Production ML, not science projects.
Vision, language and prediction models wired into the tools your team already uses — shipped to production, measured on business outcomes, and maintained by the engineers who built them.
Most ML initiatives die between notebook and production. Ours don’t.
The industry is littered with proofs-of-concept that impressed a steering committee and never processed a single real transaction. The hard part of machine learning was never the model — it is the data plumbing, the retraining loop, the latency budget, and the dashboard a human actually checks at 8 a.m.
We build the whole thing. Every model we ship comes wired into your operations — ingesting live data, monitored for drift, and owned by senior engineers who stay on after launch. If a heuristic beats a neural network for your problem, we will tell you and charge you less.
Intelligence, applied.
Six engagements we run most often — each one scoped to a measurable business number, not an accuracy score in a slide.
Demand & sales forecasting
Models that ingest history, seasonality and live signals so teams plan inventory, staffing and spend with confidence.
Computer vision
Detection, classification and OCR pipelines that run on the edge or in the cloud — built for messy real-world footage.
NLP & document intelligence
Extraction, classification and summarisation across contracts, invoices, tickets and emails — grounded in your corpus.
Predictive analytics
Churn, risk and propensity scoring delivered inside the tools your operators already live in.
AI integration & MCP
LLM features embedded into your existing product — with Model Context Protocol connecting models to your systems safely.
Model ops & retraining
Drift monitoring, evaluation harnesses and scheduled retraining so accuracy survives contact with next quarter.
Boring rigour, exciting results.
The same discipline we apply to all software — adapted for systems that learn.
01Data audit first
Before any model talk, we audit what you actually have — coverage, quality, leakage risks. You get an honest feasibility read.
02Baseline before deep
We ship the simplest model that moves the number, then earn complexity. No GPU bills without a business case.
03Iterate against metrics
Weekly evals on the metric you care about — euros, hours, stockouts — not abstract benchmark scores.
04Ship, monitor, retrain
Production deployment with drift alarms and a retraining loop. The model keeps learning; so does the team.
We have shipped this before.
A machine-learning forecasting platform we designed, shipped and still operate — live, paid for, and earning its keep.
Forecasting Model
Ingests historical sales, seasonality and live signals to predict demand — so teams plan inventory, staffing and spend with confidence instead of guesswork.
Chosen for the problem, not the résumé.
We are framework-agnostic and outcome-opinionated. The stack bends to the problem — never the reverse.
One team. Zero hand-offs.
Disciplines most often combined with AI / ML — same architecture, same engineers, no integration tax.
Straight answers, in writing.
The eight questions buyers of AI / ML ask us most — answered the way we would answer them in the room.
Put it in a brief. A senior ML engineer — not a sales rep — replies within one business day.
Q.01Do we need our own model, or can we build on an API?
Usually an API-first build wins: faster to ship, cheaper to run, easier to swap. We reach for custom or fine-tuned models when latency, privacy or unit economics demand it — and we will show you the maths before recommending either.
Q.02How much data do we need to get started?
Less than you fear. Many engagements start with a few thousand labelled examples or two years of transactional history. The data audit in week one tells you exactly where you stand — before you commit to a build.
Q.03How long until something is live?
Discovery runs one to two weeks and ends with a fixed plan. Most teams see a first production-grade slice — a real model, behind a real endpoint, on your infrastructure — inside six to ten weeks. From there we iterate weekly against the business metric.
Q.04What about hallucinations and accuracy?
Every LLM feature we ship is grounded in your data with retrieval, constrained outputs and an evaluation harness that runs on every change. For predictive models, accuracy is reported against a holdout set and monitored for drift in production.
Q.05Where does our data live, and who can see it?
Inside your cloud account — we deploy into your AWS, not ours. Your data is never used to train shared or third-party models, access is least-privilege and logged, and NDAs and data-processing agreements are signed before the first dataset moves.
Q.06Who owns the models, the code and the weights?
You do — all of it. Code, fine-tuned weights, pipelines, prompts and documentation are assigned to you in the contract. No licensing back, no lock-in: any competent team could take over tomorrow. Most clients stay anyway, because the system keeps improving.
Q.07What does an AI / ML engagement cost?
Outcomes priced, not hours billed. After a discovery sprint you get fixed milestones and a fixed budget in writing. Typical first engagements land between $25k and $75k; you see the number before anyone commits.
Q.08What happens after launch?
We stay. Every engagement includes lifetime support: drift monitoring, scheduled retraining and a senior engineer who knows the system — answering in hours, not a ticket queue. Models degrade silently when nobody watches. Ours are watched.
Have data that should be
working harder?
Tell us what you are trying to predict, automate or understand. A senior ML engineer replies within one business day with an honest read on feasibility.