AI agents and copilots
Assistants that execute real work inside your systems — draft, retrieve, classify, route, and act — with explicit approval gates on anything consequential.
AI Engineering
Enterprise AI engineering that ships to production.
Most AI projects die between the demo and the deployment. A pilot impresses the room, then stalls on data access, evaluation, permissions, cost, and the question nobody wants to own: what happens when it is wrong? We engineer for that question from the first week.
Enterprise AI engineering is the practice of designing, building, and operating production AI systems inside a business — not prototypes. Flowmoat builds AI agents, copilots, retrieval systems, and document intelligence that connect to real company data, run under human oversight, and hold up to enterprise security, audit, and reliability requirements.
The problem
Why AI pilots stall
The prototype works on clean sample data and collapses on the real thing.
Nobody can measure whether the output is good, so nobody will sign off on it.
The model can see documents the requesting user is not allowed to see.
Token cost at pilot scale is fine; at company scale it is a budget line nobody approved.
There is no fallback path when the model is unavailable, slow, or confidently wrong.
Capabilities
What we build
Retrieval and enterprise search (RAG)
Grounded answers over your own contracts, tickets, wikis, and records, with permission-aware retrieval so the model never surfaces what the user cannot already access.
Document intelligence
Extraction, classification, and validation across invoices, claims, policies, and contracts — with confidence thresholds that route uncertain cases to a human.
Evaluation and guardrails
Test sets, scoring, regression checks, and monitoring so you can prove the system is working and detect it when it stops.
Engagement
What you get
Every engagement ends with software your team owns and can operate without us.
A working system in your environment, not a slide deck
An evaluation harness with baselines you can hold us to
Permission-aware data access, documented
Cost-per-task modelling before you scale, not after
Handover documentation and a runbook your team can operate
Fit
Who this is for
Companies with 50–1,000 employees and real operational complexity
Teams sitting on years of documents, tickets, or records nobody can search
Leaders who need AI to survive a security review, not just a demo
FAQ
AI Engineering — common questions
A consultant produces a strategy and a recommendation. AI engineering produces a running system. Flowmoat writes the code, connects the data, builds the evaluation harness, and hands over software your team can operate.
Most engagements deliver a working system in 6 to 12 weeks. We ship in focused iterations with regular demonstrations rather than disappearing for a quarter and returning with a finished product.
We stay model-agnostic and select based on the task, your data-residency requirements, and cost per task. We architect so the model layer can be swapped without rewriting the system around it.
We ground answers in your own data, score outputs against a test set before launch, set confidence thresholds that route uncertain cases to a human, and monitor for regressions after deployment.
Related
Often delivered alongside
Start with the right roadmap