We run on what we build
Our own operations run on Claude and Codex. BiasFeed is the proof: six months live and unattended. Read the case study.
You've seen the hype. This is what it looks like when AI actually works: we find where it will genuinely pay off, build it, and hand it over running.
Most AI consultants come from data science.
We come from infrastructure.
See where AI will pay off, before you spend a dollar.
We map your workflows, identify the high-impact use cases, and build a roadmap that starts with quick wins. Data readiness, a clear business case, and no science projects.
Hand the repetitive work to software, and give your team their week back.
Approvals, data entry, report generation, document routing. We automate the tasks your team shouldn't be doing by hand, on open-source platforms you own outright.
Adopt AI with confidence, and keep your data yours.
Usage policies, guardrails, risk assessments, and secure data handling. Safe adoption you can prove, not just promise.
Purpose-built agents that quietly do the real work.
Operational agents that integrate with your existing systems and handle real tasks. Built and run the same way we run our own: AI for judgement, automation for everything else.
Go deeper on our AI services, or the IT services and infrastructure that hold them up.
AI doesn't run on good intentions. It runs on servers, APIs, networks, and security policies. We spent 30 years building them.
Our own operations run on Claude and Codex. BiasFeed is the proof: six months live and unattended. Read the case study.
No account managers, no hand-off. You deal directly with the veteran doing the build.
We don't sell software or clip vendor margins. The advice serves your outcome, not our commission.
ChatGPT answers questions. An agent does the job. It plugs into your systems, does the work a person would, and checks in with you where it matters.
We build it on open-source platforms you own, so your data stays yours. The eight steps beside this take it from idea to something running in your business.
Before a line of prompt, we pin the use case, the user's real need, what success looks like, and the constraints it has to live inside. Scope creep is where agents go to die.
The agent's goals, role, instructions and guardrails, written deliberately. This is the contract it operates under, not something you tune away later.
Base model, parameters, context window, cost and latency, matched to the job. The biggest model is rarely the right one; we pick the cheapest that clears the bar.
Local functions, web and app APIs, MCP servers, even other agents as tools. The agent reaches into your systems through exactly the connections the task needs, and no more.
Conversation history, working memory, vector search, structured databases, file storage. The agent remembers what it should and forgets what it shouldn't.
Routes, triggers, queues, agent-to-agent handoffs, and error handling. The automation layer that moves data around the AI, so tokens are only spent on judgement.
A chat window, a web app, a bare API endpoint, or a Slack bot. The agent meets your team where they already work.
Unit tests, latency checks, quality metrics, and iteration. We prove it works before it touches anything that matters, then keep proving it.
News decomposed into atomic facts, then rebuilt in three political framings, end to end, unattended. What we built, what it cost, and why showing the bias beats labelling it.
Tell us what's slowing your business down. If AI is the right answer, we'll build it. If it isn't, we'll say so. The first conversation is always free.