AI Knowledge BaseDevelopment
Gather the answers that live in PDFs, chats and people's heads into one searchable base that explains itself and shows where each answer came from.
AI Knowledge Base Development: what the work involves
Company knowledge sits in shared drives, old email threads, support tickets and the memory of whoever has been there longest. Keyword search fails because staff and customers rarely use the exact words in the document. New joiners ask the same colleague the same questions, and when that colleague leaves, the answer leaves with them.
Our approach starts with an inventory of sources and who owns each one. Content is ingested, deduplicated, split along headings rather than at arbitrary lengths, and stored with metadata such as owner, date and audience. Embeddings go into pgvector or Pinecone, combined with ordinary keyword search so exact product codes still match. A reranker orders the passages and a model writes the answer with citations. Stale documents are flagged by age, and gaps found in unanswered queries become a to-do list for the content owner. We evaluate on questions you supply before opening it to the wider team.
Core features
Source connectors
Pull content from Google Drive, Notion, Confluence, SharePoint, helpdesk tickets and website pages on a schedule.
Hybrid search
Semantic matching finds meaning while keyword matching catches exact codes, names and error messages, and the two are blended.
Cited, readable answers
Each response links to the passages it used, so the reader can verify it in a click.
Freshness tracking
Documents past their review date are marked, and superseded versions are down-ranked rather than left to confuse results.
Gap reports
Questions the base could not answer are collected so owners know exactly which article to write next.
Access-aware retrieval
Results respect who is asking, so HR files never appear in a general staff search.
What we get right before launch
Outdated or conflicting sources
Two policies that disagree produce a confident wrong answer. We surface conflicts at ingest, ask for an owner decision, and show document dates in every citation.
Permissions and confidentiality
A search layer can leak what a folder protected. We carry original access rules into the index and test with restricted accounts before launch.
Retrieval quality over model choice
Most poor answers come from poor chunking, not a weak model. We measure retrieval separately on your own question set and tune it first.
Tools and technology
- LlamaIndex
- pgvector
- Pinecone
- OpenAI
- Anthropic Claude
- Python
- FastAPI
- PostgreSQL
Common questions, answered
How is this different from a normal site search?
Ordinary search matches words. This understands the meaning of a question, pulls relevant passages from many files, and composes a short cited answer. You can still open the original documents whenever you want.
Where is our data stored?
In a database you control, hosted in the region you choose. If a hosted model API is used, we send only the retrieved passages, and can use a self-hosted model where policy requires it.
Can it include Urdu documents?
Yes. Embedding models handle Urdu script reasonably, though scanned Urdu needs careful OCR. We test retrieval on your real Urdu samples and report weak spots honestly.
Who keeps the content up to date?
Your team owns the content; we provide review reminders, change syncing and gap reports. The system cannot know a policy changed unless the source document does.
More AI Agents & Automation services
All AI Agents & Automation servicesInternal Knowledge Assistant Development
A private assistant for your own staff that answers HR, IT and process questions from your documents, inside the tools they already open every day.
AI Chatbot Development
A website chatbot that answers from your own documents, admits when it does not know, and hands the conversation to a person before the visitor gives up.
Custom GPT Development
A GPT configured for one job in your business, with your instructions, reference files and approved tools, rather than a blank chat window.
Ready to start your AI Knowledge Base Development project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.