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Enterprise assistants & private AI

Put your organisation’s knowledge to work.

Build permission-aware enterprise AI assistants using approved company knowledge, source references and clear escalation.

Illustration of a team exploring AI on a computer

What we build

An enterprise assistant should know which sources it may use and when it should ask for help. We connect authorised documents and systems, preserve access boundaries and make the evidence behind an answer visible. Private deployment options are assessed against actual data and operating requirements.

  • Knowledge preparation and retrieval architecture
  • Document permissions and source attribution
  • Private, cloud or hybrid deployment assessment
  • Answer evaluation, escalation and feedback workflows

Knowledge across teams and languages

An India-wide operation may have English policies, regional-language conversations and documents owned by different branches. We scope the first assistant around a defined set of approved sources, test code-mixed questions where required and preserve the permissions of the source systems.

Start with the right foundations

Approved documents, ownership and version information, user roles, representative questions and an agreed hosting boundary.

Measure what changes

Evaluate grounded answers, retrieval relevance, outdated references, access isolation and the usefulness of escalation—not simply the number of chats.

From a defined scope to a working service

We agree the workflow, operating responsibilities and acceptance criteria first. A focused implementation validates the important assumptions before broader integration, rollout and handover. Ongoing support is scoped around the service and the team using it.

Common questions

What does a enterprise assistants & private ai engagement begin with?

Approved documents, ownership and version information, user roles, representative questions and an agreed hosting boundary. We use discovery to turn these inputs into a defined scope and acceptance plan.

How do you evaluate the result?

Evaluate grounded answers, retrieval relevance, outdated references, access isolation and the usefulness of escalation—not simply the number of chats.

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