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.
Why Production-First Engineering Matters
The gap between "it works in the demo" and "it works at scale" is where most AI projects break down. A promising prototype falls apart when real users apply edge-case inputs, traffic spikes beyond the test environment, or model providers update their APIs without warning.
Production-first engineering means designing for observability, failure recovery, and operability from the first line of code — not as an afterthought before launch.
What this looks like in practice:
- Error handling and retries: Every model call has configurable retry logic with exponential backoff and fallback routing to alternative models when the primary fails
- Cost controls: Per-request cost tracking, usage quotas, and alerting before bills become surprises
- Latency budgets: Per-component latency targets defined at architecture stage — not discovered during load testing
- Data consistency: Idempotent processing, deduplication, and audit logs for every AI-modified record
- Graceful degradation: Systems that continue serving users with reduced functionality when an upstream model API is unavailable
This is the difference between an AI feature that ships once and an AI system your business can operate, iterate, and depend on.
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.