techvitRAG / AI KNOWLEDGE SEARCH
Turn internal documents into answers your team can verify.
I design and build RAG systems that search internal PDFs, manuals, FAQs, proposals, and meeting notes, answer with citations, and improve through retrieval and answer evaluation.
You can contact me before your requirements are finalized.
AIUnused items can be returned within 14 days of delivery when all original accessories are included.
Source 1
Return Policy.pdf · Page 4
Demonstration with fictional data. The interface and fields are designed for each project.
COMMON PROBLEMS
A chatbot is not enough for business knowledge.
Important information is buried across shared folders and document systems.
Teams repeatedly answer the same internal or customer questions.
A generic chatbot answer cannot be verified against company documents.
There is no reliable way to evaluate whether the AI is answering correctly.
A RAG proof of concept works, but its retrieval quality does not improve.
THE DIFFERENCE
Move beyond a PoC to measurable RAG quality.
Typical RAG PoC
A working answer screen does not reveal why a case failed or what should be changed.
TechVit
Retrieval, answers, and citations are evaluated so each change can be compared against a baseline.
TECHNICAL EVIDENCE
ForgeAI evaluation foundation
ForgeAI is the open-source foundation I am developing to test whether a RAG system retrieves the right documents and produces answers grounded in those sources.
ForgeAI is in alpha development. Customer-specific access control and production operations are designed separately for each engagement.
Review the implementation on GitHub →
Retrieval evaluation
Measure whether expected evidence appears using Recall@K, Precision@K, MRR, and Hit Rate.
Answer evaluation
Review correctness and relevance for each evaluation question.
Groundedness
Check whether the answer is supported by the retrieved evidence.
Citations
Return sources so users can review the original material.
Regression detection
Compare runs to find improvements and newly failing cases.
Trace
Record retrieval and answer latency, tokens, cost, and status.
Try RAG with your own document structure.
Start with the source types and representative questions your users need answered.
PROCESS
From discovery to ongoing evaluation
- 01
Discovery
- 02
Source review
- 03
PoC build
- 04
Evaluation dataset
- 05
Quality evaluation
- 06
Improvement
- 07
Production rollout
- 08
Ongoing evaluation
RAG POC
Start with a two-to-four-week PoC.
Included
- ✓Document ingestion
- ✓Retrieval
- ✓AI-generated answers
- ✓Source citations
- ✓Baseline evaluation with a Golden Dataset
Cost
Estimated for your requirements
The estimate depends on document volume and formats, integrations, authentication and permissions, evaluation scope, models, and infrastructure requirements.
Technology selected for the requirements
FAQ
Frequently asked questions
What document types can be searched?＋
Typical sources include PDFs, FAQs, manuals, proposals, project files, meeting notes, and internal documentation. I review the formats and data quality before selecting an ingestion approach.
Can you improve an existing RAG system?＋
Yes. I turn representative questions and expected evidence into an evaluation dataset, then isolate whether failures come from retrieval or answer generation.
Can a proof of concept be taken into production?＋
Yes. Authentication, permissions, source updates, monitoring, and infrastructure are designed from the PoC findings and the organization’s requirements.
New RAG systems and existing quality problems are both welcome.
I can help with a new PoC, a system that retrieves the wrong sources, or a team that has not yet defined its evaluation method.