The measurement layer for
workforce value.
Your people data tells you who and where. It can't tell you where they create the most value. EverGain reveals it, from the data you already have, and converts the gap into recoverable value, without layoffs.
EverGain in five minutes.
What we measure, how the matching works, and what a pilot delivers.
Every leader is asking how to get more from their people.
The data to answer it is new.
AI has put a hard question in front of every CHRO and CFO: are we getting more for our people investment, or less? Many firms moved fast on AI, then found some work is still done better and cheaper by a person. The answer sits in one place no system looks: where your people's skills are, compared with where the work is.
One measure.
Six answers.
From the data you already have.
Is our talent focused on the right problems?
Without data on who does what best, work goes by title, seniority, and who is free.
Where are our skill gaps? Train or hire?
Titles can't show what people can actually do. The work itself shows what you're short of.
Is AI giving us more for the money?
Many firms adopted AI fast, then found token costs piling up.
Is our people spending paying off?
AI is judged by logins. Training is judged by attendance. Neither shows whether the work got better.
Can reviews and pay rest on evidence?
Annual reviews run on manager memory. EverGain adds what the work shows.
Can our managers see what's working?
Good leadership shows up in other people's work.
When skills and work don't line up,
the firm pays for it and can't see it.
Your HR systems record pay, headcount and tenure. Your work systems record tickets and tasks. None of them put the two side by side. EverGain does, from the data you already have.
IllustrativeThen EverGain reassigns within the team. Same people, same work, same headcount. People move between workstreams; nobody changes level, nobody is hired, nobody is moved out.
Fit rises from 82% to 90% of the work covered at the right level: 13 more units done by someone at the right level, with no one added. The 17 units still short, mostly at Levels 1 and 2, are the gap to hire or train for. Illustrative; cells are units of work.
In four real operations, the model estimates better assignment would have saved
5.6% to 19% of hours.
Measured on four public work logs. None was collected for EverGain; we ran each after the fact. Savings are modeled estimates, tested on data the model hadn't seen, with each person held between 70% and 150% of their current load. Not figures realized in a live deployment. In a pilot, we measure it on your data.
Your work records already show who did what, and when.
EverGain works out the type of work and how hard it is. No surveys. No self-reports. Evidence to support manager judgment.
Who. Task. Level. Type. Time.
Three come from your system of record: who, task, time. Two are classified by EverGain from those records: level and type. The person is an encrypted ID. No content is read.
Any system that records who did what, at what level, over time will do: ServiceNow, Salesforce, Workday, GitHub.
From raw records to a dollar figure.
What the data shows.
A better match exists.
A is better suited to this work, but B gets it. The task takes longer and costs more.
Work that bounces.
It passes from person to person before it finds the right one. Every handoff adds time.
Strong people on work below their level.
It looks like good performance. It's untapped skill, and these are the people at risk of leaving.
People on work above their level.
It can look like low effort. Often it's a fit problem. Long hours, rework, then burnout.
Designed to hold up under scrutiny.
Hard to game.
Built-in checks spot people chasing the numbers instead of the work.
Stays current.
The numbers update as the work happens and are checked for going stale.
Not a replacement for managers.
It adds evidence to their judgment. Every recommendation is logged, so you can see what worked.
Your systems store the work and rate the people.
None of them read skill from the work itself.
Most firms already pay for an HR system, an assessment tool, a review platform and a dashboard. Each does its job. None of them work out skill from the work people actually do, and compare it with what the work needs.
EverGain doesn't replace these systems. It reads the work they already record.
HR & people systems
Record pay, tenure, headcount, org structure. EverGain reads the work they log.
Assessment & review tools
Rate people against competency frameworks. EverGain adds what the work actually shows.
Work systems of record
ServiceNow, Salesforce, Workday, GitHub. Five fields is all EverGain needs.
Dashboards & analytics
Show activity, throughput, utilization. EverGain adds skill-to-work match as the new dimension.
One measure.
Six answers.
From the data you already have.
Is our talent focused on the right problems?
Without data on who does what best, work goes by the best signals available: title, seniority, experience, who is free. That's reasonable, and it still misses a lot. In the first function we measured, 45.9% of tickets were passed between people at least once. The model estimates better assignment would have saved 5.6% to 7.4% of hours. Three more work logs since, in three other operations, show the same pattern. The work still got done, so the gap stayed hidden.
What EverGain shows:
- Who is best at each type of work, at each level of difficulty.
- Where each task should go first, so it stops bouncing.
- Who has room to grow, and who is stretched too thin.
- The dollar value of better matches.
Where are our skill gaps? Do we train, or hire?
Most firms answer this with job titles and market benchmarks, because that's the data they have. Titles can't show everything people can do. The work itself shows what you're short of, what you already have, and who could close the gap.
Short
Where more work arrives than your people can handle at that level. Visible as backlog, escalation, or handoffs.
Unused
Where your people are stronger than the work they're getting. Invisible until someone leaves.
What EverGain shows:
- Where you're short, by type of work and level of difficulty.
- Where you already have the skill but it's going to the wrong work.
- Who inside the team could close each gap, with training.
- Where hiring is the only realistic answer.
Build the right team
Teams picked by reputation often share the same strengths and the same gap. EverGain builds teams that cover every skill the work needs, and sizes hard projects to the right number of people and the right mix.
TestedIs AI giving us more for the money?
Most firms measure AI adoption by logins, usage rates and token spend. None of those tell you whether the work got faster or better. EverGain shows where AI is changing the skill mix required and whether the cost-per-unit of work is falling.
Where AI is working
Work that used to need a Level 3 now closes at Level 2. The cost-per-ticket falls. The pattern shows up in the data.
Where AI is hiding a problem
Token costs add up. The work gets routed through an AI tool and then back to a person anyway. That's three touches instead of one.
Illustrative: time split on a single ticket
What EverGain shows:
- Where AI is genuinely reducing the skill level needed.
- Where AI adds a step instead of removing one.
- The cost-per-unit-of-work, before and after AI adoption.
- Which workflows are AI-ready and which aren't.
Is our people spending paying off?
AI is judged by logins. Training is judged by attendance. Neither tells you whether the work got better. EverGain shows whether skill levels moved after a training program, and whether the change held.
The question isn't whether people completed the program. It's whether they are now handling harder work, more cleanly, than before.
What EverGain shows:
- Whether measured skill levels moved after each training investment.
- Which programs produced a lasting change, and which didn't.
- Where to target the next training budget.
- The cost-per-skill-point-gained, across programs.
Can reviews and pay rest on evidence?
Can managers see what's working?
Reviews and pay
Annual reviews run on manager memory, with a long feedback form and a short conversation. EverGain adds a third input: what the work shows. It doesn't replace the manager's judgment. It adds the one thing that's been missing: evidence from the work itself.
Managers and leaders
Good leadership shows up in other people's work. A manager's team gets harder work done, at a higher rate, with fewer handoffs. That pattern is visible in the data, and it's a more reliable signal than a 360 survey.
What EverGain shows:
- The hardest work each person handled, over the review period.
- How their skill level moved, in each type of work.
- Where they are relative to their peers, without a ranking system.
- A consistent, auditable record, with the same standard for everyone.
- Whose teams are improving, and at what rate.
- Where skill is concentrated and where it's thin.
- Which managers develop people and which don't.
- The team's coverage of the work it's assigned.
Built for the people who own the decision.
For the CHRO
- A skill map of the whole function, by type of work and level of difficulty, from the data you already have.
- Evidence of where training moved the needle, and where it didn't.
- A consistent, auditable record to support reviews and pay decisions.
- Which managers are developing people, and which teams are thinning out.
- The hours-saved figure to take to the CFO.
For the CFO
- The dollar value of better assignment, in one function, before any new tools or headcount.
- A cost-per-unit-of-work measure that holds across AI, training and hiring decisions.
- Evidence that people investment is moving skill levels, not just attendance numbers.
- A measurement layer that works from data you already pay for.
In four real operations, the model estimates better assignment would have saved
5.6% to 19% of hours.
EverGain ran against one full year of ServiceNow data from a real IT operation: 24,260 tickets, 217 people. The data wasn't collected for EverGain. We ran the model against records that were already there. Savings were validated on data the model hadn't seen during training. 45.9% of tickets passed between people at least once. Better assignment would have cut that, and the hours with it.
Since then the same model, with the same checks, has run on three more public work logs: Volvo IT incidents (7,554 tickets, 2010 to 2012), the LSST Data Management engineering backlog (9,000 issues, 73 engineers, 2014 to 2020), and the loan-application workflow of a Dutch bank (213,000 work stints, 113 staff, 2016). Modeled savings at realistic capacity limits: 11% to 19%, 7% to 15%, and 7% to 11%. In every log, the differences between people were 1.4 to 2.7 times what chance alone produces.
That's work already paid for, freed up for growth without new hires. The savings are model-implied and tested on held-out data. A pilot on your data turns the estimate into a measurement.
The result holds under pressure.
The variance of measured productivity between people, divided by the variance when names are shuffled across the same tickets at random, averaged over 50 shuffles. One year of ServiceNow records. Real against shuffled: 491 to 1. The skill signal is not noise.
Robustness checks passed in simulation. The method holds under different assumptions.
Of the difference between people is the shape of their strengths. One score per person throws that away.
Tested on real data.
Four public work logs across four operations, classroom cohorts and simulations. Not a curated demo.
Privacy-preserving by design: no names, no content, encrypted IDs only.
People appear only as encrypted IDs. No names, no content. How we handle your data.
Patents pending.
8 provisional filings. The method is documented, reproducible, and defensible.
The full method is shared under NDA.
We do not publish the methodology. To see it, request an NDA by email. We send the methodology once the NDA is signed.
Request the NDA by emailOr write to abhra@evergain.io
Your data stays yours.
- You remove the names before anything leaves your firm.
- You keep the key. We never see it.
- We read no content. No text, no emails, no documents.
- We install nothing on your systems.
- Your data is used for your pilot only. It is not pooled, shared, or used to train anything.
- We delete everything when the pilot ends, including our results.
- Every pilot runs under an NDA and a pilot agreement.
The full pilot data guideline is available on request.
This site sets no cookies and runs no analytics. The contact form sends what you type to abhra@evergain.io through formsubmit.co, a form delivery service. We use it only to reply to you.
Questions from your HR, legal or IT team go to Abhra Roy, Founder and CEO.
Ask about data privacyOr write to abhra@evergain.io
EverGain is the measurement layer for
workforce value.
We measure where a firm's skills are, compared with where its work is, from the data the firm already has.
Founder
Abhra Roy taught economics at Kennesaw State University for 22 years. In 2026 he gave up tenure to build EverGain.
He is a game theorist. His papers study strategy: how governments, firms and media outlets act when each is guessing what the others will do. Each paper uses a simple model to reach a result most people do not expect.
EverGain comes from the same way of working. Firms record the work their people do. They do not measure the skill behind it. Abhra designed the method that does, and he is the sole inventor on all eight patent filings.
He has tested it on four public work logs across four operations, a quarter of a million work records in all. See the evidence.
Outside work he is a street photographer and writes poetry.
Computational physicist. Ph.D. in Physics from the University of Georgia, with research in first-principles materials modeling and large-scale numerical simulation. At EverGain she runs simulations on very large datasets. B.Tech in Electronics and Telecommunication and B.Sc. in Physics, University of Calcutta.
Mechanical engineer turned marketing academic. Worked at Tata Motors and Mercedes-Benz before an MBA and Ph.D. from IIM Bangalore. Has taught for 22 years. B.E., Jadavpur University.
Professor of Quantitative Analysis, Coles College of Business, Kennesaw State University. Works on dynamic and stochastic models for decision-making under uncertainty, including workforce planning. Ph.D. in management science, Ivey Business School. Former professional engineer.
Associate Professor of Economics, Coles College of Business, Kennesaw State University. Game theorist; research in contest theory, sports economics and behavioral economics. Ph.D. in Economics, University of Connecticut. Has consulted on tax policy.
Thirty-year operator. At The Coca-Cola Company he was VP of Global Innovation and General Manager of the natural sweetener program that launched Truvia, leading a 75-person global team. Earlier ran the $260M Biogums business at NutraSweet and Monsanto. Since 2011, Partner and Innovation Practice Leader at Growth River Consulting, working with executive teams at Cox Enterprises, Edward Jones and Kennesaw State University.
Over 40 years in investment management and strategic business consulting. Founded Gray & Company, a national investment advisory firm that grew to more than $12 billion in assets under advisement, and sold it after 25 years. Earlier held senior roles at Smith Barney, Oppenheimer & Company and Morgan Keegan. Has served on professional and civic boards, including the Goodman Theatre in Chicago, the Alliance Theatre in Atlanta and the Rialto Center for the Performing Arts in Atlanta, where he was chairman. Georgia State University, with postgraduate work in financial analysis.
See what your data says.
Give us one function's work data. In weeks, not quarters, we quantify the opportunity behind each of the six questions, in hours and dollars where the data supports it. No new tools, no surveys, no layoffs.
Get in touchSee what your data says.
Give us one function's work data. In weeks, not quarters, we quantify the opportunity behind each of the six questions, in hours and dollars where the data supports it. No new tools, no surveys, no layoffs.