AI Knowledge Base & RAG Development
Answers grounded in documents your team can find, check and update.
Discuss a projectThe scope
A useful knowledge assistant must find the right material before it writes an answer. I organize approved documents for search, connect retrieval to the answer flow and make the supporting sources visible. This is especially important when policies, product details or internal procedures change.
I treat access rights and freshness as part of the system. A user should not receive an answer based on a document they cannot open, and an outdated source should not silently outrank a current one.
When this helps
- Useful answers are scattered across many documents or systems
- Staff need an answer with a source they can inspect, not an unsupported summary
- Your current AI assistant misses updates or exposes information to the wrong audience
From process to production.
A useful AI system starts with a defined task, earns access to the right context, and proves itself on real work.
Discuss your workflowDefine the job
Map the trigger, inputs, decisions and exceptions. Agree on what the system may do and when a person takes over.
Working resultA focused, testable scopeConnect the context
Connect approved data and tools. Set access rules, handoffs and fallback paths around the workflow.
Working resultAn integrated working flowEvaluate in use
Test representative tasks, review errors, time and running costs, then improve the flow before and after launch.
Working resultA system you can inspect and refine
What I can deliver
An audit of the documents, ownership, permissions and update frequency
A retrieval pipeline with indexing, relevant excerpts and source references
Access checks so answers respect the rights of the requesting user
A test set for missing, conflicting and outdated information plus a refresh process
How I work
Inspect the sources
I review content quality, ownership, permissions and how often the documents change.
Build retrieval
I structure and index a small approved set, then connect retrieved excerpts to answer generation.
Evaluate and refresh
I test source selection and unsupported questions and define how new or removed documents take effect.
A knowledge-search prototype on one supplied document set
This price covers the starting scope described above. I confirm a quote after reviewing your brief and existing systems. Third-party fees are separate.
Send a brief on WhatsAppFor development work I deliver, I correct defects in the agreed scope reported within 90 days of launch. New features, changes by others and third-party outages are separate work.
Technology
- Document indexing
- Semantic search
- Retrieval-augmented generation
- Source citations
- Access control
- Evaluation sets
Frequently asked questions
Does RAG train a new model on our documents?
No. Retrieval-augmented generation finds relevant approved material when a question is asked and gives that material to an existing model. Model training would be a different scope.
Can users see documents they are not allowed to open?
The system should apply access rules before retrieving or showing a source. I verify the available permission data and test restricted examples before launch.
What if the documents disagree?
I define source priority and freshness rules with the content owner. The assistant should surface uncertainty or hand the question to a person when the conflict cannot be resolved.
Talk to me.
Tell me where your product stands. I'll give you a direct view of the options and what I can take on.