AI & Machine Learning
Custom AI wired into real products — chatbots, agents, RAG, computer vision, and voice that ship, not just demo.
Most "AI projects" die in the demo stage — a slick prompt in a sandbox that never touches production data, auth, or real users. Ours don't. We build the plumbing around the model: retrieval pipelines that pull from your actual documents and databases (pgvector, LlamaIndex), agent orchestration that calls your internal tools and APIs (LangGraph), and the guardrails, logging, and citation trails that make an LLM output something your team can trust and ship. Whether it's a support assistant grounded in your knowledge base, a document-extraction pipeline, or a real-time voice agent, the work is the same discipline: wire a model into a system that has to work on Tuesday and still work in six months.
This is for companies with a specific, bounded problem — not "add AI to the roadmap." Support teams drowning in repetitive tickets. Ops teams re-keying data from scanned forms or IDs. Product teams that want personalization or churn prediction but don't have an ML team to build it. We work across OpenAI and Anthropic Claude for language tasks, PyTorch for custom vision and prediction models, and ElevenLabs alongside OpenAI Realtime for voice — picking the stack based on what the problem needs, not what's trendy.
Credibility here comes from scope discipline, not size. We start with a narrow, working slice on your own data — not a generic chatbot demo — because that's the only way to know if retrieval accuracy, latency, and edge cases will hold up before you commit budget to the full build.
Our process
Data & Scope Audit
We map your docs, databases, or image/audio sources against the use case — chatbot, agent, vision, or voice — and define what accuracy and latency look like before writing code.
Grounded Prototype
We build a working slice on your real data: a RAG pipeline in pgvector and LlamaIndex, or a first-pass vision/voice model, so retrieval quality is proven early, not assumed.
Agent & Integration Build
Tool-calling logic goes in via LangGraph, connected to your APIs and internal systems, with citations, guardrails, and logging so outputs are traceable, not a black box.
Hardening & Handoff
We load-test edge cases, tune prompts and retrieval against real usage, and hand off a production-hardened system with monitoring, not a demo that breaks under real traffic.
What we offer
Chatbots & Assistants
LLM support and sales assistants grounded in your docs, on web, WhatsApp, or Slack
AI Agents & RAG Systems
Tool-using agents and retrieval systems that answer from your private data, with citations
Computer Vision & OCR
Object detection, document/ID extraction, and quality-inspection pipelines
Voice & Speech AI
Real-time voice agents, transcription, and live translation with OpenAI Realtime and ElevenLabs
ML & Recommendation
Custom prediction, churn, fraud, forecasting, and personalization models
Technologies we use
Frequently asked questions
Tell us what
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