Generative AI
Generative AI grounded in your business context .
We design LLM products, RAG systems and copilots that use your knowledge, permissions and workflows instead of behaving like generic chatbots.
LLMsRAGCopilots
INSIGHTERZ / SYSTEM MAP REFERENCE
System lensContext → System → Outcome
Working modeDesigned for production
DiscoverModelBuildImprove
01 / What we build
Generative systems that know when to retrieve, reason and escalate.
Architecture, retrieval, prompts, evaluation and user experience are designed as one product system.
02 / Delivery
Reliable enough for real work.
We evaluate groundedness, retrieval quality, policy compliance, latency and cost before scaling user access.
- Source-aware retrieval
- Permission-aware access
- Evaluation datasets and regression tests
- Model and provider abstraction
03 / Use cases
Where this capability creates value.
Internal knowledge assistants
Designed around your data, users, operating constraints and measurable business outcome.
Customer support copilots
Designed around your data, users, operating constraints and measurable business outcome.
Proposal generation
Designed around your data, users, operating constraints and measurable business outcome.
Research synthesis
Designed around your data, users, operating constraints and measurable business outcome.
Policy Q&A
Designed around your data, users, operating constraints and measurable business outcome.
Document workflows
Designed around your data, users, operating constraints and measurable business outcome.
04 / Engagement
A practical path from idea to production.
Discovery
Problem, workflow, users, data and value.
Prototype
Technical proof with measurable evaluation.
Production
Integrations, controls, UX and deployment.
Improve
Monitoring, feedback and optimization.
RAG + controls
Enterprise generative AI needs a retrieval and governance design, not just a prompt.
Grounded systems connect trusted knowledge, retrieval, model behavior, evaluation and access control.
CAPABILITY / SYSTEM MAPcontrolled
SOURCEBusiness knowledgeDocs · DBs · APIs
INGESTNormalize + chunkMetadata · permissions
RETRIEVEVector + keywordGrounded context
GENERATELLM responsePrompt + tools
CONTROLEvaluate + auditQuality · safety
Operating model
Enterprise GenAI grounded in your knowledge, workflows and controls.
Useful generative systems are designed around source quality, permission-aware retrieval, evaluation and clear escalation paths.
- Trace answers back to approved sources
- Separate retrieval quality from generation quality
- Design permissions and evaluation into the architecture
Technology stack
Technology selected around the operating requirement.
OpenAIGeminiAzure OpenAIpgvectorPineconeLangChain
FAQ
Questions teams usually ask before they start.
Scope, data readiness, ownership and evaluation are clarified before a build is treated as a production commitment.
Usually yes. Discovery starts by mapping the systems, data quality, APIs and workflow constraints already in place.
No. We can work as a focused delivery partner while keeping ownership, documentation and operational handoff clear for your team.
We define technical and business acceptance criteria before production scope expands, then monitor the same signals after launch.
Start with the problem
Explore what generative ai can do for your business.
Bring us the workflow, decision or customer experience you want to improve. We will help define a pragmatic solution.