Production AI/ML for Regulated Industries
Inference infrastructure, LLM integration with proper guardrails, multi-model orchestration, and the cost discipline that makes AI features economically viable. For teams shipping AI under finance, healthcare, or compliance constraints.
When to call us
Models are stuck in research
Your data science team has working models but nothing makes it to production — or the things in production are bespoke notebooks no one wants to touch.
Adding LLM features under regulatory pressure
You want to ship LLM-powered features but compliance, hallucination risk, and citation requirements are blocking. You need a real production pattern, not a demo.
Inference cost is out of control
GPU bills, vendor model costs, or always-on inference clusters are eating your margin. The compute does not scale with revenue the way it should.
Model governance is missing
No documented decisions on model selection, no monitoring for drift, no audit trail for an AI Bill of Rights or NYDFS Part 500 ask. Auditors are noticing.
What you actually get
- Production ML infrastructure: training pipeline, inference layer, monitoring, drift detection, retraining cadence
- LLM integration patterns with grounded retrieval, citation, evaluation harnesses, and proper handling of refusals and hallucinations
- Multi-model orchestration: routing across providers (OpenAI, Anthropic, Vertex, open-source) by task type, cost, and context window
- Cost optimization: batch vs real-time, GPU rightsizing, caching strategies, prompt-level cost telemetry
- Model governance: versioning, evaluation rubrics, decision documentation, drift and bias monitoring with paper trails
- Risk and fraud model integration with sub-second decisioning at the transaction edge
Named proof
Engagements where we shipped ML/AI to production, not just to a demo.
AI Trader
AnonymizedBuilt a multi-model agentic platform orchestrating four LLM providers with FDA pipeline shipped in 2 days. 3x research throughput, 24/7 unattended ops.
Latent Sciences
Boston, MAServerless ML infra connecting experiments to on-demand GPUs. Cut idle infrastructure cost 98%, 4x GPU utilization, unlimited concurrent pipelines.
Global Advertising Leader
AnonymizedBuilt a knowledge graph and AI platform replacing weeks of manual audience research with instant AI-driven targeting. 28,000+ audience segments, 9+ markets.
How we work
30-min discovery call
Where you are with AI/ML, what is blocking, what compliance asks are coming. Honest read on whether the bottleneck is engineering, model quality, or organizational.
Architecture & cost assessment
2–3 weeks. We review the current ML/LLM stack, instrument cost and quality, and produce a written plan with prioritized changes. Output addresses both shipping speed and unit economics.
Embedded build
We pair on the highest-leverage components — usually the inference layer, the evaluation harness, and the cost telemetry. Through the next 1–2 production launches.
Frequently asked
Are you a "Build my AI agent" shop?
No. We build production ML/AI for companies that already have data and a real product. The work is much closer to platform engineering, MLOps, and disciplined inference engineering than it is to demo-quality agent prototypes.
How do you handle hallucinations and risk for finance use cases?
Grounded retrieval with hard citation requirements, output schema validation, evaluation harnesses that run against held-out cases on every model swap, and clear human-in-the-loop checkpoints for any decision touching money or regulated content. We design for refusal and uncertainty, not just happy path.
Which model providers do you work with?
OpenAI, Anthropic, Google Vertex (Gemini), Mistral, and open-source models on local or self-hosted infra. The right choice usually depends on task, cost, latency, and what your data residency rules allow. We orchestrate across providers when it makes sense.
Do you do training from scratch?
Rarely. Most production AI/ML wins come from better infrastructure, retrieval, evaluation, and routing — not from training new models. If you have a genuine case for fine-tuning or training from scratch, we will tell you, but we will also tell you when it is not worth the cost.
How fast can we ship an LLM feature?
4–8 weeks for a well-scoped feature from kickoff to production, including evaluation harness and monitoring. Faster is possible at the cost of governance — which is fine for an internal tool, less fine for a customer-facing finance product.
Ready to talk?
30 minutes, free, no pitch. We will tell you honestly whether what you want to build is feasible right now and at what cost.