Expected Outcomes
- - Higher answer accuracy on domain-specific questions
- - Lower hallucination rates through retrieval and context controls
- - Faster updates when source knowledge changes
Service Overview
RAG Development
Grounded responses with retrieval, indexing and context controls.
RAG quality depends less on the model and more on retrieval design, chunking strategy and content governance. This service builds reliable grounded response systems.
Deliverables
- - Indexing and chunking strategy aligned to content types
- - Retrieval pipeline with ranking, filtering and citation behavior
- - Evaluation framework with baseline tests and quality thresholds
Process
Phase 1
Discover
Goals, baseline and technical constraints are translated into a prioritized execution setup.
Phase 2
Build
Features, integrations and UX-critical flows are delivered in clear milestones.
Phase 3
Launch
Rollout, QA, tracking and technical sign-off ensure a stable production release.
Phase 4
Optimize
Performance, conversion and operational workflows are improved continuously with data.
FAQ
Can we use our existing docs and knowledge base?
Yes. Existing sources can be normalized, indexed and versioned for retrieval workflows.
How do we measure RAG quality?
With task-specific benchmarks, retrieval hit metrics and human review loops.
Can RAG run with private data only?
Yes. Architectures can be designed for private datasets and controlled model access.
Project References for This Service
Real Delivery
Private Trading Product
News-Gathering Trading Intelligence
RAG and Decision Support
Built a system that ingests market news, normalizes signals and produces stock-level structured fundamental analysis.
Shortened research cycles and improved consistency in stock-level analysis.
PythonLLM Analysis LayerMarket Data APIsDashboard UI
View project