AI for Technology

In technology companies knowledge is lost to scatter, not absence. Architecture decision records live in a repo, runbooks in a wiki, postmortems in a separate folder, API contracts inside the code. The answer to a new engineer's "why was this service designed this way" usually exists in writing — nobody knows where.

The assistant collects that scattered archive under a single query surface. Because every answer shows which section of which document it came from, an engineer can open the source and read the full context. It does not replace making documentation readable, but it lowers the cost of finding it.

For an on-call engineer the problem is rarely missing knowledge — it is finding the right runbook under pressure. The assistant answers "what is the first step when this alarm fires" with a citation from the relevant runbook. If past incident reports are attached too, the same query surfaces how a similar incident was resolved before.

One boundary matters here: the assistant does not act on production systems, it relays documents. Do not expect automated remediation; the engineer decides. Because generation is RAG-based, an out-of-date runbook produces an out-of-date answer.

Separating assistant scope matters especially in technology teams. Customer-facing product documentation and internal architecture decisions should not sit in the same assistant; support reaches the first, engineering reaches both. Role-based access and per-assistant document scope provide that separation.

Document volume grows fast in technology companies. The 12500-document, 25-assistant limit on the Pro plan supports the practice of running a separate assistant per product. Each company's content is indexed in its own vector collection; one company's document never appears as a source in another company's query.