The Challenge

As companies grow, tribal knowledge spreads across wikis, PDFs, shared drives, and chat history. New hires ask the same onboarding questions. Ops teams re-answer policy questions. Engineers dig for the "right" runbook version. Search that returns ten documents is not the same as an answer.

An internal knowledge assistant solves this by retrieving the relevant passages and generating a grounded response — with links back to the source — while respecting who is allowed to see what.

Signs You Need This

  • New employees take weeks to find "where things live"
  • The same questions keep hitting Slack/Teams channels
  • Policies and SOPs exist but nobody trusts search to surface the latest version
  • You have sensitive docs that can't go into a public ChatGPT-style tool
  • Support or HR teams are overloaded with internal how-to requests

Our Approach

We inventory knowledge sources, define ownership and refresh cadence, then build a RAG pipeline: chunking, embeddings, vector storage, retrieval quality tuning, and an answer layer with citations. Access control mirrors your existing identity model (SSO/RBAC) so finance docs don't leak to the whole company.

Security is part of the design: no training on your private data without approval, prompt-injection defenses, logging of queries/responses for audit, and optional private model hosting (AWS Bedrock, Azure AI, VPC deployments) when compliance requires it.

What We Deliver

  • RAG assistant connected to approved sources (Confluence, Notion, Drive, SharePoint, Git, PDFs)
  • Cited answers with source links
  • SSO and role-based document access
  • Admin tools for re-indexing, feedback, and blocked topics
  • Monitoring for retrieval quality and failed queries
  • Deployment options: SaaS LLM APIs or private/cloud-hosted models

Timeline & What to Expect

Week 1 Source audit, access model, success metrics, architecture choice
Week 2 Ingestion pipeline, chunking strategy, prototype over a pilot corpus
Week 3 SSO/RBAC, UI (Slack/Teams/web), evaluation set and quality tuning
Week 4 Pilot rollout to one department, feedback loop, production hardening

Frequently Asked Questions

Do our documents get used to train the model?

No — by default we use retrieval + generation without fine-tuning on your corpus. If you later want fine-tuning or private hosting, we scope that separately with clear data-handling terms.

What if documents are outdated?

We set ownership and refresh jobs, surface last-updated metadata in answers, and add feedback buttons so wrong answers get routed to doc owners.

Can this live inside Slack or Teams?

Yes. Most teams start there for adoption, with a web UI for longer research sessions.

When This Is the Right Fit

Ideal when you have a real document corpus and repeated internal questions. Less useful if knowledge is almost entirely tribal and undocumented — in that case we pair the assistant build with a lightweight documentation sprint first.