Enterprise LLM Integration
Connect ChatGPT Enterprise, Microsoft Copilot or custom models safely to your identity, data and processes. With SSO, logging and clear data flows.
Safely move ChatGPT Enterprise, Copilot and custom assistants into operations. With retrieval, guardrails and evaluation. Productive over prototype, reliable over impressive.
The step from impressive demo to reliable assistant decides adoption. Without retrieval, guardrails and measurement every LLM remains a black box.
Almost every mid-market company now runs at least one GenAI pilot. The question is no longer whether, but how a pilot becomes a productive tool that business and audit both accept.
We integrate enterprise models, build retrieval augmented assistants on your knowledge base and anchor prompt standards, guardrails and quality measurement. The result: assistants that deliver, not just impress.
Four priorities that interlock in every GenAI mandate. From integration to quality in operations.
Connect ChatGPT Enterprise, Microsoft Copilot or custom models safely to your identity, data and processes. With SSO, logging and clear data flows.
Your own knowledge base as the foundation. Assistants answer from your documents, contracts or processes, with citations and traceable reasoning.
Reusable prompt templates, role profiles and versioning. Prompts move from craft object to controlled operational resource.
Input and output filters, policy checks and continuous quality measurement. Model swaps become configuration, not risk.
Six to twelve weeks depending on integration depth. We work with IT, business and audit in parallel, not sequentially.
Sharpen use cases, review data and identities, decide model and architecture. Output: target picture, risk assessment and concrete delivery path.
Integration, retrieval setup, prompt standards and guardrails. Assistants tested against an evaluation set, not gut feel. Output: production ready assistant.
Monitoring, quality measurement and handover. Your teams maintain prompts, refresh sources and stand up new assistants independently. Output: sustainable operations.
Assistants that work measurably, and an organization that creates new use cases on its own.
Assistants with defined interfaces, roles and monitoring. No shadow tool, but a documented operational standard.
Input and output controlled, policies enforced, audit trail in place. Acceptance by audit and data protection.
Every statement with provenance. Hallucinations become rare, correction becomes possible.
New assistants ride the same platform with the same standards. Growth without sprawl.
30 minutes. Initial assessment of use case, data foundation and integration path. No commitment.