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Platform · Prove

Prove it on your own work.

Agents run in shadow mode first, scored against what your people actually did. Then the same workflow is measured by people, people with AI and agents, on time, quality and cost.

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NolliBenchmarks Weekly revenue review prep
MeasureHumanHuman + AIAgent
Time to a ready deckHuman2 h 30 mHuman + AI1 h 10 mAgent40 m
People’s timeHuman2 h 30 mHuman + AI1 h 10 mAgent15 m
Conflicts caught before reviewHuman2 of 3Human + AI3 of 3Agent3 of 3
Recommendation written byHumanA personHuman + AIA personAgentA person

RecommendationAgentAn agent prepares the deck. A person resolves conflicts and writes the recommendation.

Shadow mode and the live view

Try it on real work before it goes live.

An agent works alongside the real work, sends nothing, and is scored against what your people did. After go-live, you see how every agent and every team performs, and where a person stepped in.

Shadow mode before go-live.

An agent works alongside the real work, sends nothing, and is scored against what your people did.

Shadow run · Revenue review agentNothing sent

  • Sep 894%Missed one late renewal
  • Sep 1597%Matched
  • Sep 2299%Caught a conflict people missed
  • Sep 2998%Matched

Matched people on 97% of figures. Ready to go live.

Watch people and agents, live.

See how every agent and every team performs after go-live, and where a person stepped in.

Revenue review · liveLast 30 days

  • Agent4 decks prepared98% accepted · 40 min each
  • People4 reviews15 min each, down from 2 h 30 m

A renewal moved the total by 6%. The agent stopped and asked the lead.

Why your own work

Public benchmarks can’t tell you what happens in your close.

Two accounting studies from Mercor show why. Frontier models beat licensed CPAs on short, well-defined tasks, yet on long, realistic close tasks the best model got just 2.6% fully right on every attempt. Results depend on the work, so measure them on yours.

Guide: Why public benchmarks are not enough
100% vs 37%

Frontier models versus twelve licensed CPAs, on four short, well-defined month-end close tasks.

Source: Aden Barton, Mercor, October 2026

2.6%

Share of 160 realistic close tasks the best model got fully right on all eight attempts.

Source: Ramp Labs and Mercor, APEX–Accounting

Evals

Your evals become a company asset.

Every shadow run and every correction adds to a growing set of real cases and agreed answers: a record of what “correct” means at your company, exceptions included. Use it to compare models, improve agents and decide when an agent has earned more responsibility.

  • Real cases from your own work, the awkward exceptions included
  • Answers your experts agree are correct
  • Scores for the final output and the steps behind it
  • Re-run whenever a model, prompt or price changes

How shadow mode works

From shadow to live, one step at a time.

  1. Real work happensYour people do the work as usual.
  2. The agent runs alongsideSame inputs, same task.
  3. Nothing goes outIts output is kept for scoring, never sent.
  4. Scored against your peopleTime, quality and cost, case by case.
  5. Responsibility is earnedIt goes live where it passes, with a person approving risky steps.

Questions leaders ask

Good to know

Something we haven’t answered? Email the founders.

What is shadow mode?

An agent works alongside the real work without sending or changing anything. Its output is scored against what your people did, so you know how it performs before it goes live.

What is an eval?

An eval (evaluation) is a repeatable test that checks an AI system’s output against what “correct” means for a task, using real cases and answers or grading rules the team agrees on.

What is a personalized benchmark?

A personalized benchmark scores AI on your own work: your real tasks, judged by your team’s own definition of correct, and compared with how your people do the same work.

What does Nolli measure?

Time, quality and cost per task for the same workflow done by people, by people with AI and by agents, plus how often a person had to step in.

What happens after an agent goes live?

You see how every agent and every team performs, where a person stepped in, and whether results hold up over time, so problems surface early.

Keep going

Go deeper

Next · EquipEquip every person with what works.Your best people have already figured out how to use AI in their work. Nolli turns their know-how into skills, SOPs, PRDs and agents, shares them across the company, and shows which ones pass your checks.Explore EquipGuideHow to choose an AI model by cost per taskMeasure cost per task, not price per token, and find where a cheaper model does the same job.GuideWhat does AI-native actually look like?A plain definition, the five stages, an AI-native workday, and ten signs you are getting there.

Start with one team

See it on your own work.

Nolli maps one function, ranks its opportunities and proves the best ones, before you scale.

Request a demoEmail the founders