Add AI to an existing product
Introduce search, assistance, extraction, recommendations, or tool-using workflows without rebuilding the application around an LLM.
Explore this use caseAI Application & Agent Development
Build an AI system that fits the way your business actually works.
I help teams design and build production AI applications, AI agents, RAG systems, and private AI workflows. The work starts with the business process, data, users, and constraints, not a model demo.
The short answer
AI where it helps. Deterministic software where it does not.
A reliable AI application needs more than a model and a prompt. I design the surrounding system: how it finds private knowledge, which tools it may use, what users can access, how results are evaluated, and what happens when the model is uncertain or wrong.
When I Can Help
Start with the workflow, not the AI category.
You do not need to arrive knowing whether the answer is an agent, RAG, automation, or a conventional application. That is an architecture decision we can make after the requirement is clear.
Use private company knowledge
Design RAG and knowledge access around document types, exact business terms, permissions, citations, freshness, and retrieval quality.
Explore this use caseMove an agent beyond a demo
Define tool access, state, approval points, failure paths, evaluation, observability, and the boundaries the agent must not cross.
Choose a private AI architecture
Compare local, cloud, or hybrid inference based on data sensitivity, model quality, latency, operating cost, and the team available to run it.
What I Own
The decisions around the model matter most.
01
Requirement and architecture
Clarify the users, decision or task, source data, tool access, access control, measurable success criteria, and consequences of a wrong answer.
02
Application and integration
Build the interface, APIs, retrieval, agent workflows, deterministic business rules, integrations, and approval steps as one maintainable system.
03
Evaluation and operations
Make quality and failure visible through evaluation sets, traceable outputs, observability, cost and latency controls, and a practical operating path for your team.
A Necessary Boundary
Not every workflow should become an AI agent.
If a rule can be expressed clearly, tested exhaustively, and run cheaply, I prefer deterministic software. AI earns its place when interpretation, language, uncertain inputs, or flexible reasoning create enough value to justify the additional evaluation and operating burden.
Selected Proof
Systems I have designed, built, or operated.
ContextHarbor
A private context platform for document retrieval, project knowledge, prompts, and durable agent memory, with hybrid search, MCP access, evaluation, and project isolation.
See the evidenceKovaad
A conversational AI platform using LLM orchestration, vector memory retrieval, multi-agent workflows, cloud services, and production observability.
See the evidenceRecruitment intelligence
Python and AI workflows used to analyze large resume and interview-transcript collections while keeping the processing pipeline inspectable.
See the evidenceGGUF Switchboard
Local LLM infrastructure that manages model lifecycle and exposes a stable OpenAI-compatible API on constrained hardware.
See the evidenceRelated Engineering Notes
The architecture behind the service.
RAG by Document TypeWhy retrieval should reflect the source document rather than force every file through one chunking strategy.Your Coding Agent Has a Context ProblemA practical separation of repository state, project knowledge, architecture decisions, and durable memory.AI Won’t Fix a Broken ProcessWhy workflow clarity must come before model selection or automation.
Bring the use case, workflow, and constraints.
Tell me what users need to accomplish, what data the system can use, and what a good result looks like. We can decide whether the right answer is an agent, RAG, conventional automation, or a simpler deterministic application.
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