Acceler / How we work
Your project moves faster on the build capacity we bring in. Then we hand the system over to your team, and stay until they can work on it without us.
Our engineers
Your team
About 2 weeks
Our engineers
Our senior people and a practitioner from a frontier lab spend two weeks learning your systems as they are, with your team.
Your team
Your steering group, the buyer, the sponsor and your architect, agrees the architecture with us before anyone writes code.
A plan you have shaped, and an architecture you have agreed.
About 8 weeks
Our engineers
Our engineers do the building, inside your environment, with the practitioner reviewing the work as it grows.
Your team
At each checkpoint you see the working system and its test results, and we decide the next step together.
A working system, on your own systems and data.
About 2 weeks
Our engineers
A hands-on accelerator on the system we just built, run by the same practitioner, so your team knows it inside out and how to extend it.
Your team
We leave one piece unbuilt on purpose. Your team builds it, with our practitioner beside them.
A team that can take the system forward on its own.
About 2 weeks
It ends in a joint architecture review. Nothing is built before you have seen the plan.
What you get:A plan you have shaped, and an architecture you have agreed.
About 8 weeks
Your team is not asked to do the work it is paying us for.
One or two milestone reviews let both teams change course if the work says so.
What you get:A working system, on your own systems and data.
About 2 weeks
The proof is your team extending the product on the last day.
What you get:A team that can take the system forward on its own.
Builds
Two weeks of discovery with your steering group, then about eight weeks of build by our engineers, with checkpoints you steer. In the last two weeks your team learns the system, builds the final piece itself, and takes it on.
How a build worksEngineers
When AI-native engineers are hard to find, we add capacity. Senior engineers with hands-on experience of AI systems in production join on contract-to-hire, direct placement, or as a managed pod.
We work with teams across many industries, and we have a fair share of learnings on what works and what does not. The most common failure we see: teams do not build their own capability, so they cannot scale what they deliver with AI. Book thirty minutes and we will exchange notes: what is working for you, what is not, and what we are seeing.
