Custom AI development, shipped to production.
Custom AI development means building AI around your data, your workflows, and your constraints — instead of bending your product around someone else's tool. It's what you need when the AI is the product, not a feature.
BroutonLab is a custom AI development company: a dedicated senior team that designs, builds, and runs production AI — agents, voice systems, ranking engines, ML pipelines — for founders building vertical AI products. ML in production since 2017, LLM products since 2023.
When custom AI development pays — and when it doesn't.
Build custom when
- Your data is the moat — proprietary, domain-specific, or regulated — and generic tools can't touch it
- The AI is your product's core loop, not a feature bolted on the side
- You've hit the ceiling of an off-the-shelf tool: accuracy, latency, cost per call, or compliance
- Models must run inside your infrastructure — VPC or on-prem — for policy or privacy reasons
Skip it (for now) when
- A generic copilot or SaaS tool already solves 90% of the problem at a fraction of the cost
- You haven't validated demand yet — a no-code prototype will teach you more, faster
- The workflow changes weekly; build custom when it stabilizes
We turn down projects from the right column — a pilot that shouldn't exist is expensive for you and bad for us. If you're unsure which side you're on, that's a good first call.
The AI surface of your product, end to end.
AI agents & LLM products
Agents that do real work — call tools, follow multi-step workflows, survive edge cases. Generative AI development services built on whichever model fits, with evals so you know when it breaks.
Voice AI
Production voice agents: telephony, real-time speech, turn-taking, tool calling. Ours makes thousands of calls a day for a US client — booking appointments, switching languages mid-call, escalating to humans when it should.
Ranking & matching engines
Search, recommendation, and matching systems — the invisible ML that decides what your users see first. Built for platforms where a better match is the product.
AI software development services
Custom models and the software around them, where off-the-shelf fails: scarce data, domain-specific labels, on-prem constraints. An AI software development service shipping ML since 2017, with peer-reviewed research behind it.
MLOps & integration
Pipelines, monitoring, deployment, cost control. The part that turns a demo into a system your business can rely on — and keeps it reliable as data drifts and models change.
How custom AI development actually works here.
- 01
Scope the slice
One or two calls to find the hardest valuable slice of your product — the piece that proves the whole thing works. We write the scope down: inputs, outputs, success metrics, what "production" means for you. This becomes the pilot contract.
- 02
Get the data honest
Real projects start with messy data. We audit what you have, build the pipelines to clean and structure it, and tell you plainly if it's not enough — before you've spent the budget, not after.
- 03
Pick the architecture
Commercial LLM, open-weights model, fine-tuning, classic ML, or a hybrid — chosen per problem, not per partnership. The write-up explains why, so your next engineer inherits reasons, not folklore.
- 04
Build against evals
Before the system ships, we build the measuring stick: accuracy evals on your real cases, latency budgets, cost per request. Every change is judged by the numbers, not by how the demo felt.
- 05
Ship, monitor, iterate
Deployment, monitoring, drift alerts, cost control — the MLOps layer that keeps the system trustworthy in month twelve, not just week one. Then the roadmap continues with the same team.
Every step above happens inside the pilot — it's the full process at small scale, not a discovery phase that ends in a slide deck.
Verticals we've shipped AI into.
HR & staffing platforms
An AI sourcing agent that turns one English sentence into a search across millions of candidates — in production inside a hiring platform.
Sports analytics & media
Fan video generation that cut processing from 3 hours to 30 minutes. The client was later acquired by Sports Info Solutions.
AgroTech
Crop analytics and field-level ML for agriculture platforms — models that work with noisy, seasonal, real-world data.
Voice agents at scale
Customer-facing phone calls end to end: scheduling, reminders, language switching — thousands of calls a day for a US client (industry under NDA).
HPC & scientific computing
Performance engineering for compute-heavy workloads — the background that keeps our inference costs sane at scale.
Systems our clients run their businesses on.
AI sourcing agent
Finds the right candidate in millions from one English sentence. Runs in production inside a hiring platform used daily by recruiters.
Read the case →Sports fan video generation
Video processing cut from 3 hours to 30 minutes — roughly 90% of the manual labor cost removed.
All case studies →Two of our long-term clients were acquired — their products, with our engineering inside, passed technical due diligence on the way.
Start small. Scale when the work earns it.
- 01
Pilot — $30–50K fixed
2 to 4 months. One hard, valuable slice of your product, shipped to production against a written scope. No obligation to continue.
- 02
Dedicated team — from $15K/mo
The custom AI development company that ran your pilot stays: the same 3–8 engineers, bi-weekly demos, 12–24 month partnerships. 30-day notice both ways.
- 03
Scale or hand over
The system grows with you. If you ever bring it in-house, we help you hire and transfer cleanly — code, models, and infrastructure are yours from day one.
Full engagement model, team tiers, and FAQ — how we work. Looking for narrower AI software development services? See AI chatbot development. Not sure what to build first? Start with AI consulting. Workflows that should run themselves — AI agent development.
Questions founders actually ask.
How much does custom AI development cost?
A typical AI development service engagement starts with a fixed-price pilot: $30–50K for 2–4 months of development — one production slice, scope agreed upfront. Ongoing dedicated teams run $15–65K/month depending on size (3 to 8 engineers). The main cost drivers are integration depth and how much of the system has to run on your own infrastructure.
How long does it take to build a custom AI product?
A production-grade first slice takes 2–4 months — that's what the pilot is. A demo takes days; the gap between the two is exactly where most AI projects fail. We ship to production from the first engagement, not after it.
We already vibe-coded a prototype. Is that useful?
Very. A working prototype is the best brief a project can start from — it shows us what you want far better than a spec. Bring it to the first call; the pilot turns it into something that survives real users.
Do you build custom AI models from scratch or fine-tune existing ones?
Almost never from scratch — that's research, and it's rarely the right spend for a product company. Most custom work is architecture and integration: retrieval over your data, fine-tuning open-weights models on your labels, classic ML where it beats an LLM on cost and latency. Training a foundation model from zero is the one thing we'll talk you out of.
What if our data is messy or there isn't much of it?
That's the normal starting point, not a disqualifier. Data audit and pipeline work is built into the pilot, and we've shipped models in scarce-data domains using labeling strategies and synthetic data. The honest version: if your data genuinely can't support the product yet, we say so in week two — not in month four.
Which models and stack do you use?
Whatever the problem needs — commercial LLMs, open-weights models, or classic ML. For sensitive data we run models on your infrastructure, so nothing leaves your environment. As an AI/ML development company we're model-agnostic by policy: the vendor landscape shifts every quarter, your product shouldn't depend on one bet.
Should we hire AI developers in-house instead?
Eventually, maybe — and we help clients do exactly that when the time comes. But most AI development companies won't tell you the honest math: assembling a senior in-house team takes about six months and $500K+/year before anything ships. Hire AI developers when AI is your forever-core; until then, a dedicated team gets you to production faster.
Who owns the code and IP?
You do, from day one. Standard mutual NDA at the start; code, models, data, and documentation are yours. We work as an extension of your team, not as a licensor.