AI consulting from people who ship the code.
AI consulting services answer one question: what should your company build with AI, in what order, and at what cost — before you spend a year finding out the hard way. Ours are run by engineers, not analysts: the same team that puts systems into production writes the roadmap.
BroutonLab has shipped ML since 2017 and LLM products since 2023. That's the difference between advice that sounds right in a meeting and advice that survives contact with your codebase.
What our AI consulting services cover.
AI strategy consulting
Which problems in your product are actually AI problems, in what order to attack them, and what each one costs to ship. A roadmap with numbers and sequencing — not a vision deck.
Generative AI consulting
LLM consulting grounded in shipping products since 2023: model selection, RAG vs fine-tuning, cost-per-request math, and where generative AI genuinely beats classic approaches — plus where it doesn't.
Machine learning consulting
Classic ML and data science consulting for the problems LLMs are wrong for: forecasting, ranking, matching, scoring. Often the cheaper, faster, more reliable answer.
MLOps consulting
Deployment, monitoring, evals, and inference-cost control. If your model works in a notebook but scares everyone in production, this is the gap we close.
Data readiness audit
What your data can actually support today, what it can't yet, and the shortest path to fixing that — labeling strategy, pipelines, synthetic data where it's justified.
Build vs buy decisions
A vendor-neutral answer to the most expensive question in AI: use an off-the-shelf tool, fine-tune something open, or build custom. We don't resell anyone's platform, so the answer isn't rigged.
The deck is not the deliverable.
Most AI consulting firms sell strategy and leave before the first line of code. The failure mode is predictable: a beautiful roadmap that no one can implement at the quoted budget. Our consulting carries a different kind of accountability — every recommendation is written by people who might have to build it next quarter, at the price they quoted, with the architecture they proposed. It's easy to be visionary when you'll never see the codebase again. We will, so we aren't.
Audit. Roadmap. Then — only if it's justified — build.
- 01
Audit — 2 to 3 weeks
We read your code, your data, and your product plans; interview the team; and test what your current stack can really do. Fixed fee, agreed upfront, no obligation to continue.
- 02
Roadmap with numbers
A written plan your engineers can execute — with us or without us: prioritized use cases, architecture choices with reasons, cost and timeline estimates, and the risks we'd bet on going wrong first.
- 03
Pilot, if the math holds
When the roadmap says build, the same people who wrote it can ship the first slice: a fixed-price pilot ($30–50K, 2–4 months) to production. Consulting that has to survive its own recommendations.
The full engineering practice behind the advice — custom AI development — is what makes the estimates real.
When to start with consulting.
Start with an audit when
- You have an AI budget and three conflicting opinions about where it should go
- A prototype works in demos but nobody can say what production costs
- The board wants an AI answer and you want one that survives an engineer's questions
- You're about to sign a large vendor contract and want a rigged-free second opinion
Skip straight to building when
- You already know exactly what to build and just need senior hands — skip to a pilot
- The question is a week of prototyping, not a report — vibe-code it first, then talk
Advice with production behind it.
Questions founders actually ask.
How much does an AI consultant cost?
Market rates are all over the place, which is exactly why we don't bill by the hour. Our AI audit is a fixed fee agreed before we start, scoped to 2–3 weeks. If the roadmap leads to building, the pilot is $30–50K fixed for 2–4 months. You always know the number before you commit to it.
Should I hire an AI consultant or an AI development company?
If you only ever get a report, you've bought a deck. The useful version is consulting connected to delivery: our AI consulting services are run by the same engineers who build and operate production systems, so the advice is priced with shipping in mind — and if you already know what to build, skip the consulting and start with a pilot.
What do we get at the end of an audit?
A written roadmap your team can execute without us: prioritized use cases with effort and cost estimates, architecture recommendations with the reasoning spelled out, a data-readiness verdict, an MLOps consulting note on how to deploy and monitor it, and a list of the risks we would attack first. Deliberately vendor-neutral and specific enough to hand to any competent engineering team.
Can you audit an AI system another team built?
Yes — including vibe-coded prototypes, half-finished vendor integrations, and models nobody currently understands. We tell you what's salvageable, what to rewrite, and what it costs either way. No judgment: a messy working prototype is a better starting point than a clean idea.
Do you cover governance, privacy, and compliance?
We cover the engineering side honestly: data-flow documentation, access control, audit trails, self-hosted deployment when data can't leave your infrastructure. Formal certification and legal opinions stay with your compliance advisors — we build so their job is easy, and we don't pretend to replace them.
We're a startup, not an enterprise. Is consulting overkill?
The 2–3 week audit exists precisely for founder-stage companies: long enough to give you a defensible plan, short enough not to burn your runway. Most of our consulting clients are founders choosing between build paths before committing a year of budget — the audit usually pays for itself by killing one wrong direction.