[AI LAunchpad]
Most enterprise AI stalls in the gap between a promising pilot and a system your operations can run. AI Launchpad closes that gap. You start from a working asset our AI Solutions Lab already built and proved on manufacturing data, then we shape it to yours.
scoped and measured on your P&L
Idea to production AI
Built on your data, in your environment
[AI Solutions Lab]
Your first mission opens with capability already built, not a blank page. The AI Solutions Lab funds and hardens models on manufacturing and automotive data ahead of any client request, so you inherit proven work on day one. You pay to adapt that capability to your data, not to invent it from zero.
[001]
You see a working build in week one. It starts from an accelerator the lab already proved, so it behaves like the real system from the first demo.
[002]
Your domain team sits beside our engineers. The build bends to your process, your data, and your edge cases as you watch.
[003]
The asset runs where it will live. You judge it on the number it moves in your operation, with evidence you can take to the floor.
[004]
The working asset goes into production and starts earning. You extend from something that already works, and the loop keeps feeding the next mission.
[What the lab hands you]
Every mission draws on work the lab funded ahead of demand. You collect the return on day one.
Your mission keeps moving across time zones. Onshore and offshore teams carry the build so work continues while one office sleeps.
You adapt proven models to your data instead of starting from a blank repository. Every mission draws on components the lab already built and hardened.
You inherit an asset that has already survived hard cases. The lab runs the deep investigation and testing before your mission begins.
The capability is ready the day your mission calls for it. A standing research team works ahead of demand across deep tech, AI/ML, and data science.
[The head start]
The same mission looks different depending on where week one begins.
Starting cold
Pilots stall in the gap between a demo and production.
Teams rebuild core models from scratch on every project.
Budget is spent before the work earns anything.
The result sits with a vendor your team cannot run alone.
With the lab behind you
You begin from an asset the lab already proved.
The build runs on your real data from week one.
You reach production in weeks and measure the P&L.
Your team owns and runs what ships.
[Why Ascentt]
Each mission is scoped to one decision and judged on the number it moves in your operation.
The asset runs on your real data first, so you see the number move before the build scales.
