Built for
Industry: HR Tech · Delivered: Multi-Agent Negotiation Coach Platform
Predicted Negotiation Outcomes 80% of the Time for Fairness Factor
AI workplace advocacy platform that helps employees walk into performance reviews, raises, and tough conversations prepared. Multi-agent coaching with outcome prediction and on-demand talking points.
80% PREDICTION ACCURACY • 30% MORE CONFIDENCE (PROJECTED) • 35% BETTER REVIEW OUTCOMES (PROJECTED) • LIVE IN PRODUCTION
The problem
Employees walk into reviews without a plan.
Industry pain. Most employees prep the night before, guess at talking points, and have no idea how the manager will respond. The result is missed raises and awkward reviews.
Fairness Factor's solution. Every employee gets a personal AI advocate that scopes the conversation, predicts how it is likely to go, and hands over the talking points to walk in prepared.
Your employees stop guessing and walk in with a plan.
An AI workplace advocacy platform: a personal coach for every conversation, outcome prediction grounded in the employee's situation, talking points and scripts on demand, and role-play before the meeting.
In one screen
Three signals that turn a stressful review into a conversation the employee leads.
One screen shows it all: multi-agent orchestration reads the situation, outcome prediction hits 80% accuracy before the meeting starts, and talking points generate on demand.
Walk-in-ready scripts
Talking points and scripts written for the conversation in front of you.
The platform writes the opening line, the response to pushback, and the follow-up email. Every script is grounded in the employee's work history and the company's voice.
Rehearse before the meeting
Role-play the hard moments before they happen.
The simulator plays the manager. The employee practises the opener, the rebuttal, and the close, then gets coached on tone and pacing.
Solutions we provided.
Predicts negotiation outcomes.
80% accuracy on outcome prediction before the meeting starts. Measured prediction accuracy on negotiation outcomes across pilot deployments.
Org-level data isolation by design.
Every employee's data stays inside their organisation's boundary, no cross-tenant leakage. Data isolation is built into the platform architecture.DISC personality framing.
Talking points adapt to how the manager on the other side reads situations.
Generates talking points and follow-up scripts.
Openers, rebuttals, and post-meeting emails for the case at hand. Every script is grounded in the employee's work history.Multi-agent coaching.
Specialised AI coaches scope the goal, the manager, and the company voice. Six specialised subgraphs run against the employee's history.
Considering an AI advocate or coaching platform for your team?
30 minutes. Bring the conversations your people dread. Walk out with an architecture and a build plan that ships in eight weeks.
Our process
Four stages from brief to live deployment.
Discovery
Design
Build
Deployment
Fixed-scope MVP. Eight-week engagement from kickoff to production, with multi-agent coaching, outcome prediction, and document generation live at launch.
Our partner
People don't leave companies, they leave managers. Fairness Factor builds common ground between employees and managers, AI-powered.
What's inside
Built on a production-grade stack.
A team of AI coaches predicts the outcome, drafts the talking points, and runs the role-play. Every company's data stays isolated by design.
Figure 1. Fairness Factor AI workplace advocacy pipeline. FastAPI on Python 3.12 routes each request through a LangGraph multi-agent graph (six specialised subgraphs), with Claude 3.5 Sonnet as primary reasoning and OpenAI GPT-4.1 as fallback. Qdrant hybrid retrieval and Mem0 supply org-isolated context, MongoDB persists agent state, hosted on AWS.
The Results
From a guess at the night-before talking points to a plan that holds up in the room.
Before
What employees did before.
Rehearsed alone the night before. Guessed at talking points. Walked in blind to how the manager would respond.
AFTER
What they do now.
The AI coach scopes the situation, predicts the outcome with 80% accuracy, and generates the openers, rebuttals, and follow-up emails. The employee role-plays it first, then walks in ready.
Prediction
Outcome prediction accuracy
Confidence
More negotiation confidence (projected)
Review outcomes
Better performance review outcomes (projected)
Status
Multi-agent platform in production
Frequently asked
It is measured prediction accuracy on negotiation outcomes across pilot deployments. Six subgraphs run against the employee's history, the company's voice, and DISC framing for the manager.
Ready to ship an AI coaching or advocacy platform of your own?
30-min call. We will walk through the conversations your people dread, your timeline, and what a fixed-scope build looks like.
Related Work
A few more products Kodexo Labs has designed, built, and launched on schedule.
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