Zevian
Weekly performance scoring that managers trust, live with paying customers
Managers write the criteria. Zevian scores every weekly report against them and ties each score to a quote from the report. I founded it and directed the build, so every product and design decision below was mine.
4.4 / 5
Average usefulness, across 5 manager interviews
4 of 5
Managers willing to try it at the price I tested
2
Paying customers, from direct outreach
- Role
- Founder. Product, design, data model, prompts, and sales
- Team
- Solo
- Platforms
- Web app, multi tenant
Three decisions that shaped the product
- 01
I tested the price with real managers before I built onboarding or marketing.
- 02
Every score points to a quote from the report. A manager can change any score.
- 03
The assistant only answers from the reports it retrieved. If the reports do not cover the question, it says so.
One team score, one reporting bar, and the numbers each agent hit against target.
Chapter one
Problem, strategy, and process
CRMs show deals moving. HR tools run reviews twice a year. Nothing tracked the weeks in between. Managers scored from memory, employees got vague feedback, and a bad hire went unnoticed until month four.
A new hire who fails costs a US sales team about $15K to $20K before the problem is obvious.
Two discovery interviews, four follow ups, and a survey with 16 responses. The problem was worst on teams of 15 or more.
Strategy
One question mattered more than the rest: would a manager pay to have their team scored. Until that had an answer, anything I spent time on was a guess. So I tested price first, shipped a plain interface early, and spent design time only where managers got stuck.
Validate demand first
I tested $8 per user per month during discovery, before onboarding or marketing. Four of five managers said they would try it.
Keep the interface cheap to change
I shipped rough screens on purpose. Every rebuild came from watching someone use the product, not from polishing alone.
Almost nothing in the product is a first draft. The loop was the same every time: ship it, watch someone use it, rebuild it.
Rebuild indexThe full saw / changed list, kept out of the main reading flowShowHide
| Area | What I saw | What I changed |
|---|---|---|
| Weighting | Managers had to make their percentages total 100 themselves | Importance levels |
| Criteria | Judgment criteria only. A customer wanted his team's reported numbers next to the scores | Number criteria |
| AI context | Three separate fields. Managers asked which one changed the score | Project Memory |
| Dashboard | Status flags on every row and four KPI cards that looked the same. Nothing said what to check first | One main number |
| Employee dashboard | One Overview tab, a color per KPI, reasoning one criterion at a time. Seeing if a person improved took clicking | Five tabs |
| Badges | Improving and Aligned on every row, so the badge on the person who needed attention did not stand out | Exceptions only |
| Assistant | A call center manager pasted his team's numbers into ChatGPT and acted on the answer | Scoped assistant |
Each change links to the section that covers it
Chapter two
Manager workflow
A KPI is a goal, written instructions, and a set of weighted criteria: a scoring rubric. Every score in the product is measured against that definition.
Weighting
Problem
Managers typed a percentage for each criterion. Making the numbers reach 100 took more thought than the work itself.
Decision
Split KPI creation into two steps: define the KPI first, then add the scoring rubric. Presets and the AI builder help managers start from a role instead of a blank form.
Why
It asks managers to decide one thing at a time: what success means, then how each report should be scored. Importance levels still set the weight automatically.
Tradeoff
Exact control is gone. Nobody has asked for it back.
Before and after: one long form became a two-step flow with presets, AI-assisted setup, and criteria added after the KPI details.
Number criteria
Problem
It shipped with judgment criteria only. A customer wanted the numbers his team already reported shown next to the scores. He wanted to compare the two.
Decision
Number criteria carry a target, a unit, and a direction. The system checks the number against the target. The AI does not score them.
Why
He was right. A manager who reads one number, or the same number across a few weeks, already understands it. An AI in the middle adds a step and a reason to doubt the answer.
Tradeoff
Two criterion types to learn, and someone has to keep the targets up to date.
Which type fits depends on the team. Sales floors and call centers run on numbers and on catching a pattern early. Work that is harder to count stays on judgment criteria. Most teams use both.
Project Memory
Problem
Three separate fields: scoring instructions, project description, and knowledge base. Managers kept asking which one changed the score.
Decision
Merge all three into one plain text box. Memory only updates when the manager presses Update.
Why
The three fields came from how the system was built. A manager does not think in those parts. Manual updates mean every change to scoring is on purpose.
Tradeoff
Less structure for the model. In exchange, the manager knows what they are editing.
One box for everything the AI reads.
Score guard design
Problem
An AI that rewards polished writing teaches employees to polish reports and stop there. The product would create the problem it was sold to fix.
Decision
Rules that treat writing as a claim that needs proof.
Why
Every score has to point at something the person wrote. That is what makes it hold up in a review.
Tradeoff
Someone who writes briefly but did good work scores lower than they deserve until they add details.
Anti-gaming rulesHow the score guards against polished or repeated reportsShowHide
- Pattern detection — templated reports: the same report submitted each week with different wording.
- Prompt detection — entering an instruction as-is with a few changes.
- Vague claims — scored less.
What is in a report
The two criterion types are shown differently. The pattern flag looks across several weeks.
Pattern flagged, numbers checked against target, and judgment scores anchored to report evidence.
Manager dashboard
Problem
Status flags on every employee row, then the same numbers again across four near identical cards. Nothing said what to check first.
Decision
Move each trend into the card it belongs to. One main number with its trend line inside it, and every report status in a single bar.
Why
A manager opens this page with one question: is anything wrong this week. One number and one bar answer it. The team list below shows which person.
Tradeoff
Detail moved a click away, which is why I built the scoped assistant below.
Before
After
Manager override
Problem
The AI will be wrong sometimes. A manager who cannot change a score will not trust it with people decisions. A person has to decide.
Decision
Managers can change any score. A reason is required, and both numbers stay in the record.
Why
After eight adjusted reports, the pattern says as much about the manager as the employee: Likely Inflated, AI Aligned, or Underscoring.
Tradeoff
Every override takes extra effort. That effort is what makes the record worth having.
No reason, no override.
Scoped assistant
Problem
A call center manager pulled his team's numbers out of the system and pasted them into ChatGPT or Claude. He asked which of his people were falling behind. It answered with confidence, it was sometimes wrong, and he made decisions about people based on it.
Decision
Build the assistant into the product and scope it to one source: the reports the team submitted, filtered by group and date range.
Why
A general model answers anything, which is what went wrong. Retrieval augmented generation limits this one to reports that exist, and it names them under every reply.
Tradeoff
It refuses far more questions than a general model would. Refusing is better than guessing about a person.
Suggested questions it can answer.
The search step is shown as it happens.
Annotated: the scope filter, the reply, and the reports it drew from.
An answer from retrieval augmented generation needs three parts:
- —What it was allowed to read
- —The answer
- —The sources, naming every report the answer came from
Chapter three
Employee trust and system
Problem
A scoring product only works if the people being scored accept it. If it feels like being watched, they write for the AI or stop caring.
Decision
Employees see the same numbers the manager sees. They see the criteria and weights before they start writing. A draft can be scored and revised before it is submitted.
Why
Scoring a draft lets someone fix the report against the criteria before anyone else reads it. That is why employees started using it.
Tradeoff
Showing the criteria means people write to them. The anti gaming rules mean the only way to score well is to do the work. The reason behind a manager's change stays private, so nobody can work out the gap between AI and manager.
The criteria and weights sit above the writing box.
Employee dashboard
Problem
One Overview tab held everything, and every KPI had its own color. The reasoning for a low score opened one criterion at a time.
Decision
Dedicated tabs, one shared KPI color, and a six-period table with color only where attention is needed.
Why
The table answers the main question: is this person improving. No clicking.
Tradeoff
Six periods is a guess. Long enough to show a trend, short enough to fit on screen.
Annotated: the old overview tab next to the rebuilt employee dashboard.
Badges
Problem
Every row carried the same two green badges, Improving and Aligned. The badge on the person with low scores did not stand out. Improving was measured against an all time low, so almost everyone qualified.
Decision
Reserve badges for exceptions, and measure Improving against recent scores. A filled badge is a fact about the person, an outline badge is a trend from their scores.
Why
The color system was already set: green good, amber caution, red critical. My first instinct was to give every badge its own color, which would have broken that. A badge is only readable if it is rare.
Tradeoff
A row with no badge says nothing, so a manager has to trust that no badge means no problem.
Before
After
Shipped: 28 people, 2 badges.
System
The scoring model sits on a multi tenant data structure. KPIs are stored as weighted criteria, and each one is scored a criterion at a time against the manager's own instructions.
The data model behind scoringMulti tenant structure and the scoring schemaShowHide
Built for many companies at once
- —Every employee, project, KPI, and report belongs to one organization
- —A three layer role based access structure decides who sees which data. Managers at different levels only see their own teams
KPIs stored as weighted criteria
- —Each score is saved one criterion at a time, with its own instructions, grouped under a KPI
- —That is what lets one skill be tracked over time, for the company and for each person
AI that scores evidence
- —Every score is tied to a quote from the report and to the manager's own instructions
- —Nothing scores well on writing quality alone
Organization → Employees → Projects → KPIs → Reports → Criterion Scores
Outcomes
What shipped
4.4 / 5
Average usefulness, across 5 manager interviews
4 of 5
Would try it at the price I tested
2
Paying customers, from direct outreach
Live
Scoring, overrides, onboarding, notifications
The gap is usage data. PostHog tracking is going in now. Funnel and retention numbers will sit here once there are enough customers for them to mean anything.