The "Last Mile" of AI
Is The Hardest.
It's easy to get a demo working. It's easy to make Gemini 3 write a poem. But making an AI system that reliably interacts with your legacy SQL database, adheres to strict compliance rules, and doesn't hallucinate when faced with edge cases? That is an engineering problem, not a prompting problem.
Most businesses are currently stuck in "Pilot Purgatory." They have a dozen half-finished AI projects that look cool but can't be trusted in production.
I solve the Integration Gap. I don't just fine-tune models; I build the scaffolding around them—the guardrails, the retrieval pipelines, the evaluation suites—that turns a probabilistic model into a deterministic business asset.
Deterministic Control
Constraining LLM outputs using grammars and schema validation (Pydantic) to ensure 100% type-safe JSON responses for your APIs.
GraphRAG Implementation
Implementing Knowledge Graphs alongside Vector Stores to allow the AI to "reason" across disconnected data points in your documentation.
Adversarial Testing
Red-teaming your AI agents before deployment to identify prompt injection vulnerabilities and logic failures.
Engineering Capabilities
Moving beyond "Chat with PDF" into complex, stateful, and autonomous systems.
Multi-Agent Swarms
Orchestrating teams of specialized autonomous agents (using AutoGen or LangGraph) that collaborate, critique, and execute complex workflows.
Advanced RAG & GraphRAG
Moving beyond simple vector search. We implement GraphRAG to capture semantic relationships in your data, ensuring high-fidelity retrieval for complex queries.
Sovereign & Local AI
Deploying high-performance open-weights models (Llama 4, Mistral Large) on your own VPC or bare metal. Zero data egress, ultra-low latency.
Cognitive Pipelines
Designing deterministic control flows where probabilistic AI models act as reasoning engines within robust software architectures.
AI Governance & Evals
Implementing rigorous evaluation frameworks (LLM-as-a-Judge) to continuously monitor model performance, drift, and safety before deployment.
Legacy Modernization
Using AI to analyze, document, and refactor legacy codebases (COBOL, Java 8) or act as an intelligent layer over ancient SQL databases.
Production-Grade Stack
From Chatbots to Agentic Swarms
The era of the single "Helpful Assistant" is ending. Complex business problems require specialization. You don't hire one employee to be your lawyer, coder, and accountant; why expect one LLM prompt to do it all?
I architect Multi-Agent Systems where distinct AI personas collaborate to solve problems. Using frameworks like LangGraph or AutoGen, we create:
- The Planner: Deconstructs a vague user request ("Research competitors in the EV space") into a step-by-step execution plan.
- The Researcher: Uses tool-calling capabilities to browse the web, scrape financial reports, and summarize findings.
- The Critic: Reviews the Researcher's output for hallucinations or logical fallacies, rejecting it if it doesn't meet quality standards.
This "System 2" thinking approach allows for self-correction and significantly higher success rates on complex tasks compared to zero-shot prompting.
Sovereign AI & Data Privacy
For many enterprises, sending proprietary code or financial data to OpenAI's API is a non-starter. The risk of data leakage or vendor lock-in is too high.
The gap between closed models (GPT-5) and open-weights models (Llama 4, Mistral) has effectively closed. I help organizations deploy Local AI infrastructure.
By running quantized models on your own on-premise GPUs or private VPCs, you achieve:
Total Privacy
Your data never leaves your network. It is physically impossible for the model provider to train on your secrets.
Zero Latency
No more waiting for API queues. Local inference can be optimized for your specific hardware.
Fixed Costs
Stop paying per token. Run the model 24/7 for the cost of electricity and hardware amortization.
Custom Fine-Tuning
Use LoRA adapters to train the model specifically on your internal jargon and coding standards.
Evaluation: The Missing Link
How do you know your RAG system is working? Because it "feels" right? That doesn't scale.
I implement DSPy and other optimization frameworks to treat prompts as weights that can be trained. We build golden datasets of Question/Answer pairs and run automated evaluations (using LLM-as-a-Judge) to score your system on:
- Context Recall: Did the system find the right document?
- Faithfulness: Is the answer actually derived from the document, or is it hallucinated?
- Answer Relevance: Did it actually answer the user's question?
This allows us to deploy with confidence, knowing exactly how the system performs against benchmarks, rather than relying on vibes.