Most AI projects fail between the demo and production. Not because the technology doesn't work — but because building a working proof-of-concept and building a reliable AI system are two entirely different engineering problems. We specialise in the second one.
What We Build
We design and build AI-powered products end-to-end — from architecture and model selection through to deployment and ongoing monitoring. Every system we ship runs in production for real users, handling real data, under real load.
RAG Pipelines and Knowledge Assistants
Retrieval-Augmented Generation systems that let your LLM answer questions grounded in your own documents, databases, and internal knowledge. We handle chunking strategy, embedding models, vector database selection, retrieval ranking, and prompt engineering — the full stack, not just a demo.
Voice AI Agents
Real-time conversational AI with sub-300ms end-to-end latency. We use Deepgram for speech-to-text, OpenAI or Anthropic for reasoning, and ElevenLabs for natural speech synthesis — integrated over WebRTC or WebSocket pipelines designed for production reliability. We've built voice platforms processing 2000+ calls per day.
Autonomous Multi-Step AI Workflows
Agentic systems that plan, execute, and recover from failures without human intervention. Document classification pipelines, multi-step research agents, automated data enrichment — workflows that replace repetitive knowledge work at scale.
LLM Integrations into Existing Products
Adding AI capabilities to products that weren't built for it. We work with your existing APIs, databases, and infrastructure to add LLM-powered features without rebuilding from scratch.
Document Intelligence and OCR Pipelines
Extracting structured data from unstructured documents — clinical notes, contracts, invoices, emails. We combine traditional OCR with LLM-based extraction to handle the messy formats that rule-based systems can't.
How We Work
Every engagement starts with a discovery sprint — typically one week — to validate technical feasibility and define the right architecture before writing production code. We don't charge for discovery if we don't believe we can deliver measurable value.
After discovery: we scope tightly, build iteratively, and ship working software on a two-week cadence. You see working code in weeks, not months.
Our stack: Python, FastAPI, LangChain, LlamaIndex, OpenAI, Anthropic Claude, Deepgram, ElevenLabs, Pinecone, Weaviate, PostgreSQL with pgvector, AWS, Docker, Kubernetes.
Related Work
We built a real-time Voice AI roleplay simulator for a sales onboarding platform — sub-300ms latency, automated call scoring, and a manager dashboard. Agent onboarding time dropped by 70%.
We also built Cuebo's multi-tenant AI call auditing platform: 2000+ calls processed per day, 90% reduction in manual review time, tenants onboarding in under 2 hours instead of days.