Case study, careers
Averil.
A career operating system for the person doing the work.
What this proves for client teams
A live product with documented product boundaries, evaluation criteria, and a maintainable operating model.
The problem
Career advice is written for whoever is paying for it.
Many products in this category are funded by employers and optimize for placement. Others focus on courses, certificates, or résumé services. Averil is designed to give the candidate a current view of market demand and practical next steps.
So the person doing the work is left guessing at two separate jobs at once: staying employable in the role they have, and building toward the one they want. Nothing on the market treated those as one system.
The product brief: help someone understand their current market position and identify practical next steps.
What we built
Four connected product areas address the main questions identified in user research.
Standing
Current position
A read on the user's current skills against what their field is hiring for, stated plainly, including the parts that are weakening. There is no vanity score and nothing to collect.
The gap
Between now and next
The distance between the job they hold and the one they're aiming at, broken into work that fits around a full-time role.
This month
Small, real moves
A short list of actions with a reason attached to each. If a recommendation can't be justified in one sentence, it doesn't ship.
What it won't do
Designed-in refusals
The product does not submit applications on the user's behalf, sell employer access, or resell candidate data. These boundaries shape both the product and its revenue model.
How we worked
Four phases. The product only got its name at phase three, which is our rule.
Study
Interviews with people mid-career, plus a read of what the incumbent tools optimise for and who pays them.
Prototype
A single screen answering one question, put in front of real users before any account system existed.
Name & ship
It survived users, so it earned a name, a domain, and a roadmap. Averil went live on averil.ai.
Run it
The build team operates it. Every support thread lands with the people who wrote the feature.
Stack & architecture
The system uses four replaceable layers, with evaluation retained as a core requirement.
Layer: signal
What the market is asking for
Ingest and normalise role requirements over time, so "in demand" is a trend rather than a snapshot.
Layer: profile
User controls
A structured skills and work-history model that the user can review and edit. It is not resold to employers.
Layer: reasoning
Model work, tightly scoped
The model compares, explains, and drafts. It doesn't decide anything the user can't see the reasoning for.
Layer: evaluation
Guardrails before features
Recommendation paths include cases where remaining in the current role is the appropriate outcome. Results that do not meet the evaluation criteria are not released.
Named technologies per layer are deliberately left off the public write-up.
Screens
The product as it stands today, straight out of the running build.
Averil, the standing screen
Averil, the gap view
Averil, this month's moves
Outcome
What's true today, with the figures reported as they're confirmed.
In production
Averil is live at averil.ai, run day to day by the team that built it.
Commitments kept
Zero employer seats sold and no candidate data resold, through every revenue conversation so far.
n/a
People using Averil
Figure pending publication.
n/a
Weeks from prototype to live
Measured from phase two to phase three.
n/a
Retention or return rate
Measured over rolling ninety days.
Founder's note
The hardest part wasn't the model. It was agreeing, in writing, to the things Averil would refuse to do, and then not quietly walking them back when revenue asked.
What we'd do differently
Three decisions we changed after testing the first version.
Ship the refusals earlier
The "won't do" list was written after the first build, not before it. Next time it's part of the prototype brief.
Fewer surfaces at launch
Two of the early screens existed because they were easy to build, not because anyone asked for them.
Measure the advice
Engagement told us nothing useful. What matters is whether a recommendation changed something in someone's actual career.