What we build
Four services, each with a clear deliverable. Pricing is scoped per project after we've seen your data — we don't quote blind, because blind quotes are guesses.
ML prototypes
You have an idea — maybe "could a model predict which support tickets will escalate?" — and you need to know if it works before betting a roadmap on it. We build the smallest honest version: real data in, real predictions out, measured against a baseline.
You get: a working prototype, an evaluation report with error rates, and a clear recommendation — build it out, adjust the approach, or drop it. Typical timeline: 3–8 weeks.
Data pipelines
Unsexy, and usually the thing that decides whether a project succeeds. We build ingestion from your sources, cleaning and validation steps that catch bad data before it poisons anything downstream, and storage layouts your analysts can actually query.
You get: tested pipeline code (typically Python), data quality checks with alerts, and documentation of every transformation. Works standalone or as the foundation for a model project.
Computer vision & NLP
The applied stuff: pulling structured fields out of documents, classifying images, semantic search over your knowledge base, summarising long text. In most cases we fine-tune strong existing models rather than training from scratch — it's faster, cheaper, and usually works better.
You get: a model tuned to your data, benchmarked against off-the-shelf alternatives so you can see whether the customisation was worth it. If a plain API call beats our fine-tune, we'll show you that comparison too.
Deployment & monitoring
Maybe you already have a model — built in-house, or left behind by a previous vendor — and it lives in a notebook. We turn it into a service: an API with sensible latency, monitoring for accuracy drift and cost, and alerts before things quietly degrade.
You get: a deployed service on your infrastructure (cloud or on-prem), dashboards, runbooks, and a handover session with your engineers. Optional ongoing maintenance if you want a second pair of eyes.
A note on compute
Prototypes and pipelines usually run on CPUs; fine-tuning and heavy inference run on NVIDIA GPUs rented by the hour or installed on-prem. We benchmark before renting, and the compute always sits in your account — not ours.
How we pick hardware for each workload →When we're the wrong choice
Saving everyone time: a few situations where you shouldn't hire us.
You need a big team fast
We're a small studio and we don't pretend otherwise. If you need twenty engineers by next month, a larger consultancy will serve you better.
You want "AI" for the press release
If the goal is a headline rather than a working system, we're a bad fit. We only take projects where somebody actually plans to use the thing.
There's no data yet
ML needs data to learn from. If you're pre-launch with nothing collected, let's talk about instrumenting your product first — and come back to models later.