I build AI systems that understand your business, handle real workflows, and scale without friction. Not demos — production-grade deployments.
LLM-powered agents that read your knowledge base, understand context, and resolve customer tickets automatically. Handles 70–85% of inquiries without human intervention.
End-to-end process automation — ticket routing, escalation logic, response generation, CRM updates. Systems that run 24/7 without supervision.
AI that schedules, rebooks, and manages appointments. Connects to Google Calendar, Calendly, and custom booking systems with natural language interfaces.
Multi-tenant AI platforms where different user roles access different AI capabilities, knowledge scopes, and permissions. Built for enterprise environments.
Purpose-built agents for specific business functions — data extraction, document summarisation, competitor monitoring, internal knowledge search.
Ingest your documentation, PDFs, databases, and internal wikis into a retrieval-augmented system. Your AI answers from your own data, not hallucinations.
The interface on the right shows a live AI support agent processing a ticket in real time. Ticket ingestion, context retrieval from a RAG knowledge base, response generation, and CRM update — all automated.
Three real bot platforms I've shipped — wired to a working command parser right here in your browser. Pick a platform below, type a slash command, hit enter. Try /help in any of them. No backend. Pure JS demo of the same parsing layer I run in production.
This is a working RAG (Retrieval-Augmented Generation) demo, running entirely in your browser against an embedded knowledge base about Dan's experience. In production this exact pipeline points at Pinecone, Chroma, or Qdrant, embeds queries with OpenAI text-embedding-3-large, retrieves the top-k matches by cosine similarity, and pipes them as context into Claude or GPT-4. The retrieval/citation logic you're seeing here is real — only the embedding lookup is faked (keyword-scored against a local KB). Ask anything.
LLMs aren't always the right tool. For pattern recognition with clean labels, time-series forecasting, latency-sensitive trades, or anything regulated — classical ML is faster, cheaper, deterministic, and easier to audit. I ship both, sometimes in the same system.
Hosted LLM with a retrieval layer over your private knowledge. Trades reasoning ability and natural-language flexibility for higher latency and per-token cost. Best when answers must be explained, when domain knowledge changes weekly, or when the user is in the loop.
Pure pattern in, probability out. Trained on your labeled history. No language layer, no hallucinations, sub-millisecond inference, fully deterministic. The right choice when speed and certainty matter more than explainability.
// And yes — I ship hybrids. LLM for the strategy layer, ML for the execution layer.
Docs, PDFs, APIs, DBs → structured chunks
pgvector / Pinecone semantic search index
Claude API — RAG context + system prompt
Python workflows, webhooks, CRM sync
AI agent handles order status queries, initiates returns, and processes refund requests without a human agent touching the ticket.
RAG system trained on documentation, changelogs, and past support tickets. Users get accurate answers instantly, 24 hours a day.
Natural language booking agent integrated into the property's calendar system. Handles availability checks, bookings, and modifications.
AI pipeline that ingests contracts, invoices, and reports — extracts structured data, flags anomalies, and summarises for decision makers.
Collects patient info, checks appointment availability, pre-fills forms, and routes to the correct department — all via a conversational interface.
Internal AI trained on your SOPs, HR policies, and product documentation. Employees get instant answers from your own knowledge base.
Real, production-grade builds — LLMs, RAG pipelines and agents I've designed, deployed and run end-to-end.
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