Key takeaways
- An internal assistant answers “how do we do this here,” not “what does the internet think.”
- Start with procedures, templates, and approved past work. Leave out half-finished drafts and personal files.
- Access, exceptions, and a human owner matter more than the model name.
What the assistant is for
In a professional services firm, people lose hours looking for the same answers: the current engagement letter, the pricing exception, the last similar proposal, the onboarding checklist, the way this firm writes a status update. An internal AI knowledge assistant is a secure way to ask those questions and get the firm’s answer, not a generic one.
It is not a public chatbot. It is not a substitute for professional review. It is a retrieval and drafting aid sitting on top of material the firm already trusts.
What belongs in it
- Current procedures and checklists.
- Approved templates and sample work the firm is willing to reuse.
- Service descriptions, pricing rules, and escalation paths.
- Past work that has been designated as a reference, not every file on the shared drive.
What stays out: anything a client would not expect a junior employee to see, unfinished drafts, and sources no one will maintain. If a document is wrong in the assistant, the assistant will be confidently wrong at scale.
How to build it without a science project
Name an owner. Choose one department or matter type. Collect twenty to fifty documents that already answer the questions people ask. Decide who can use it and what it must refuse. Test with the people who currently get interrupted. Measure two things: time to find an answer, and how often the answer is accepted without a correction.
Then expand. The failure mode is loading the entire file share on day one and wondering why nobody trusts it.
TechStack installs this as part of AI for professional services — alongside client agents, document workflows, and training — so the assistant is connected to how the firm actually works.
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