Custom AI Solutions That Solve Real Business Problems

AI chatbots and assistants, RAG systems over your documents, LLM agents that use tools and APIs, natural-language-to-SQL analytics, document processing (including scanned PDFs) and workflow automation. Each has a working example on this page: one live demo and open-source project examples.

Yes. Your documents (PDFs, policies, help articles or wiki pages) are split into passages and indexed in a vector database such as ChromaDB, FAISS, pgvector or Pinecone, chosen for your data volume and hosting. The chatbot answers from the most relevant passages and cites its sources, and scanned or layout-heavy files can be read with vision models, as in the VisionRAG example. Who may see which documents is agreed during scoping, because it shapes how the system is built.

Yes, as long as there is an API or database access to work with. My builds connect to PostgreSQL, DuckDB, Redis and CSV data, REST APIs, webhooks, email, Telegram and Google Sheets. For databases I prefer read-only credentials and validated queries, so the AI cannot change data unless that is explicitly required and approved.

Usually. Services are packaged with Docker so they run on most container platforms, with CI/CD through GitHub Actions. My work includes Azure OpenAI, a multi-agent system built for AWS ECS Fargate, and this website, which runs on Railway. For other platforms or locked-down internal environments, I confirm the fit during scoping rather than assume it.

Against a set of real test questions with agreed good answers, before launch. For RAG I measure faithfulness, answer relevance and retrieval quality with RAGAS-style metrics; NL2SQL queries are validated and given a confidence score; and tracing tools such as LangSmith show how each answer was produced. No AI system is right every time, so the aim is measured quality, sensible fallbacks such as showing sources or declining to answer when the evidence is weak, and monitoring after launch.

Your data and access are used only for your project. In practice that means least-privilege, read-only access where possible; samples or anonymised data during prototyping; keeping data in your own cloud account when required; an AI model provider whose data terms fit your policy; and no credentials in code. No system is perfectly secure, so production systems may also need access controls, logging and human approval for sensitive operations. If you must meet a specific compliance standard, tell me at the start so the system is designed around your organisation's controls and review process.

Yes. I can deploy to your cloud account or a managed platform with containerised services, CI/CD, logging, and monitoring of cost, latency and answer quality, as in the LLMWatch example. Ongoing maintenance, such as monitoring, fixes and model updates, can be agreed as a separate arrangement after launch.

Yes. I start by reviewing where it falls short, whether that is retrieval quality, prompts, evaluation, cost or latency, and fix the highest-impact issues first. Typical work includes better chunking and re-ranking, caching, guardrails, and adding evaluation and monitoring. I will need access to the code and a few examples of where it goes wrong.

By the engagement model and by your requirements, data availability, integrations, security needs and deployment expectations. Published starting points are $249 for a focused chatbot, $299 for MCP setup, $499 for workflow automation, $999 for RAG, NL2SQL, analytics or document intelligence, and $1,499 for AI agents; discovery work and larger systems are estimated individually. You receive a written estimate before any work begins.

A short description of the problem and who will use the solution, examples of the data or documents involved (a sample is enough at first), the systems it needs to connect to, any security, hosting or compliance requirements, and your timeline. The enquiry form on this page covers the essentials.

I read every enquiry personally and reply within 24 hours. From there we have a short conversation to understand the problem, and you receive a scoped estimate before any work begins.

Yes. I am based in Bengaluru, India and work remotely, so projects run over email, chat and video calls wherever you are.