GenAI Solution Design
Replace fragile demos with production-grade assistants, retrieval systems, and agentic workflows. Reduce hallucinations, control cost and latency, protect data, and make outcomes measurable—so stakeholders trust what ships.
- Model-agnostic (open or closed weights)
- Designed for regulated environments with Responsible AI controls
- Evidence-by-design (EU AI Act and ISO/IEC 42001 aligned)
The Challenge
Most GenAI POCs fail for predictable reasons: retrieval quality is uneven, hallucinations erode trust, data boundaries are unclear, and costs spike without guardrails.
Teams often iterate on prompts without building the system contracts required for reliability, governance, and Day-2 operations.
Our Approach
We produce a use-case-first blueprint that covers end-to-end architecture: grounding data, orchestration, tool interfaces, evaluation, and safety controls.
The result is a design your teams and vendors can implement with clear quality targets, risk boundaries, and an operating path to production.
Key Outcomes
Core Deliverables
Solution Blueprint
End-to-end architecture covering data flow, model integration, and system boundaries—with clear interface contracts and ownership decisions documented.
- Decision log for model, routing, retrieval, and safety trade-offs
- Ownership model (teams/vendors) and operating assumptions
Critical Infrastructure Provider
The Context
Designed a secure RAG architecture for sensitive engineering data, ensuring zero leakage.
The Outcome
Result: Deployed trusted internal Q&A agent used by 5,000+ engineers with verified citations.