Case study · AI product development
AI product development case study: KrutAI, 30 minutes to 30 seconds
KrutAI turns a product-copy task that took 30 minutes into a 30-second one. Catalogue work stopped being a queue, and the team measured a 40% conversion lift.
Measured result
What changed after launch
40%
conversion lift after the new workflow
30 min
old product-copy turnaround
30 sec
new AI-assisted turnaround
Production
full-stack product, not a prototype
The challenge
Product teams were losing half an hour every time they prepared copy for one item. At catalogue scale that is not a delay, it is a queue: launches wait behind it, and the brand language drifts because six people write it six different ways.
The product needed more than a prompt box. Someone has to constrain the input, hold the brand voice, catch a bad generation before a customer sees it, and account for who used what. That is a backend, not a prompt.
The outcome
The team could create product copy in seconds instead of batching hours of manual work.
Brand and input controls made the output usable inside a repeatable catalogue workflow.
A working model was the smallest part of the build. Accounts, data, usage limits and failure handling are what made it something the team could rely on daily.
Scope
What we built
- Generation constrained to the brand voice, so six people stop writing it six ways
- The 30-minute copy task reduced to an input, a review and an accept
- Backend APIs and a data model built for catalogue volume, not for one item at a time
- Accounts and access control, so usage is attributable to a person
- Usage tracking, because someone has to answer what the model cost this month
- A bad generation caught before a customer sees it, not after
- Deployed to production with the failure paths handled, which is the part that took the time
- A codebase their team can read without us
How it ran
The delivery timeline
Find the real bottleneck
We started with the 30-minute manual task and defined the output quality the new workflow had to match.
Test the generation loop
We made the core input-to-copy flow clickable early, so the team could judge useful outputs before the full build.
Build the product around the model
Accounts, data, usage controls, APIs and failure states came after the model worked. That is the part that takes the time.
Track the business result
The shipped workflow cut turnaround to 30 seconds and contributed to a measured 40% conversion lift.
They handled our whole stack under heavy load and shipped faster than anyone I've worked with. If you need an MVP built right and fast, this is your team.
You own the code, infrastructure and accounts from day one