Who it's for
- Support and ops teams needing high-confidence internal copilots.
- Products shipping AI features where hallucination risk must be controlled.
- Organizations centralizing fragmented knowledge into searchable workflows.
Service
AI RAG Development Services
We build retrieval-grounded AI systems that prioritize correctness, transparency, and measurable output quality.
What we deliver
- RAG architecture with ingestion, chunking strategy, and retrieval tuning.
- Evaluation harnesses for answer quality, citations, and failure cases.
- Guardrails, fallback behavior, and human-review escalation paths.
- Production UI/API integration with monitoring and cost controls.
Typical timeline
- • Knowledge audit + retrieval strategy: 1 week
- • Pipeline implementation + evals: 2-5 weeks
- • Product integration + hardening: 1-2 weeks
Engagement models
- • POC with success criteria and measurable eval targets.
- • Production implementation across product surfaces.
- • Continuous optimization retainer for retrieval and model quality.
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FAQ
Do you support multi-model strategies?
Yes. We can orchestrate multiple models with ranking/synthesis when reliability matters more than single-pass speed.
How do you measure RAG quality?
We define domain-specific evals, track retrieval precision/recall proxies, and audit answer quality over time.
Can RAG work on private internal data?
Yes. We design ingestion, access controls, and deployment patterns around your compliance and security constraints.
Ready to scope this service?
Share your requirements and constraints. We'll map the scope, timeline, and execution path.