Internal knowledge assistant
One place to ask about policies, products and past decisions. It searches every source your team uses.
RAG and Knowledge
RAG development that gives answers you can trust.
We turn your documents, tickets and wikis into an AI that answers questions with sources. Your team stops searching. Your customers get answers that match your docs, not a guess.
- pgvector
- Pinecone
- Weaviate
- Elasticsearch
- LlamaIndex
- Hybrid search
- Rerankers
What is RAG?
RAG stands for retrieval augmented generation. Before the AI answers, it searches your own content and pulls the most relevant passages. The model then writes an answer from those passages and shows where each fact came from. This keeps answers current and grounded in your data.
Signs you are ready
Sounds familiar?
- People ask the same questions in Slack every week.
- Your knowledge lives in Notion, Drive, Confluence and old tickets.
- Search returns ten documents when you wanted one answer.
- A generic chatbot made things up about your product.
Use cases
What we build.
Customer help center AI
Answers on your site and in your app, grounded in your docs. Each answer links to the page it used.
Support copilot
Suggests replies to support staff using past tickets and the knowledge base. New hires ramp up faster.
Sales and RFP assistant
Drafts answers to security reviews and RFPs from your library of approved answers.
Document Q and A
Ask questions over contracts, reports or manuals. Get the exact clause or page back.
Deliverables
What you get.
Everything ships in your repo and your cloud. You own all of it.
- 01
Data pipelines
Connectors that sync your sources and keep the index fresh as content changes.
- 02
Search that finds the right passage
Hybrid search, smart chunking and rerankers tuned on your real questions.
- 03
Answers with citations
Every answer links to its source so people can check it.
- 04
Access controls
People only get answers from documents they are allowed to read.
- 05
Quality scores
A test set of real questions. We measure accuracy before and after every change.
How we work
Our approach.
Every project runs on the Zap Sprint. A fixed price, a working prototype in about 14 days and a production launch in four to six weeks.
- 01
Audit your content
We list your sources, find gaps and agree what the AI should and should not know.
- 02
Build the index
We clean, split and embed your content. Permissions stay attached to every piece.
- 03
Tune retrieval
We test with real questions from your team until the right passage shows up first.
- 04
Ship and measure
We launch in Slack, your app or your site. We track which answers help and which miss.
By industry
Where it works.
FAQ
RAG and Knowledge FAQ
Still curious? Email us at hello@zappient.com. A real human replies within a day.
For most company knowledge, yes. RAG keeps answers current because it reads your latest docs at question time. Fine tuning changes how a model writes, not what it knows. Sometimes we combine both.
RAG greatly reduces this. The model answers only from retrieved passages and must cite them. If it cannot find an answer, it says so. We test this on real questions before launch.
Most sources work. Google Drive, SharePoint, Confluence, Notion, Zendesk, Intercom, websites, PDFs and databases. If it has an API or an export, we can usually index it.
No. We can host the vector database and the models inside your own AWS, Azure or Google Cloud account.
We build a test set of real questions with known good answers. We score retrieval and answers on every change. You see the scores in a dashboard.
Related services
Pairs well with.
Got a messy process?
Let’s zap it.
Tell us what slows your team down. We will show you what an agent could do about it. The first call is free.
- 01Tell us about the job in two minutes
- 02A senior engineer replies within 24 hours
- 03Get a plan and a fixed quote after one call