We build AI-first SaaS products using Claude, GPT-4o, RAG pipelines, and custom agents. Not bolt-on AI — architecture decisions made from day one.
What we build
Claude, GPT-4o, or open-source models integrated into your product. We handle API calls, error handling, fallbacks, and model routing.
Let users query your own data with AI. Document ingestion, chunking, vector embeddings, and semantic search using pgvector on Supabase.
Autonomous agents that plan, reason, and execute multi-step tasks — from web research to code generation to API orchestration.
Real-time streaming responses so users see output as it generates — no loading spinners, no slow perceived latency.
Production prompts are different from playground prompts. We design, test, and version prompts that perform reliably at scale.
Right-sizing models, caching, batching, and prompt compression to keep your AI infrastructure costs in check as you scale.
Use cases
Long-form content generation with your brand voice
Upload PDFs, contracts, manuals — ask questions
LLM-powered helpdesk trained on your knowledge base
Code review, generation, and debugging tools
Natural language queries over your database or CSV files
Multi-step agents that replace repetitive knowledge work
Process
Days 1–2
Before writing a line of code, we map out the AI architecture: which models, which data flows, how agents interact, and what will cost what in production.
Week 1–2
We build the full SaaS platform first — auth, billing, database, dashboards — so AI features have a production-ready home.
Week 2–4
LLM calls, RAG pipelines, agents, streaming UIs — built against the real platform with production API keys and realistic data.
Week 4–5
We stress-test prompts, edge cases, and failure modes. Production AI requires defensive prompt engineering — not just happy-path demos.
Stack
We use the best tools for each layer. No vendor lock-in — we pick models and frameworks based on your requirements, not whoever sponsors our blog.
Best-in-class reasoning, long context, tool use
Vision, function calling, broad capability
Agent orchestration and chain management
Vector search on your Postgres database
Streaming RSC, App Router, Edge functions
Type-safe AI responses and tool definitions
FAQ
AI SaaS development starts from £8,000. This covers the full platform (auth, billing, dashboards) plus AI features: LLM integration, RAG pipeline, streaming UI, and prompt engineering. Complex multi-agent systems cost more — we scope honestly before you commit.
It depends on the task. Claude excels at long-context reasoning, nuanced writing, and careful instruction-following. GPT-4o is stronger at vision tasks and has broad capability. Many products use both via a routing layer. We advise based on your actual use case, not brand preference.
A chatbot takes input and returns output. An agent can take actions — call APIs, search the web, run code, chain multiple steps, and make decisions along the way. Agents are more powerful but require careful design to be reliable in production.
We don't send user data to AI models without your explicit design decision. We advise on data residency, model provider terms, and anonymisation strategies. For sensitive industries we can configure local model deployments.
Yes. If you have an existing Next.js/Supabase codebase we can audit it and integrate AI features directly. For other stacks we'll advise on the best approach — sometimes a clean AI microservice is better than patching an existing monolith.
Highly variable. A low-traffic tool might cost $20–100/month in API calls. A high-volume pipeline could be $2,000+. We model your expected token usage before you build so there are no surprises. We also help optimise as you scale.
Tell us what you're building. We'll advise on the right AI architecture, model choices, and give you a realistic cost estimate — no obligation.
Discuss your AI product →