Use cases
Start where the memory matters most.
An AI employee is most valuable exactly where your team keeps paying the same cost twice: work nobody remembers the reasons behind, incidents you have seen before, and a backlog that never reaches the top of the sprint. Pick the one that hurts and start there.
No credit card required.
Three places to point your first digital employee.
Each one is a different buying trigger, and each one is a different reason memory beats raw code generation.
Legacy modernization
The system the business runs on, written by people who left. The blocker isn’t writing new code — it’s that nobody remembers why the old code does what it does. Rebuild that understanding first, then migrate it incrementally.
Read the use case →On-call & incident response
Most of incident response is recall: has this happened before, what did we do, what changed recently. An employee that remembers every past incident and deployment can triage while a human keeps authority over production.
Read the use case →Backlog burn-down
Dependency bumps, flaky tests, small bugs, deprecation warnings. This work isn’t hard, it’s unprioritised — and it quietly compounds into the thing that slows everything else down.
Read the use case →What they share
All three are memory problems wearing different clothes.
A tool that generates code helps with none of them, because in each case the expensive part isn’t writing the code. It’s knowing what your business already learned.
The pattern
Someone once understood this. They investigated the incident, made the architectural call, worked out why that flag exists. Then they moved teams, or left, or simply forgot — and the next person starts from zero. Every time that happens, you pay for the same understanding again.
What changes
An AI employee writes what it learns into your organizational memory and keeps it. The second incident of a kind is cheaper than the first. The second migration is cheaper than the first. Work starts compounding instead of resetting, which is the only way a team gets faster over years rather than slower.
The work isn’t hard. Re-learning it every time is what costs you.
Background reading
Why we think this is the real problem.
Plain-English writing on what companies lose and what it costs them.
The Knowledge That Walks Out the Door
When an experienced colleague resigns you don’t just lose a person. You lose years of context nobody wrote down.
Read the article →When Your Best People Become Bottlenecks
Long before your most experienced person leaves, they’re already a constraint. Everything routes through them.
Read the article →Why AI That Forgets Costs You Twice
Most AI tools meet your business as a stranger every time. You pay once for the tool and forever for the context.
Read the article →Get started
Pick one workflow and prove it.
The free plan gives you one AI employee and enough credits to see a real piece of work through, end to end, with your approval on anything that ships.
No credit card required.