There Was No Way to Prepare
No tool put goal setting, likely outcomes, and how to ask for them in one place. Everyone worked it out alone, from scratch, every cycle.
What it cost them
A career conversation walked into unprepared
Case Study · HR Tech · Performance Review Preparation
Kodexo Labs built Fairness Factor, a platform where an employee talks through what they want out of their performance review, gets a score for how likely they are to get it, and walks out with the legal brief, talking points, and negotiation playbook to go and ask for it.
of prep reclaimed per review cycle
per user avoided in legal consultation
prep documents generated in one flow


01 · Overview
An employee signs in and starts talking to Sidekick, the platform's advocacy agent. It asks the mandatory things first, position, review date, company, current pay, then asks what they actually want out of the review. A raise. A title. A different manager. Better hours.
For each goal the agent works through the factors that decide whether it lands and scores it. By the end the employee holds a Goal Attainability Report, six prepared documents, a rehearsal against an AI playing their manager, and drafted emails to send. Sessions pause and resume, and the Vault keeps every document and transcript.


02 · The Problem
A performance review decides pay, title, and the next two years of someone's career. The manager arrives with a form, a rubric, and HR behind them. The employee arrives with a hope. The specification names four problems.
No tool put goal setting, likely outcomes, and how to ask for them in one place. Everyone worked it out alone, from scratch, every cycle.
What it cost them
A career conversation walked into unprepared
Ask for 5% or 15%? Push for the title now or wait? Nothing turned the facts of the job, the policy, and past cases into a number.
Why it hurt
Career-defining asks made on a hunch
Rights, pay benchmarks, talking points, negotiation advice: pieced together from articles that knew nothing about their job, their company, or their manager.
Where it went wrong
Hours of research that fit nobody's situation
The first time anyone said the words out loud was in the real meeting, with the real manager, and the real answer on the line.
What was missing
One rehearsal, and it counted
No tool put goal setting, likely outcomes, and how to ask for them in one place. Everyone worked it out alone, from scratch, every cycle.
What it cost them
A career conversation walked into unprepared
Ask for 5% or 15%? Push for the title now or wait? Nothing turned the facts of the job, the policy, and past cases into a number.
Why it hurt
Career-defining asks made on a hunch
Rights, pay benchmarks, talking points, negotiation advice: pieced together from articles that knew nothing about their job, their company, or their manager.
Where it went wrong
Hours of research that fit nobody's situation
The first time anyone said the words out loud was in the real meeting, with the real manager, and the real answer on the line.
What was missing
One rehearsal, and it counted
03 · The Solution
The whole product is a chat on the left and whatever the chat is producing on the right. Pick a stage to see the screen the employee is actually looking at.

Stage 01
Prep never happens in one sitting. Sidekick greets the user by name, says which stage they left mid-way, and keeps every past session in the sidebar, ready to reopen.

Stage 02
The employee describes the outcome they are after. Known goals load an expert question set; unfamiliar ones are defined together, then scored with a general set and a stated lower confidence.

Stage 03
Sidekick asks the factors behind the goal one at a time, or hands over the whole set as a form. Add a factor of your own, weight it, and the estimate recalculates in front of you.

Stage 04
Achievements, KPIs, hard situations, strengths and gaps, plus how the manager communicates and who signs off. Uploads happen in the chat: the AI reads the file and pulls the answers out of it.

Stage 05
Policies, KPI documents, the position description, the training manual. Once loaded, the platform argues from the employer's own words instead of generic advice, and keeps them for later sessions.

Stage 06
Transcripts, uploaded files, generated reports, and a change log for every document edit, grouped by day. The log records what changed, when, and where in the conversation it was agreed.

Stage 01
Prep never happens in one sitting. Sidekick greets the user by name, says which stage they left mid-way, and keeps every past session in the sidebar, ready to reopen.
What You Walk Away With
Every document can be changed by talking to it, and every change is logged.
01
Your rights in your state, in plain words
02
What your role pays in your city
03
What to say, strongest evidence first
04
Answers for objections and delays
05
Questions about where you fit next
06
Your employer's form, already filled in
Role-play
Pick the moment you are dreading, set the tone, and run it as many times as you like.
Emails
The meeting request, the follow-up, the reply to a no, drafted, editable, ready to copy.
06 · How It Works
No dashboards to learn. One conversation carries you from stating a goal to walking into the review ready.
Tell it what you want out of your review: a raise, a title, better hours. It asks the follow-up questions that matter.
Your answers, plus anything you upload about your job and manager, turn into a plain likelihood score for that goal.
Legal rights, salary data, talking points, and a negotiation plan, written for your situation, not generic advice.
Practice the real conversation against an AI playing your manager, then walk in with everything ready.
The score updates as you add more detail, and nothing about your review sits in someone else's inbox: every document, transcript, and edit stays in your own Vault.
07 · Outcomes
Real time saved, real money saved, better odds at the table. This is what a well-built AI product can do for your users, and it can be built for yours too.
Reclaimed per review cycle
Six prep documents generated in one flow instead of assembled by hand from scratch.
Legal cost avoided per user
A state-specific legal brief answers what most people were paying for consultation time to hear.
Scoring confidence from Enhanced Factors
Letting users add their own weighted factors closes most of the gap on goals with no expert set.
Sessions per user, per cycle
Up to seven goals does not fit one sitting. The Vault is what makes coming back cheap.
Higher follow-up survey completion
Reminders start the day after the review date and repeat every three days until answered.
Of sessions produce a document actually used
One-click copy on every draft, and a change log behind each edit the user agreed to.
FAQ
The questions that come up whenever advice has to be personal, scored, and defensible.
The agent works with the user to define the goal clearly, then uses a generalized factor set to score it. That score is shown with a disclaimer stating it has lower confidence than a goal with expert factors behind it. The system says what it does not know rather than pretending.
It is a weighted calculation over factors that human experts defined and weighted, not a number the model invents. When the user adds factors of their own, they assign the weight and the engine recalculates with a dynamic weighted average, which is why both scores are shown side by side.
Yes, by discussing it. They highlight a passage, talk it through with the agent, and once they accept a change the document is rewritten. Each edit is written to a Document Log with a timestamp and a link back to the point in the conversation where it was agreed.
The stage, the documents, the uploads, and the transcript are stored. On the next sign-in the agent greets them and says where they stopped. The same path covers adding or changing a goal after the goal stage was already finished.
PDF or DOCX up to 10 MB. The AI extracts answers to the mandatory and factor questions from the file, stores it in the user's Vault, and makes it retrievable for the rest of the session. Oversized or unsupported files are refused with the accepted formats listed.
Because there is a limit to how many outcomes one person can pursue in a single review. The cap keeps the prep focused and the scoring meaningful rather than producing a list nobody can act on.
Twenty minutes with the team that shipped this. Bring the expertise you want scored and delivered, and leave with a straight answer on what it takes to build.
Products Shipped
Across 25+ industries
Client Retention
Across every engagement
Rated on Clutch
Elite AI Firm designation
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