AI AGENTIC SOLUTIONS
ADLC · RAG · LLM Building · Chunking & Embeddings · Foundry IQ · Fabric IQ · OneLake
Enterprise AI agents - built for production, not demos
We help organizations design agentic systems with a clear Agent Development Lifecycle (ADLC), strong RAG foundations, LLM engineering, and Microsoft's Foundry IQ / Fabric IQ / OneLake stack - so agents are accurate, observable, and aligned to business KPIs.
Agentic AI
Multi-agent systems that plan, use tools, and complete workflows under governance.
New Tech Stack SDLC (ADLC)
Plan → Build → Test → Deploy → Operate → Monitor with experimentation and runtime loops.
RAG & Knowledge
Chunking, embeddings, retrieval, and citations grounded on OneLake and enterprise data.
Microsoft AI Stack
Azure AI Foundry IQ, Fabric IQ, and OneLake as the enterprise agent control plane.
Dedicated AI solution pages
AI Agentic Solutions
Design, build, and operate enterprise AI agents that plan, use tools, orchestrate workflows, and deliver measurable business outcomes.
Learn moreAgent Development Lifecycle (ADLC)
The new tech-stack SDLC for AI agents - Plan, Code & Build, Test & Release, Deploy, Operate, and Monitor with experimentation and runtime optimization loops.
Learn moreEnterprise RAG Systems
Production Retrieval-Augmented Generation: chunking, embeddings, vector search, reranking, citations, and agent-ready knowledge retrieval.
Learn moreLLM Building & Fine-Tuning
Build, adapt, and operate LLMs for enterprise workloads - prompt systems, fine-tuning, evaluation, hosting, and cost/performance tradeoffs.
Learn moreData Chunking & Embeddings
The foundation of RAG quality: intelligent data chunking, embedding model strategy, metadata design, and index hygiene.
Learn moreMicrosoft Azure AI Foundry IQ
Build, evaluate, and operate enterprise agents and models on Microsoft Azure AI Foundry - with governed catalogs, tools, and Foundry IQ intelligence patterns.
Learn moreMicrosoft Fabric IQ
Unify analytics and AI semantics on Microsoft Fabric IQ - lakehouse intelligence that powers agents, BI, and decision systems.
Learn moreMicrosoft OneLake
OneLake as the enterprise data foundation for Fabric IQ, RAG corpora, and Foundry agents - one lake, many workloads.
Learn moreWhat you get with Gensten
- ADLC operating model for agent teams
- RAG pipelines: chunking, embeddings, retrieval evals
- LLM selection, fine-tuning, and hosting strategy
- Azure AI Foundry IQ agent platforms
- Fabric IQ semantics + OneLake knowledge lakes
- MCP tooling, OpenTelemetry, and governance gates
AI Agentic Solutions - FAQs
Common questions about agentic AI, ADLC, RAG, LLMs, Foundry IQ, Fabric IQ, and OneLake.
What are AI Agentic Solutions?
AI Agentic Solutions are production systems where AI agents plan multi-step work, retrieve private knowledge with RAG, call enterprise tools, and complete workflows under governance - not just chat responses.
What is the Agent Development Lifecycle (ADLC)?
ADLC is the new tech-stack SDLC for AI agents: Plan, Code & Build, Test & Release, Deploy, Operate, and Monitor, with experimentation and runtime optimization loops so agents improve safely after go-live.
How do RAG, LLMs, and embeddings fit together?
Embeddings turn documents into searchable vectors after chunking; RAG retrieves the best chunks at query time; the LLM generates grounded answers or plans agent actions using that retrieved context.
What are Microsoft Foundry IQ, Fabric IQ, and OneLake?
Azure AI Foundry IQ is the agent/model control plane; Fabric IQ provides governed business semantics for analytics and agents; OneLake is the unified data lake that stores and shortcuts enterprise data for both BI and AI.
Can Gensten implement agentic AI on Microsoft Azure?
Yes. We design and deliver Azure AI Foundry agents connected to Fabric IQ semantics and OneLake knowledge, with ADLC governance, evals, security, and KPI measurement.
How long does an enterprise agentic pilot take?
A focused pilot (one use case, RAG corpus, tool set, and eval suite) typically runs 6–12 weeks. Production rollout depends on data readiness, integrations, and compliance requirements.
Are Gensten AI agents suitable for regulated industries?
Yes. We build for BFSI, healthcare, and other regulated environments with audit logs, least-privilege tools, content safety, data residency, and ADLC release gates.
Where should I start if we are new to agentic AI?
Start with use-case and KPI alignment (ADLC Plan), then a RAG-backed pilot agent with clear tools and evals. Expand to multi-agent orchestration once retrieval quality and governance are proven.