not an opinion.
Inference service
Deployed API with latency, cost and rate-limit budgets defined up front.
Monitoring & drift alerts
Live quality tracking, so degradation surfaces before your users report it.
How the work runs
- 01
Frame the decision
Which decision improves, who acts on it, and what a good outcome measures.
- 02
Audit the data
What exists, what is usable, what must be captured. Gaps are reported honestly.
- 03
Baseline first
A simple model or heuristic sets the bar every later version must beat.
- 04
Build & evaluate
Iterate against the evaluation harness, not intuition.
- 05
Ship behind a flag
Released to a slice of traffic, measured against the baseline.
- 06
Monitor & retrain
Drift alerts and a scheduled retraining path so quality holds.
Tools we use
What you get
- Measured against a baseline
- Always
- Evaluation set shipped
- With every model
- Rollback path
- Same day
- Deployment
- Your cloud or ours
Where it applies
Document understanding
Turn invoices, duty slips and contracts into structured records that flow straight into your system.
Grounded assistants
Answer staff and customer questions from your own documentation, with citations back to the source.
Forecasting & routing
Predict demand and assign work using your own operational history.
Quality and anomaly detection
Flag the transactions or trips that do not look like the rest, early.
Frequently asked questions
- Do we need a large dataset to start?
- Not always. Retrieval-based systems work from your existing documents with no training data, and classical models often perform well on a few thousand well-labelled examples. We tell you which case you are in during discovery.
- Will our data be used to train public models?
- No. Your data stays in your environment or ours under contract, and is never sent to train third-party public models.
- How do you prove the model actually works?
- Every engagement ships an evaluation harness — a held-out dataset and a score you can re-run yourself on any future change.
- What if machine learning is the wrong answer?
- We say so in discovery and propose the simpler system instead. A rules engine that works beats a model that does not.
Got a project in mind? Let's make it a reality together
Looking to make your mark? We'll help you turn your project into a success story.