AI agents development for workflows that have to run unattended.
Our own products are operated by agents that write, publish, reply to comments, run ads and reconcile spend every day. We build the same kind of agents for companies: scoped, evaluated, observable, and designed to fail safely when a model has a bad day.
What it is
An agent is a model with tools, memory and a goal. A production agent also needs scoped permissions, an evaluation harness, retries and fallbacks, cost controls, logs a human can read, and a clear hand-off when confidence is low. We build all of it, not just the prompt.
Typical systems we build: research and writing agents for content teams, publishing and community agents for social channels, analytics agents that answer business questions over data, operations agents for ad accounts and catalogues, and orchestration layers that coordinate several specialists.
We work with Claude, Gemini, GPT and open models and design so the model layer can be swapped. Everything ships with evals, so you know the accuracy before launch and notice when it drifts.
Who it's for
- Content and media companies automating production and distribution workflows
- Operations teams replacing manual multi-step processes in ads, catalogues or support
- Data teams that want self-serve analytics over their warehouse
- Product teams adding agentic features to their own software
What you get
- Agent architecture with tool definitions, permissions and escalation rules
- Evaluation suite with measured accuracy on your real cases
- Observability: traces, cost per task, quality dashboards and alerts
- Deployment on your cloud with runbooks and a documented handoff
- Optional operate retainer where we keep tuning as models change
How it works
- 01
Map
We walk the workflow with the people who do it today and define what the agent owns, what it asks about and what it never touches.
- 02
Prototype
A working agent on real data within two weeks, with the first evals.
- 03
Harden
Guardrails, fallbacks, cost limits and monitoring. Accuracy is measured and reported.
- 04
Operate
Production deploy, handoff, and optional ongoing operation.
Related work
Multi-agent analytics
Natural language to SQL over 500+ tables for a leading consumer media platform. 90%+ accuracy, used daily by 40+ business users.
In-house ad-ops platform
Replaced a $200K+/yr SaaS tool with a custom platform: maker-checker workflows, real-time budget alerts and auto-pause rules across ad networks.
Questions
- How do you keep agents from doing something expensive or wrong?
- Scoped tools, budgets per task, approval steps for irreversible actions, and evals that run before and after every change. Agents are built to stop and ask rather than guess.
- Will this lock us into one model provider?
- No. The architecture separates the model from the tools and evals, so switching providers is a configuration change validated by the same test suite.
- Do you build on frameworks or from scratch?
- Whatever fits. We have shipped on major agent frameworks and on lean custom orchestration. The evals and observability matter more than the framework.
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Let's scope it.
Tell us the outcome you need. You'll get a concrete plan, a fixed quote and a prototype date.
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