Field Notes: Deploying a Private Knowledge Assistant for Engineering and IT Teams

(Anonymised case study)

Context

We worked with a mid-to-large organisation with a substantial engineering and IT function responsible for maintaining critical internal systems.

Over time, the organisation had accumulated a large volume of internal knowledge: technical documentation, architecture decisions, runbooks, tickets, post-incident reviews, and internal guidelines. This knowledge was spread across tools such as wikis, ticketing systems, and shared drives.

While the information existed, accessing it quickly and reliably had become increasingly difficult — particularly for newer team members or during time-critical situations.

Because of security, compliance, and intellectual property concerns, public cloud AI tools were not considered suitable. Any solution needed to operate within controlled infrastructure and respect existing access boundaries.

The Real Problem

The problem was not missing documentation.

It was knowledge friction.

Engineers spent significant time:

• searching for the right internal information

• interrupting colleagues for context

• rediscovering decisions that had already been made

• onboarding new team members slowly and unevenly

This created hidden costs: slower delivery, higher dependency on key individuals, and increased cognitive load across the team.

From a leadership perspective, this was an efficiency and resilience problem — not a tooling problem.

Constraints That Shaped the Design

Several constraints defined the solution space.

All internal documentation and system knowledge needed to remain private and controlled. Access to information had to mirror existing identity and permission models, ensuring that users only saw what they were authorised to see.

The system needed to integrate with existing tools rather than replace them, and it needed to behave predictably. Hallucinated or speculative answers were unacceptable in an engineering context.

Most importantly, the system needed to support engineers in real workflows, not introduce another platform to maintain.

What We Built

We designed and deployed a private AI knowledge assistant running entirely within controlled infrastructure.

The system used a retrieval-augmented approach to connect a language model to the organisation’s internal documentation and tooling. Engineers could ask questions in natural language and receive responses grounded in internal sources such as wikis, tickets, and runbooks.

Access to information was permission-aware, aligned with existing identity systems. Responses were designed to surface relevant context and references, rather than generate unverified conclusions.

The assistant was introduced as a support layer for day-to-day engineering work, including troubleshooting, onboarding, and navigating internal systems.

Design Considerations

Several principles guided the implementation.

Accuracy and grounding were prioritised over fluency. Engineers needed answers they could trust and verify, not eloquent speculation.

Permission boundaries were treated as first-class concerns, ensuring that the assistant respected existing organisational structures.

The system was deliberately scoped to remain assistive. It provided context and direction, but did not attempt to make decisions or take actions on behalf of users.

Outcome

The assistant reduced the time engineers spent searching for internal information and reduced reliance on informal knowledge sharing.

New team members were able to become productive more quickly, and experienced engineers spent less time answering repetitive questions. Internal documentation became more usable, without requiring large-scale rewrites or migrations.

From a leadership perspective, the system improved operational efficiency and reduced single-point-of-failure risk associated with institutional knowledge held by a small number of individuals.

Why This Matters

This deployment highlights a common pattern across engineering organisations.

As systems grow in complexity, the limiting factor is often not capability, but access to knowledge. Private AI systems that make internal information easier to use — without compromising security or control — provide immediate, measurable value.

For CTOs and CIOs, this kind of system is not an experiment. It is infrastructure.