Miniml is an enterprise AI consulting firm built for the second half of an AI project — the half where it has to actually run.
We started Miniml because we kept seeing the same pattern across enterprise AI buyers in 2025. The pilots worked. The models hit accuracy targets. The slide decks landed. And then nothing shipped. Or something shipped and it didn’t survive contact with real data, real operators, and real regulatory review.
The first half of an AI project — proving a model can do something — has commoditized. The frontier models from Anthropic, OpenAI, Google, and the open-weights cohort have closed the capability gap fast. The hard half now is the operating half: standing up an AI system inside an enterprise’s existing infrastructure, with auditable behavior, integrated data pipelines, and governance the business can defend.
That’s what we do.
What we build
Miniml works in six expertise areas, each chosen because it’s where AI projects most often fail to make the production leap.
AI Agents. Bounded autonomous workflows operating inside enterprise systems with rate controls, audit logs, and exception routing. Not full autonomy. Not chat assistants. Agents that finish bounded tasks operators can supervise.
Knowledge & Retrieval. Production RAG systems with hybrid search, cross-encoder reranking, grounded answers with citations, and evaluation harnesses that catch drift before users do.
Document Intelligence. VLM-plus-schema extraction pipelines for contracts, claims, KYC, and regulatory documents. Bounding-box citation back to source. Verification agent cross-checking fields.
AI Strategy. Roadmaps and architecture decisions that get a buyer past the “should we” stage to a deployment plan their CTO and CFO both sign.
MLOps & AI Infrastructure. The converged stack — gateway, observability, evals, vector, runtime — that turns one model into a platform.
AI Governance. Audit-grade tracing, per-model cost attribution, regulator-ready documentation, lifecycle management for the EU AI Act and equivalent regimes.
How we work
Three things are true of every engagement.
Senior teams from day one. No junior pyramid. The people who design the system are the people who build it and the people who operate it. The senior consultant in the kickoff is the same person you message at 11pm when a production model drifts.
Fixed scope, fixed deliverables. Every engagement starts with a written scope, a written success metric, and a written deliverable. No open-ended discovery calls compounding into a portfolio of nothing.
Built to own. What we ship is documented, testable, and handed over. Your team runs it after we leave. Knowledge transfer is part of every engagement, not an afterthought.
Where we work
Healthcare. Finance. Retail. Manufacturing. Logistics. Insurance. Fintech. Different regulatory regimes, different operational realities, same engineering discipline. The system has to work inside the constraints your business actually operates under — residency, data classification, latency, compliance — not the constraints of a sandbox.
Who we are
The Miniml team is built from research and operations in roughly equal measure. We come from the University of Edinburgh, Amazon, Stanford, the Alan Turing Institute, ELLIS, and GE. We’ve trained models, shipped production systems, written compliance documentation, sat across the table from regulators, and operated platforms at enterprise scale. That mix is the bet. AI projects that ship and stay shipped are built by teams that can do all of those things, not just one.
The bet
The next decade of enterprise AI won’t be won by the buyer with the most pilots. It’ll be won by the buyer who put a small number of governed workflows into production, ran them at scale, instrumented the operating layer, and iterated.
That’s where we work.
If you’re trying to figure out which of your AI workflows is the right wedge, or what the operating scaffold needs to look like for your environment, book a consultation. 30 minutes with a senior consultant who can tell you what’s genuinely buildable today and what it would take to ship.