RAG, LLMs & Azure OpenAI
Generative AI
We build production-grade Generative AI systems that go beyond demos. From enterprise RAG pipelines to custom LLM applications, we architect and deploy Gen AI that drives measurable business value.
Challenges We Solve
Sound Familiar?
- LLM pilots that hallucinate on enterprise data and can't go to production
- No way to ground AI responses in company-specific knowledge
- Generic chatbot responses that fail to reflect brand voice or policies
- Unacceptable latency for real-time enterprise use cases
- Compliance concerns about data leaving your Azure tenant
Our Approach
How We Help
Enterprise RAG Systems
Production-ready Retrieval-Augmented Generation over your internal knowledge base — documents, databases, and APIs — with hybrid vector + keyword search.
Custom LLM Applications
Fine-tuned or prompt-engineered LLM applications tailored to your domain, tone, and workflows, deployed on Azure OpenAI within your tenant.
AI-Powered Search
Semantic search and Q&A systems over enterprise content using Azure AI Search with vector indexing and re-ranking.
Document Intelligence
Automated extraction, summarization, and classification of contracts, reports, and unstructured documents at scale.
Tech Stack
Technologies We Use
How We Work
Delivery Process
Use Case Definition
Define the specific Gen AI use case, target users, data sources, and success criteria with your team.
Data & Knowledge Audit
Inventory and assess the quality of documents, databases, and APIs that will ground the LLM's responses.
Architecture Design
Design the RAG pipeline: chunking strategy, embedding model, vector store, retrieval logic, and prompt engineering approach.
Prototype & Evaluate
Build a working prototype and measure accuracy, latency, and hallucination rate against your evaluation dataset.
Production Build
Harden the system: authentication, rate limiting, logging, content filtering, and integration with your existing systems.
Deploy & Monitor
Deploy on Azure with CI/CD, monitoring dashboards, and alerting. Set up feedback loops for continuous improvement.
What You Get
Deliverables
Every engagement has a defined scope and concrete outputs. No vague “consulting reports” — you get production-ready artifacts.
- Production RAG API (FastAPI on Azure Container Apps)
- Vector index with chunking and embedding pipeline
- Evaluation framework with accuracy and hallucination metrics
- System prompt library and prompt versioning setup
- Admin dashboard for document ingestion and monitoring
- Runbook and architectural documentation
Why StarkLogik
What Makes Us Different
Hallucination-First Engineering
We build evaluation frameworks before we build features. Every RAG system ships with a benchmark suite measuring accuracy, faithfulness, and citation quality.
Azure-Native Security
All deployments run inside your Azure tenant. Your data never leaves your environment. We configure private endpoints, managed identity, and RBAC from day one.
Beyond the Demo
We've seen dozens of Gen AI pilots that impressed in the demo but failed in production. We build for the production case: edge cases, scale, and real user workflows.
FAQs
Common Questions
Get Started
Ready to Get Started with Generative AI?
Book a free 30-minute call with our engineering team to discuss your use case.