AI Applications & Knowledge

Enterprise Knowledge AI

Somewhere in SharePoint, Confluence, a shared drive and eleven years of email, the answer to most internal questions already exists. It is not findable, so people ask a colleague instead, and the colleague guesses. We build assistants that retrieve from your own content, cite what they used, and respect the permissions already set on the source.

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The business problem

Search stopped working at about ten thousand documents

Keyword search fails on internal content because nobody writes documents using the words other people search for. The result is an organization where the cost of finding something exceeds the cost of asking a person, so everyone asks a person. That tax is invisible on any budget line, which is why it never gets fixed, and it grows with headcount. The naive fix, pointing a chatbot at a document dump, fails differently and worse: it answers confidently from an outdated policy, or it surfaces a salary review to somebody who should never have seen it.

What we do

Retrieval that respects who is asking

We connect to the systems that hold the content rather than copying it somewhere new, so permissions stay where they were set and revocation still works. Retrieval is filtered by the asking user's identity before the model sees anything, which means the assistant cannot reveal what that person could not already open. Every answer carries citations to the documents it used, so a reader can check rather than trust. Accuracy is measured against a labelled set built from real questions your staff have actually asked.

Enterprise Knowledge AI

Capabilities

Enterprise AI Search
Internal Knowledge Assistants
Semantic Enterprise Search
AI Document Search
Internal Copilots

Common use cases

Answer policy and procedure questions for front-line staff, citing the controlling document and its version.
Give new joiners a way to ask what a system does without booking time with the person who built it.
Surface prior work on a topic across projects, so proposals stop being written from scratch.
Let support agents query product documentation and past tickets during a live conversation.

How we deliver

Shape

Turn the request into a specification: who uses it, what a correct answer is, who decides.
Ground

Connect to the content and systems that hold the answers, respecting existing permissions.
Evaluate

Score against a labelled set built from your own cases, before anyone outside sees it.
Ship & operate

Staged release with monitoring, cost control and a regression suite that guards quality.

Technology

OpenAI
Anthropic
Azure OpenAI
pgvector
Elasticsearch
Microsoft 365
SharePoint
Confluence
Salesforce

Security & governance

Answers are grounded in your own content and carry citations, so a reader can check them. Retrieval respects the permissions already set on the source, which means a user never sees through the application what they could not see directly. Prompts, retrieved context and responses are logged for audit, and evaluation runs continuously rather than once at launch.

Engagement models

AI Project

We take responsibility for designing and delivering a defined AI solution.

Dedicated AI Team

Long-term dedicated engineering capacity built around your stack and delivery model.

Managed AI

We operate, monitor and continuously improve production AI systems.

Why TeamExtension.ai

The hard part is permissions, not retrieval

Building something that answers questions from documents takes a weekend. Building something that answers correctly, cites its sources, never leaks across permission boundaries, stays accurate as content changes, and can be audited afterwards is an integration and security problem. That is ordinary enterprise engineering, which is what we have been doing for years, and it is the reason most internal assistants stall before they reach the whole company.

Selected clients

Frequently asked questions

Will it expose documents people should not see?

No. Retrieval is filtered by the asking user's identity before anything reaches the model, using the permissions already set in the source system. If a person cannot open a file directly, the assistant cannot read it on their behalf. Revoking access in the source revokes it here.

What happens when a document is updated or withdrawn?

The index follows the source. Updated documents are re-indexed on a schedule or on change notification, and withdrawn documents disappear from answers. Because answers cite their sources, a reader can always see which version an answer came from.

How do you stop it inventing answers?

Answers are grounded in retrieved passages and cite them. Where retrieval finds nothing relevant, the assistant says so rather than filling the gap. We measure this explicitly against a labelled set, because an assistant that is confidently wrong is worse than no assistant.

Which systems can it connect to?

SharePoint, Microsoft 365, Google Workspace, Confluence, Jira, ServiceNow, Salesforce, file shares, databases and internal APIs. Anything with an API or a database can be a source; where there is no API we build the connector.

How long before staff can use it?

A bounded pilot over one content set and one user group typically runs six to eight weeks, including the evaluation work. Widening to further sources is incremental after that, because the permission and citation model is already built.

Related capabilities

AI Applications & Knowledge

RAG Development

Retrieval systems that ground answers in your own content, with citations and measurable accuracy.

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Agentic AI & Automation

AI Agents

Software that completes multi-step work inside your systems, under your access controls and your approval rules.

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AI Applications & Knowledge

Conversational AI

Customer and employee assistants with controlled scope, grounded answers and clean handoff to people.

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Discuss Your AI Initiative

Turn fragmented internal knowledge into secure assistance that respects existing permissions.

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