AI agent & automation development
A clever prompt that works once in a playground is not a feature. We build application-specific AI agents and automations that survive thousands of real users — grounded in your own data, constrained by guardrails, and engineered so the unit economics actually work.
Start a conversationWhat's included
- Job-specific agents grounded in your data via tool calls
- Multi-model routing across Anthropic, OpenAI and Google for cost and quality
- Structured, validated output so results feed safely back into your app
- Prompt-injection guardrails and scoped tool permissions
- Automation pipelines (e.g. content, reporting, onboarding flows)
- Evaluation sets so quality is measured on every model change
Grounded, guarded, and economical
Our agents are built around a clear job — onboard a member, answer a programme question, generate a workout — and grounded in your data through tool calls, not left to free-associate. A guardrails layer scopes exactly what each agent can touch, so an agent helping a member can never write to billing or admin data, and prompt-injection attempts are refused. We route each call to the cheapest model that meets the quality bar and cache deterministic outputs, so AI features cost single-digit dollars per thousand monthly users instead of bleeding budget.
We have shipped this, including a content pipeline
We run an automation pipeline that turns a one-line brief into a finished social reel, carousel and caption — LLM script, AI voiceover, headless-browser slide rendering, auto-posting — in about four minutes per asset. The same engineering discipline goes into the agents we build for clients: versioned prompts, structured output, and an evaluation set that runs on every change.
Frequently asked
How do you stop an AI agent from doing something harmful?
Every agent has explicitly scoped tools — it can only call the functions you allow — and a guardrails layer that treats user content as data, not instructions, so prompt-injection attempts are refused. Anything touching a sensitive action goes through a second review pass. We also log every tool call for replay and debugging.
Won't AI features cost a fortune in tokens?
Not if they are engineered properly. We cache deterministic outputs, route short tasks to cheap models and long-form tasks to premium models only when quality demands it, and use prompt caching for stable system prompts. In practice our AI features cost single-digit dollars per thousand monthly active users.
Can you add AI to our existing app?
Yes — most of our AI work is adding a grounded, well-scoped agent or automation to an existing product rather than building from scratch.
Let's scope it.
A two-week fixed-scope diagnostic tells you the full cost and plan before you commit. Tell us what you're building.