---
title: "The feature that finally went live."
description: "Studocu had an AI study feature it could not launch. We worked across product, AI engineering, and leadership to examine the experience, address the underlying issues, and help the team move forward."
url: https://coyotiv.com/work/studocu/
---

1. [Work](https://coyotiv.com/work/)
2. [Studocu](https://coyotiv.com/work/studocu/)

# The feature that finally went live.

Studocu had an AI study feature it could not launch. We worked across product, AI engineering, and leadership to examine the experience, address the underlying issues, and help the team move forward.

[Book a call](https://coyotiv.com/work/studocu/#book) [See all work](https://coyotiv.com/work/)

Client

Studocu

Sector

Education technology

Our role

AI product & engineering transformation

Today

Live, after product and leadership aligned

The story

1. [A product that needed more than another release attempt.](https://coyotiv.com/work/studocu/#a-product-that-needed-more-than-another-release-attempt)
2. [Shared direction, followed by a live feature.](https://coyotiv.com/work/studocu/#shared-direction-followed-by-a-live-feature)

## A product that needed more than another release attempt.

The study product generated notes, quizzes, and mock exams from learning material. But the feature could not get into production.

Testing the product as a student exposed a central problem: mock exams did not reliably reflect the uploaded document. Other questions involved the generation approach, the user experience, evaluation, and how the team owned the work.

## Shared direction, followed by a live feature.

Leadership supported an immediate experiment giving uploaded documents more weight and welcomed the preview approach. Product and leadership aligned around the changes, and the feature went live.

The work connected the behavior of the AI product with the decisions and responsibilities needed to move it into production.

How it went

## Work through the experience with the people building it.

1. Experience  
### Start with the student’s experience  
We tested the product as a student and brought specific failures to product and engineering. That made the discussion concrete: what the student expected, what the product produced, and where the two diverged.
2. Generation  
### Examine generation and evaluation  
With AI engineers, we reviewed context handling, generation design, and the tradeoffs between quality, latency, and cost. Recommendations included generating questions individually, validating them with bounded retries, and evaluating mock exams with input from educators.
3. Product  
### Connect product choices to engineering work  
We investigated experience gaps such as streaming display and course selection. We also discussed prioritizing uploaded documents and showing a five-question preview before generating a full exam.
4. Team  
### Address how the team worked  
The engagement covered development setup, team ownership, and AI-assisted development. We recommended small teams that could own the whole experience and worked with leadership and product on the direction.

The result

## Shared direction, followed by a live feature.

Leadership supported an immediate experiment giving uploaded documents more weight and welcomed the preview approach.

Aligned

Product and leadership aligned around the changes, and the feature went live.

Live

More work

## More work.

[See all work](https://coyotiv.com/work/)

Personalized products and fulfillment

### [A critical application, ready to run the business.](https://coyotiv.com/work/thefreshener/)

Months of continued development added features and integrations, allowing the company to expand its operations to new product types.

TheFreshener

HR technology and hiring

### [A hiring workflow that had outgrown a single agent.](https://coyotiv.com/work/klareda/)

Klareda could take on larger hiring use cases, with the work divided across agents and their steps available for review.

Contact

## Book a call.

Free, 30 minutes, with an engineer. Tell us what you are working on.

- Startups Free architecture review
- Enterprises AI review
- School Apply for the next cohort
[hello@coyotiv.com](mailto:hello@coyotiv.com)