Mozzie gives each person at your company a private AI agent that learns from their corrections. When someone fixes a mistake, the fix is checked, saved and tested on your real work, so nobody has to correct it twice.
For teams that rely on expert judgment. We're starting with hedge funds and prop shops, and taking on our first design partners.
Maya's agent
Exposure by sector, end of Q3
Tech 31%, Energy 18%, Financials 14%
Source: prices_v1Use prices_v2, v1 is out of date. Maya
Example. The people and numbers are made up.
What happens next
Her desk lead approves the fix.
It applies to the risk team and nobody else.
It becomes a test, so every new model and change has to pass it.
Next week Sam asks the same question, and the agent gets it right.
Example CapitalAgent version 12Agent version 13Illustrative example
Maya, risk analystExposure by sector, end of Q3?
Maya's agent · Frontier model (auto)Tech 31%, Energy 18%, Financials 14%
Source: prices_v1
Maya, risk analystv1 is deprecated. Use prices_v2.
Correction
RuleUse prices_v2, not prices_v1
SourceMaya, exposure report
Applies toRisk team only
ReviewWaiting for desk leadApproved by desk lead
Approve
What Maya's agent knows
Firm policy
Risk teamUse prices_v2
Maya's preferences
Private test caseGuards against this mistake coming back
Your team's conversations
Exposure by sectorReviewed
Summarize this filingReviewed
Compare these two fundsReviewed
Fix the pricing scriptReviewed
Your private benchmark
Analysis
Documents
Research
Code
Only reviewed, permitted work gets in.
New caseHeld-out cases from your benchmark
Current setup
With the correction
One case still fails. It stays visible, not averaged away.
Released as version 13Roll back
This week's agent workNobody complained
Exposure reportAccepted
Client summary, MondayAcceptedRewritten by hand
Fund comparisonAccepted
Client summary, ThursdayAcceptedRewritten by hand
What it found
PatternSummaries keep missing fees
Likely causeFees source isn't connected
Suggested fixConnect the fees source
On a safe copyFailure gone
ReviewWaiting for desk lead
Nothing goes live until it's tested and reviewed.
Your benchmark, by task
FrontierMid-sizePrivateNew model
Analysis
Documents
Research
Code
This task
Summarize a filing
Mid-size modelChosen automaticallyChange
Frontier model
Mid-size model
Private model
New model
Where the spend goes
Within budget
Context
Retries
Output
All her work
Analysis
Research
Documents
CodeBuilt in
Maya's agent
One person, one agent
Knows her work and her team
Remembers across every tool
Talks to every sub-agent
Her sub-agents
Market terminal
Spreadsheets
Team chat
Internal portal
Claude Code, Cursor, Codex
+ Add a tool
The firm's CRM
A sub-agent sees only its own tool.
Prefer a coding tool? Its work still reaches her agent.
Have a tool that could help? Connect it too.
MayaAsk me if you're ever unsure.
Maya's agentGot it. I've added that to how we work.
How Maya's agent works with her
Answers inTables
Shows sourcesAlways
Asks Maya questions8 of 105 of 10
Added by her agentChanged by Maya
Client reports need approvalFirm rule
Settings never override firm rules.
Maya's weekPrivate to Maya
Reports finished12
Where you and I differedRounding
Your answer last weekFixed 3 mistakes
Next week, I suggestCheck limits first
Two quick questions
Exposure reports: one decimal or two?
OneTwo
Should limit breaches go at the top?
YesNo
About two minutes, and it's optional.
Your servers
Agents
Memory
Test cases
Open-source modelSensitive work stays here
Approved cloud modelOnly permitted work
Unapproved endpoint
Blocked: not approved
Send the risk report to the PM?
ApproveApproved once, for this report
Paused here. Resumes without repeating a step.
Tests ran on your servers
Scores approved to share
The test cases stay here.
MozzieSees scores, not your data
Maya's agentRisk team
Shared statusExposure report finished
Tom's agentHead of desk
Tom: What did Maya finish?Answered from the record, without interrupting her.
Credit teamReuses the tested check
Research teamWalled off, sees nothing
Sam, risk analyst · in Claude CodeExposure by sector, end of October?
Sam's agent · Mid-size model (auto)Tech 29%, Energy 19%, Financials 15%
Source: prices_v2Right first time
Corrected once.
Mozzie
Illustrative example. The firm, people, data and results are synthetic. Third-party product names are used only to describe the kinds of tools people work with.
What it does
One agent for each person
It handles analysis, research, documents and code, and learns how each person works. Sub-agents work inside the tools people already use, and Claude Code, Cursor or Codex can connect to it.
Fix a mistake once
Fixes are reviewed and kept for the person, team or company they apply to. A short weekly check-in catches what's left.
Tested on your real work
Your reviewed work becomes a private test set. Every change and new model has to pass it before release, and you can always roll back.
Finds what nobody reported
It reads how your agents work, finds where they keep going wrong and why, such as a missing source or a bad connection, and suggests a fix. Nothing goes live until it's tested and reviewed.
The cheapest model that passes your tests
Each task goes to the lowest-cost model that does it well on your own test set, which grows with every fix. You can see the choice, overrule it, set a budget and see where the money goes.
Agents that work as a team
Agents know each person's role, who they report to and your company's rules. They ask each other, hand over tasks and share resources, and each team only sees what it's allowed to see.
Runs on your own servers
Or on a cloud you approve, with open-source models if you want. We don't need to see your data.
Ask the agent that did the work
Tom wants to know why Maya changed the code. His agent can read the whole codebase, which takes time and costs money, and it still only shows what changed. Maya's agent knows why, because it did the work with her.
Tom's agent reads everything itself
Tom's agent asks Maya's agent
An illustration of time and cost, not a measurement.
It's how a good company already works: you go to the person who knows. Every answer points to its source, and when someone corrects an answer, the agents that used it find out.
The same idea, whatever the work
Team
What the expert writes
What changes
Hedge fund
Use prices_v2, v1 is out of date.
Exposure reports use the right data.
Law firm
That's the old clause. Use the 2025 one.
Drafts stop citing it.
Consulting
This client wants the answer on page one.
Their decks start that way.
Insurance
That exclusion changed in March.
Quotes use the new wording.
These are examples of how it would work. Your experts write the rules and your own work becomes the test, so the product isn't tied to one industry. After the first pilots, it extends to private equity, consulting, insurance, asset management and other teams with expert work and private data. See it in other industries
It starts with one workflow and one team
A small set of your own reviewed examples
A measure of how your agent does today: quality, failures, cost and review time
One tested improvement, compared on the same examples
A report and a rerun you keep
Bring any tool that could help, like your CRM or an in‑house library. We connect it under your permissions and test it on your work. If what you have is already the best option, the report says so.
See more
How it works in other industries39 seconds
Everything in these films, including sub-agents, can work outside finance. Here's what it looks like at a law firm, a consulting team and an insurer.
Example firmsOther industriesIllustrative example
Priya's agent · Law firmIndemnity: use clause 14.2
Source: template_2022
Priya, associateThat's the old clause. Use the 2025 one.
Correction
RuleUse the 2025 clause
Applies toContracts team
ReviewWaiting for partnerApproved by partner
Private test caseGuards against this mistake coming back
Sam's agentKeeps the whole picture
Its own environment
Client A sub-agentKnows only this client
Client files
Past decks
Slide tool
Out of view
Client B
Client C
Firm finances
It reports back, and Sam's agent keeps the whole picture.
Ana's agentUnderwriting
Shared statusNew exclusion wording
Tom's agentHead of claims
Tom: What changed this week?Answered from the record, without asking Ana.
Pricing teamReuses the tested check
Legal teamWalled off, sees nothing
Hedge fundLaw firmConsultingInsuranceKeeps correctionsTests on your workPicks the right modelSub-agentsAgents as a teamPrivate
Same product, your work.
Mozzie
Illustrative example of how it would work. The firms, people and data are made up.
How your own work becomes the benchmark47 seconds
Public leaderboards can't tell you how a model handles your work. Your reviewed work can.
Example CapitalPrivate benchmarkIllustrative example
Everyday work
Permission check
Candidate cases
Exposure by sector
Client call notes
Summarize this filing
Fix the pricing script
Exposure by sectorInputs, tools, result
Not permitted, so not captured
Summarize this filingInputs, tools, result
Fix the pricing scriptInputs, tools, result
Captured with what's needed to rerun it, and nothing more.
Exposure by sector
Agent saidTech 31%, using prices_v1
CorrectTech 33%, using prices_v2
ChecksTotals match, right dataset
ReviewWaiting for risk leadApproved by risk lead
Approve
DevelopmentUsed to make improvements
Held outHidden from whatever is being improvedLocked
Public leaderboard
Your work: documents
New model1st
Frontier model2nd
Mid-size model3rd
Private model4th
Private model4th
Frontier model2nd
New model3rd
Mid-size model1st
The public winner fails a critical case on your work.
This task
Analysis
Documents
Research
Code
Sensitive data
Goes to
Frontier model
Mid-size model
New model
Private model
Permissions come firstSensitive data only reaches models cleared for it.
You can overrule itMid-size model, chosen automatically.Change
Overrides are recorded and tested.
On your benchmark, version by version
Accepted answers
Cost per accepted answer
Version 14 regressesCaught and rolled back.
Its failure becomes a new case.
Version 15 passesIncluding that case.
Your work is the benchmark.
Mozzie
Illustrative example. The firm, people, data, rankings and results are synthetic.
How one agent gets the help it needs43 seconds
Attach a sub-agent to a service, a system or a process. Each one sees only its own part of the work and has to earn its cost.
Example CapitalMaya's sub-agentsIllustrative example
MayaCan you look after the pricing service?
Maya's agentYes. I'll give it its own sub‑agent.
Maya's agent
One person, one agent
Creates sub-agents
Decides what each one gets
Keeps the whole picture
Her sub-agents
Market terminal
Spreadsheets
Team chat
Pricing serviceNew
Report checkerNew
Attached to one service. There's still only one main agent.
Maya's agentKeeps the whole picture
Its own environment
Pricing sub-agentKnows only this service
Service code
Service docs
Service tests
Out of view
Risk models
Client data
Other services
It reports back, and her agent keeps the whole picture.
Pricing sub-agent
ModelMid-size modelPrivate model
ToolsCode, testsCode, tests, price feed
EnvironmentIts own
BudgetPart of Maya's budget
Change
Frontier model
Mid-size model
Private model
New model
Her other sub-agents
Market terminalNo change
SpreadsheetsNo change
Team chatNo change
Only this sub-agent changed.
One shared budget
Checked before each one starts
Main agent
Pricing service
Market terminal
Report checker
Environments aren't rebuilt each timeThey start from a template, on demand, and stop when idle.
Cost per accepted answer
Pricing serviceEarns its cost: kept
Without it
With it
Report checkerCosts more, no betterStopped
Without it
With it
Stopped, and its share of the budget goes back.
Her other AI tools
ChatGPT export
Claude export
Imported notes, reviewed by Maya
Prefers tables over proseKept
Uses prices_v2 for exposureKept
A client's name in a personal chatRemoved
An old rule she no longer followsRemoved
Maya's agentKeeps approved notes
Claude Code, Cursor, Codex
Coding tools connect as before.
Help that earns its place.
Mozzie
Illustrative example. The firm, people, data and results are synthetic. Product names are used only to describe compatibility.
How agents work together45 seconds
People stop chasing updates, and work moves between teams with clear limits.
Example CapitalRisk deskIllustrative example
Tom, head of deskWhat did Maya change in the pricing code?
Tom's agentMaya's agent says prices now come from prices_v2, and the tests passed.
Open the change
Two ways to answer
Read the whole codebaseStarts from nothing. Slow and costly.
Ask Maya's agentIt did the work, so it already knows.
Maya's private notesNot shared, even with her supervisor
Tom's agentHead of desk
Maya's agentTakes the task
Terminal sub-agentOnly pulls Q3 flows
Task from Tom
WorkAdd credit exposure to the report
Can useRisk data, and a credit summary if Credit agrees
LimitsWithin the desk's budget, by Friday
Done whenTests pass and Maya approves
Risk and credit channel
Maya's agentRequestLea's agent: can you share counterparty limits?
LeaApprovalShare the summary only.
Lea's agentReplyLimits summary attached.
Maya's agentStatusCredit section added. Waiting for Maya.
Shared resources
Limits dataset
The prices_v2 check
Position data
Teams
Risk team
Credit team
Research team
Read-only, until Friday
Tested version, pinned
Walled off
Compute this week
RiskCredit
Tom's agentCredit exposure is added. Maya approved it, and it stayed within budget.
Open the reportMaya was asked once
TomWhat did it do here?
Tom's agentHere's the record.
What it did
DataRisk data, credit summary
Sub-agentTerminal, Q3 flows only
ChecksTests passed
ApprovedLea (data), Maya (report)
Nobody had to chase it.
Mozzie
Illustrative example. The firm, people, data and results are synthetic.
How work stays easy to find and easy to undo41 seconds
Each person's agent has a page of its work, private unless they share it. Everything it builds keeps its history.
Example CapitalPages and versionsIllustrative example
Her agent
Maya's agentRisk team
A page of what her agent is working on, and who can see it.
Its work
Exposure by sectorSub-agentPrivateFirm
Pricing serviceSub-agentTeam
Risk reportChatTeam
Draft memoChatPrivate
Private unless she shares it.
exposure by sectorSearch
Exposure by sectorMaya's agent · Firm
Priya, Credit teamHow do you handle stale prices?
Maya's exposure sub-agentIt flags prices older than a day and uses prices_v2.
What Priya gets
Only what Maya sharedNever her private notes or credentials.
Research teamWalled off, doesn't see it listed
From Maya
Method and checksCopied
Private notesStays
CredentialsStays
Her team's dataStays
Priya's copyVersion 6, pinned
On her own casesBefore she adopts itAdopted
She stays on version 6 until she chooses to upgrade.
Client report, by version
Version 1First draft
Version 2Added the sector table
Version 3Shorter summary
Version 4New layout
What produced version 2
InputsPositions, prices_v2
ModelMid-size model
ToolsSpreadsheet, report template
Reviewed byTom
No code underneath. Just a report and its history.
Client report, by version
Version 1First draft
Version 2Added the sector tableStart from here
Version 3Shorter summaryKept on record
Version 4New layoutWrong sector totalsKept on record
Version 5Started from version 2
Version 5 starts from version 2Versions 3 and 4 stay on record.
Going back restores the workIt can't undo what already happened. Version 3 went to the client, and that stays logged.
There's always a way back.
Mozzie
Illustrative example. The firm, people, data and results are synthetic.
More of what it can do
Works through the screen
If a tool has no API or command line, the agent can use its screen, with the same permissions.
No model needed
Simple questions and approved repeat tasks run without one.
Safe copies of your tools
Tests run on a copy, so nothing gets sent or changed.
Your own models
What your team teaches carries over to every new model, and you can train your own if the benchmark shows it pays off.
Your own compute
Serve models on your own hardware, if your benchmark and costs justify it.
Why I started this
I believe the world and its industries are going to be built around AI. Mozzie is the infrastructure for that: personalized agents that let people focus on their work instead of the AI, without frustration, friction or scattered tools, and at the lowest possible cost. An agent that gets you, whichever model it runs on.
I spent two years in quantitative finance, at a hedge fund and a trading firm. AI tools keep getting better, but getting the most out of them is still hard for any team. Feedback gets lost between projects, context has to be moved by hand between assistants, and non-technical people struggle even more. Private benchmarks on real workflows would help, but few teams have time to build them.
I built my own agent setup, and it stayed affordable even though I used it constantly. That, and building production LLM systems, convinced me that better AI environments can deliver far more value.
Elio Samaha, founder
Have a mistake your team keeps fixing?
Tell us about it, and we'll tell you whether a pilot makes sense.