- Author
- Creative Ventures engineering
- Published
- Read time
- 9 min read
How to estimate AI features: a practical framework for product teams
AI feature estimation is harder than CRUD sizing — the happy path lies. A three-axis framework for AI feature scoping: accuracy tolerance, recoverability and data exposure.
Estimating a CRUD feature is a habit. Estimating an AI feature is an argument. The model works in the demo, fails 8% of the time in real use, and the 8% is exactly where your users are. Here is the framework we now use before we give a client a number on any AI feature.
The three axes we measure in AI feature estimation
Every AI feature we are asked to scope gets sized along three axes. Accuracy tolerance — how wrong can the output be before the user cares. Recoverability — if the model gets it wrong, what does recovery cost. Data exposure — what does the model need to see to do its job, and what is the blast radius if it leaks.
What we used to get wrong about AI estimation
Our first year of AI estimates were basically software estimates plus a fudge factor. We scoped the happy path, multiplied by 1.5 and called it a day. We consistently missed the eval harness, the fallback UI and the human-in-the-loop path. None of those are optional in production; all of them are invisible in a demo.
“The cost of an AI feature is the cost of the happy path, times the cost of the recovery path.”
The one-page template we use for every AI feature
Every new AI feature has a one-page doc: task definition in one paragraph, accuracy floor as a single number, fallback UI in two sketches, human-in-the-loop path as a diagram, data footprint as a bullet list. If any of the five is hand-waved, the feature is not ready to estimate.
More builds from the shelf.
Same team, different problems. Recent cases in adjacent industries — each shipped with the senior people who own outcomes.
Notes from people who shipped.
Real reviews from founders, CTOs and PMs we shipped alongside. Not curated soundbites — actual sentences from launch retros.
· Parsewise®
They rebuilt our entire platform in 4 months. Performance improved 3×, and the codebase is finally something our team can maintain on their own.
· Wishboard®
From zero to 50k users in 6 months. The team handled everything — design, development, and launch marketing. We just focused on the product.
· RLC®
We needed 5 senior engineers fast. They embedded with our team, matched our coding standards, and shipped features alongside our full-timers.
· Blured®
The AI agent they built handles 70% of our support tickets. Response time dropped from hours to seconds.
Before we get started — what teams ask us most.
With a discovery phase. We interview stakeholders, audit existing systems, and map the competitive landscape. You get a written roadmap before any code is written.
MANIFESTO
Two-week sprints. Senior engineers from day one. Code that reaches production, products people actually use, and a team that stays through launch.
Stop piloting. Start shipping.
A 30-minute call to clarify your next steps. Zero obligations — bring a brief, a deadline or a half-formed idea, leave with a written plan.
Book a call
A 30-minute call to map the brief, your deadline, and what would actually move the needle.
/02Get an offer
Written estimate within 48 hours — scope, team, milestones and a fixed price you can sign off on.
/03Pay & start
Sign the SOW, pay the first milestone, kick off the same week. No multi-month onboarding theatre.
/04Ship in 2 weeks
First working sprint goes live in 14 days — real demo on a staging URL you can hand to customers.