SYSTEM · 3–5 WEEKS · FIXED FEE
Stop hunting for information. Build an AI that already knows where it lives.
We build a retrieval-augmented AI knowledge system over your existing documentation and internal knowledge — company wiki, SOPs, product specs, support history. Your team asks questions in plain language. The system answers from your actual content.
WHAT IT IS
Your company's knowledge. Suddenly searchable by anyone.
Most companies have more knowledge than they can use. SOPs that live in someone's head. Product specs buried in a folder no one remembers. Support answers that get rewritten from scratch every week because the good version is in a Slack thread from 2021.
A knowledge base AI layers a retrieval system over the documents you already have. Staff ask natural-language questions. The system finds the relevant sections, synthesizes an answer, and cites its sources. It doesn't hallucinate what it doesn't know — it says it doesn't know. Reliability is a design constraint, not an afterthought.
The output is a system your team can query through Slack, a web interface, or directly via API — whichever fits your workflow. We build it, connect it to your doc sources, train the indexing pipeline, and hand it off. You own and operate it from day one.
WHO IT'S FOR
Teams where knowledge is scattered but real.
Right fit if…
- Your team spends 30+ minutes a day hunting for internal information
- Onboarding new staff requires weeks of shadowing — the knowledge isn't written down
- Your support team keeps answering the same questions from scratch
- You have documentation but it's spread across Notion, Drive, Confluence, SharePoint
Probably not a fit if…
- You have almost no documentation yet — document first, then build the search layer
- Your knowledge is highly sensitive and can't be sent to any cloud service (discuss on discovery call — on-premise options exist)
- You need a broad multi-system knowledge graph — too large for a single Sprint
WHAT'S INCLUDED
Data ingestion to production interface.
Source connector setup
Ingestion pipelines for your existing document stores — Notion, Google Drive, Confluence, SharePoint, or local file sources.
Vector database & indexing pipeline
Chunking strategy, embedding model selection, and vector store (Pinecone, Weaviate, or pgvector depending on your infra preference).
RAG pipeline engineering
Retrieval-augmented generation pipeline with source citation, confidence handling, and "I don't know" behavior when coverage is insufficient.
Query interface
Slack bot, web UI, or API endpoint — whichever fits how your team already works. Not a new tool to learn, a layer on what you use.
Refresh scheduling
Automated re-indexing pipeline so the system stays current as your documentation evolves. No manual re-runs required.
Documentation & handoff
Full technical runbook, admin guide, and team training session. Your staff can add doc sources, retrain the index, and manage the system without us.
PROCESS
Three to five weeks, depending on source complexity.
Week 1
Audit & scope lock
Inventory your doc sources, assess quality and coverage, agree on connectors and interface type.
Weeks 2–3
Ingestion & indexing
Build connectors, run initial ingestion, tune chunking and embedding strategy. Validate retrieval quality on real questions.
Week 4
Interface & integration
Build and wire the query interface. Set up refresh pipeline. Run end-to-end testing with your team's real questions.
Week 5
Handoff & training
Go live, train admins and daily users, transfer all assets and documentation.
OUTCOME
Questions answered in seconds, not hours.
Teams using internal knowledge base AI consistently report the biggest impact on onboarding and support. New hires who used to spend weeks shadowing senior staff to absorb tacit knowledge start being productive in days — because the knowledge is now queryable.
Support and operations teams stop rewriting answers they've written before. The system finds the existing good answer, cites where it came from, and presents it. If the answer doesn't exist yet, the system says so instead of fabricating something plausible-sounding.
The directional estimate: for a team that handles 15–30 internal information queries per day, a well-tuned knowledge base typically saves 1–2 hours of search and synthesis time daily. At that rate, the system pays for the engagement within the first quarter.
FAQ
Common questions.
What if our docs are a mess?
Common situation. We'll document the coverage gaps and quality issues in the Week 1 audit, and scope only what makes sense to index. Garbage in, garbage out — but we won't pretend otherwise.
Will it hallucinate?
Designed not to. RAG systems with source attribution and explicit "I don't know" behavior hallucinate far less than vanilla LLM chat. We test this explicitly before handoff.
What about data privacy?
We sign NDAs before any engagement. We scope the data handling approach in Week 1. For regulated industries or sensitive content, we can discuss on-premise or private cloud deployment.
Which LLM do you use?
Depends on your content, budget, and data sensitivity requirements. We'll recommend during scoping — usually OpenAI or Anthropic for cloud, Mistral or Llama for on-premise.
How do we keep it updated?
We build a scheduled re-indexing pipeline as part of delivery. You update the source documents as you normally would — the system picks up changes automatically.
What's the cost?
Flat fee, scoped after the discovery call based on number of source connectors and complexity of the interface. Plus ongoing LLM API costs (billed directly to you, typically modest).