Policy and procedure assistant
Index versioned policies, return cited answers, and flag when a user question requires human escalation or newer source material.
RAG system development
Retrieval systems that answer from the right knowledge, not just plausible text.
GTA Studios designs and builds retrieval-augmented generation systems with document pipelines, search relevance, grounding, citations, permissions, and evaluation loops treated as first-class product concerns.
- Data
- Docs, tickets, policies, records, and product data
- Quality
- Retrieval evals, citations, and review loops
- Output
- Grounded answers and maintainable pipelines
Where this helps
When internal knowledge is valuable but hard to retrieve safely.
A useful RAG system depends on ingestion quality, chunk strategy, metadata, access rules, ranking, answer formatting, and observable feedback. The goal is not to index everything; it is to make trusted knowledge available in the moments where teams need it.
Problems this solves
- Search results are noisy, stale, or disconnected from the questions users actually ask.
- Knowledge lives across PDFs, help docs, tickets, spreadsheets, and application records with uneven structure.
- The team needs citations, permission filtering, and confidence signals before answers can be trusted.
- There is no repeatable way to test retrieval quality as documents and prompts change.
Outcomes to expect
- A retrieval architecture with explicit sources, metadata, refresh cadence, and access controls.
- Chunking, embedding, search, reranking, and answer composition choices matched to the content shape.
- Evaluation datasets that track answer grounding, missed documents, hallucination risk, and citation quality.
- Operational documentation for ingestion jobs, monitoring, and knowledge maintenance.
Deliverables
- 01 Source inventory and retrieval architecture
- 02 Document ingestion and normalization pipeline
- 03 Search and reranking implementation
- 04 Grounded answer UI or API integration
- 05 Retrieval evaluation set and quality dashboard
Technical examples
Technical support knowledge search
Blend help-center articles, historical tickets, and product metadata so support teams can find likely resolutions faster.
Sales and implementation enablement
Retrieve approved product, pricing, security, and implementation answers with source links that reduce ad hoc internal requests.
Fit criteria
A good fit when the work has real operating consequences.
You have valuable internal or customer-facing knowledge that is hard to search with ordinary keyword tools.
You need grounding, citations, permissions, and measurable quality before exposing AI answers.
You want a maintainable retrieval system, not a one-time document upload experiment.
Related paths
Keep exploring the connected work.
Engagement path
From knowledge inventory to evaluated retrieval.
RAG work is treated as data product work: source quality first, model behavior second.
01
Week 1
Inventory sources
Map documents, systems, permissions, freshness needs, and representative questions.
Outcome A source plan with risks and ingestion priorities.
02
Weeks 2-4
Build retrieval
Implement ingestion, chunking, metadata, search, ranking, and answer construction.
Outcome A working retrieval path connected to real sources.
03
Weeks 4-6
Evaluate quality
Create test questions, measure grounding and citation quality, and tune retrieval behavior.
Outcome A quality baseline with repeatable regression checks.
04
Weeks 6-8
Operationalize
Document refresh jobs, monitoring, feedback loops, and ownership expectations.
Outcome A RAG system ready for controlled use and ongoing improvement.
Start the conversation
Bring the technical context. We will help turn it into a practical path.
Share the workflow, platform, product, or AI system you are trying to improve. GTA Studios will respond with a focused next step.
- 01AI, cloud, product, and systems work
- 02Discovery through implementation
- 03Production-minded handoff