AI in production.Built inside your team.
Tide places a forward deployed engineer for your company — one senior engineer who sits with your team, learns your data and your systems, and builds AI your people will use every day.
Most AI projects stall after the demo. Tide's job is to get yours into daily use.
Real companies have several systems, a shared drive and rules that only live in people's heads. We learn how your data really looks before building anything on it.
Everything Tide builds goes into your repository and runs on your infrastructure, with tests, documentation and a proper handover. Your team can maintain it without us.
Early on, we sit down with your domain experts and write real test cases with expected answers. Every change is checked against them, so you can see how well the system performs.
What Tide builds
A senior engineer who joins your team and takes an AI initiative from first conversation to production and handover. This is the main way we work.
We pick one workflow, build an agent for it and put it in front of real users. A small, clearly scoped first project to see whether it works for you.
Ingestion, chunking, hybrid search, reranking and evaluation over your documents, tickets, repositories and drives. Answers come with sources and respect your permissions.
One place to ask questions across policies, contracts and internal documentation, available in Slack, in your own tools, or through Claude Code, Codex and MCP.
From idea to paying users: backend, agent logic, interface, billing and operations. Built so it can be run and maintained after launch.
Connect your repositories, documentation and internal tools to the coding agents your engineers use (Claude Code, Codex, Cursor), with memory, hooks and guardrails set up properly.
A short engagement to find where AI would pay off in your business, where it wouldn't, and what to build first. You get a written plan you can share internally.
We work from inside your team.
Tide joins your Slack and your stand-ups, works in your repo and with your data, and ships to your main branch every week. You talk to the engineer doing the work, not to an account manager.
We shadow the workflow, look at the real data and find where the time goes. The result is a short shared document of what we found.
Together with your experts we write a set of real cases with expected answers. Every change afterwards is checked against it.
A working version on your infrastructure, used by a few people early. We then improve it based on how they use it.
Everything is in your repository with documentation, runbooks and tests in CI. Your team can run it without us, or we stay on and take the next workflow.
Two products we built and run ourselves.
A job-search assistant with long-term memory. It tracks applications on a kanban board, sends scheduled digests and uses 18 tools behind a single agent. Live, with more than 30 active users.
A knowledge base for engineering teams. Retrieval over documentation, repositories and Notion with hybrid search and reranking, an MCP server for Claude Code and Cursor, and an evaluation suite that runs on every pull request.
“I've spent my career across very different software businesses, building systems that can't fail. I bring the same discipline to AI: measured, in production, and useful to the people who run the business.”
Common questions.
Whichever fits the task and your constraints. Recent work runs on Claude and OpenAI models, and the system is designed so the model can be swapped without rewriting the rest.
It stays in your environment. Tide builds on your cloud or on your own servers, with proper access control. Your data is never used to train models.
Every engagement starts with a set of real test cases written with your domain experts. Retrieval and answer quality are measured against them on every change, so you can see the results rather than take our word for it.
One senior engineer, working with you directly. If a project needs more people, Tide brings in engineers we have worked with before, and we tell you beforehand.
Tell us about the process you want to improve.
In a thirty-minute call you describe the workflow, the data and who would use it. We tell you honestly what AI can and cannot do there, and if it makes sense, propose a small first project.