AI Systems & Agents
We design AI systems that understand your data and work as part of your team.
Typical problems we solve
- Knowledge scattered across docs, Notion, and CRMs — no single place to ask questions.
- Repetitive operations and support that could be handled by AI with the right guardrails.
- You need custom orchestration (agents + RAG + your APIs), not a generic chatbot.
What we build
- RAG systems over your internal docs, knowledge bases, and data — so your team (and agents) can query them reliably.
- AI agents for operations, research, and customer support — with clear workflows and human-in-the-loop where needed.
- Tailored orchestration using FastAPI, Supabase, Neo4j, and vector search — built to fit your stack and compliance needs.
How we work
- 1. Discovery & design — We map your use cases, data sources, and success criteria.
- 2. Prototype in 2–3 weeks — A working proof-of-concept on your data, so you see value before scaling.
- 3. Scale & observability — Production-ready pipelines, monitoring, and clear ownership.
Example use cases
- Internal “ask your docs” RAG for product and legal — reducing time to find answers.
- Multi-step research agents that pull from your DB and external APIs, with summaries and citations.
- Support triage and draft replies over tickets and knowledge base, with human review.
Why custom AI systems
Off-the-shelf chatbots cannot access your internal data or enforce your business rules. We build retrieval-augmented generation (RAG) systems that ground answers in your documents and databases, and AI agents that orchestrate multiple steps (search, summarisation, APIs) with clear guardrails and human-in-the-loop where needed. Our stack — FastAPI, Supabase, Neo4j, and vector stores — is chosen for control, scalability, and compliance so your AI runs where you need it.
Resources and further reading
Useful references for RAG, agents, and LLM orchestration (external links open in a new tab).
- Python — Runtime for FastAPI and most AI/ML libraries
- Supabase — Postgres, auth, and real-time data for apps
- Neo4j — Graph database for knowledge graphs and relationships
- FastAPI — API framework we use for orchestration and services
- Automation (n8n) — Combine AI with workflow automation
- Web3 & RWA — Tokenization and RWA platforms
- All services
Next step
Tell us your biggest challenge right now. We’ll suggest where AI agents or RAG can help and what a first prototype could look like.