Enterprise AI, where the hard part is what the model is allowed to see
Enterprise AI pilots rarely fail on model quality. They fail at the point where a system that answered questions from a curated sample has to answer them from the real corpus, with the real permissions attached.
What changes here
Artificial Intelligence in enterprise is not the same engagement
Permissions travel with the data, or the pilot cannot ship
A retrieval system that ignores document ACLs will happily quote a compensation review to the person it is about. Access has to be enforced at retrieval time against your identity provider, not filtered afterwards in the response.
Evaluation before rollout, on your own questions
A benchmark score says nothing about whether the system answers the questions your staff actually ask. We build an evaluation set from real queries and hold it fixed, so a prompt change that helps one case and breaks four is visible before release.
A path back when it is wrong
Every answer carries its sources, and there is a route for a user to say it was wrong that lands somewhere a human reads. Systems without that feedback path degrade silently, because nobody finds out.
What governance actually asks of an AI system
Enterprise AI review is less about the model than about the data path around it. These are the questions that decide whether a pilot is allowed into production.
- Where inference runs, and whether prompts or outputs leave your tenancy
- Whether the vendor may train on your data, stated in the contract rather than the marketing
- Retention of prompts and responses, and whether they fall under existing records policy
- Document-level access control enforced at retrieval, verified against your identity provider
- An audit trail linking an answer to the sources and the user who asked
The work itself
Full artificial intelligence pageLLM applications and RAG over your own data
Retrieval-augmented systems on your documents, tickets, and records — chunking, embeddings, a vector store, and citations, so answers can be traced back to a source.
Machine learning models and forecasting
Classification, scoring, and demand or churn forecasting trained on your history, with feature pipelines and retraining scheduled rather than remembered.
Document, voice, and workflow automation
Intake, extraction, classification, and summarization wired into the systems the work already lives in, with a human review step where the cost of being wrong is high.
Evaluation sets and regression testing
A scored test set built with your subject-matter experts and run on every change, so a prompt or model swap has to prove it improved things.
Guardrails, grounding, and audit trails
Input and output filtering, retrieval grounding, refusal paths, and logged prompts and responses — the record you need when someone asks why it said that.
Latency, token cost, and model routing
Caching, batching, and routing cheap work to smaller models, with spend and p95 latency on a dashboard rather than a surprise invoice.
Enterprise questions we get asked
Something more specific? Send us the situation and we’ll answer it straight.