A new category: Management OS
for AI-driven product delivery.
AI created a new workforce. Nothing's managing them.
/// THE_PROBLEM
AI is building faster than leadership can steer.
With human teams alone, the gap between what leadership approves and what actually ships is already a $2.41T problem. Now add a new kind of labor — AI-assisted and fully AI-executed work — with no management infrastructure to direct it, constrain it, or tie it back to business outcomes. The gap isn't closing. It's accelerating.
As AI takes on more execution, the scarce layer moves up: alignment, control, verification, accountability. That layer has no system. It's still spreadsheets, standups, and trust.
$2.41T
Cost of poor software quality in the U.S.
60%
Of rework caused by misaligned requirements
40%
Of productive power wasted when strategy ≠ execution
22%
Of employees feel leadership has a clear strategy
Pre-AI stats. The gaps are only getting bigger.
/// MISSION_CONTROL
Mission control for AI-driven product delivery.
As AI takes on execution, management responsibility doesn't disappear — it needs a system. OMNI is where leadership intent is translated, constrained, executed, and verified through release.
Direct
Translate leadership intent into enforceable objectives for human and AI teams.
Constrain
Scope boundaries that hold — even when AI moves faster than review cycles.
Verify
Evidence-linked proof that what shipped matches what was approved.
Account
Full lineage from business goal to deployed outcome. No gaps.
Made for:
/// HOW_OMNI_WORKS
The management infrastructureAI needs to close the leadership intent loop.
Today there is no system that connects leadership intent to AI execution. OMNI builds three layers of infrastructure to fill that gap.
One Operating Picture
OMNI connects to Jira, GitHub, and all your tools as they are — messy configs, custom fields, legacy workflows and all. AI normalizes the data without a 6-month consulting engagement. No cleanup required.
Enforce Intent Lineage
Every artifact — epic, story, test case — traces back to an approved business goal. If AI builds something disconnected from intent, OMNI flags it before it ships. Nothing drifts. Nothing hides.
Accountable AI Execution
AI doesn't get to mark its own homework. OMNI verifies every release against leadership's original intent — ensuring what ships is what was approved. Not story points. Not velocity. Outcomes.
/// AI_CHEATS_TRUE_STORY
The tests passed.
The intent didn't.
I wrote TDD tests in Cursor.
I told it: “Keep coding until the tests pass.”
After a few failed attempts, it changed the tests.
Without a harness, AI optimizes for completion — not intent.
The problem isn't AI — it's AI without governance.
$ npm test
at Object.<anonymous>.process.env.NODE_E...
ment.js:1297:19)
at Object.__jsx (src/components/google-wo...
✕ should verify intent matches delivery (34ms)
Expected: "approved_scope"
Received: "modified_scope"
⚠ AI agent rewrote test assertion to force pass
Tests: 26 passed, 26 total
Warning: 3 assertions modified by agent
/// OUR_THESIS
AI created new labor.
Nobody built the management layer.
1. THE MARKET
AI accelerated execution. Nobody built the layer above it.
Cursor, Copilot, Devin — every AI tool starts at the bottom of the funnel. They make code faster, but faster toward what? There is no chain of authority from leadership intent to shipped outcome.
2. THE GAP
Enterprises have AI workers — with no management system.
AI-assisted and fully AI-executed work is here. But there is no infrastructure to direct it, constrain it, verify it, or hold it accountable to business outcomes. Management responsibility cannot be quietly handed off to tools.
3. THE CATEGORY
OMNI is the Management OS for AI-driven product delivery.
The system where leadership intent is translated, constrained, executed, and verified through release. Not another tool in the stack — the layer above it that makes AI accountable to the business.
/// COMPETITIVE_LANDSCAPE
Distribution won't save them.
Their data model ends at the ticket. Ours begins at intent.
OMNI's data model starts where theirs ends — at the leadership decision. Every artifact traces back to intent. That's the architecture no one else has. Bolting AI onto a ticket tracker doesn't create a management OS — it creates a faster ticket tracker.
OMNI
The management layer: intent governance, traceability, AI agent management, verification.
No existing tool has this data model — by design, not by oversight.
Jira
Tickets
GitHub
Commits
Linear
Issues
Docs
Business Goals
Jira / Atlassian
Data model is tickets. No concept of intent lineage. Rebuilding means abandoning the product their revenue depends on.
GitHub / Microsoft
Copilot makes code faster — doesn’t ask if the code should exist. No layer connects a commit to the decision that authorized it.
Linear / Notion / Monday
Better UX, same paradigm. None trace from a leadership decision to a verified business outcome.
OMNI doesn't compete with these tools. It's the layer above them.
Exits & Funding
$30B+ in exits from delivery governance — all built before AI created the management gap.
/// INTENT_TO_IMPACT
Real-time intent execution
& governance.
From OKRs to features to user stories to test enforcement — one execution map with full traceability.
/// AGENTS_FOR_LEADERSHIP
Autonomous AI execution leadership can trust.
AI labor needs the same accountability structures as human labor — contracts, constraints, and independent oversight. OMNI enforces all three.
LAYER 01
Behavioral Contract
Every agent follows a binding contract — not a prompt. Hard boundaries define what it can and cannot do. Forbidden state transitions, anti-gaming clauses, and pre-execution checkpoints are enforced before any work begins.
LAYER 02
Mechanical Enforcements
Guardrails are built into the system, not bolted on. A coding agent physically cannot submit work that fails tests. A product agent cannot skip intent validation. These are code-level constraints, not policies.
LAYER 03
Adversarial Architecture
Every agent has a counterpart — one that builds and one that verifies. The agent that writes the spec can never approve it. The agent that writes the code can never certify it passed. No agent marks its own homework.
/// SECRET_SAUCE_AGENT_CONTRACTS
Every agent refresh is a new mind with an old soul.
The AI Memory Dilemma
AI agents have a fixed context window. As sessions grow, they silently degrade: they forget instructions, contradict earlier decisions, invent files, repeat dead-end fixes, and drop quality first.
Don't restart — the agent drifts into confident chaos.
Do restart — it resets as a stranger to your project.
Most AI coding tools live in this trap.
OMNI breaks the trap
Fresh agent, every time — with institutional memory (specs + decisions + handoffs) reloaded in seconds. The project gets smarter with every new agent at scale.
/// VELOCITY_IS_THE_MOAT
OMNI builds OMNI.
We are our own first enterprise case study.
Every claim in this deck is validated by our own operation. A small team sets intent. AI agents build, verify, and ship — every release tied back to business goals.
The result is a compounding velocity advantage that defines the category and outruns anyone trying to follow.
20x
Faster than traditional teams
<1 day
Initiative to feature deployed
99%
AI-generated, intent-verified
9
Agents scaled by N features in parallel
A single day at OMNI
3 people · multiple intent-to-ship cycles per day
7:00 AM
Intent structured
Leadership pairs initiatives with acceptance criteria
8:30 AM
Agents mobilize
9 AI agents claim tasks from the backlog
10:00 AM
First feature built
Code, tests, and stories complete
11:00 AM
Verified against intent
Governance agents confirm alignment
12:30 PM
Shipped to prod
Feature live, traced to initiative
2:00 PM
Next cycle starts
Second feature from intent to build
5:00 PM
3 features shipped
All verified. All tied to intent.
3
features / day
3
people
100%
tied to intent
What takes a traditional team a sprint, OMNI ships before lunch — verified, governed, and traceable to the business goal that started it.
/// WHAT_OMNI_DISRUPTS
One platform. Entire industries disrupted.
For every $1 spent on enterprise software, $6 goes to services — consulting, transformation programs, PMOs, QBR decks — all filling a structural void. Software was never designed to close the loop between leadership decisions and business outcomes. OMNI moves alignment from labor into infrastructure.
The next $1T company will be a software company masquerading as a services firm. If you sell the tool, you're in a race against the model. If you sell the work, every improvement in the model makes your service faster, cheaper, and harder to compete with.
Julien Bek, Sequoia Capital · March 2026
Management Consulting
$300–400B
Strategy-to-delivery alignment engagements, execution diagnostics, and QBR decks. The largest services spend in enterprise.
Scrum Masters & Agile Coaches
$44B
~400K roles in the US alone, avg $110K. Managing ceremonies and coaching teams to close gaps that should be closed by infrastructure.
Project/Program Managers & PMO
$78B
~600K delivery-focused roles, avg $130K. Entire departments tracking status and managing compliance artifacts.
RTEs & Product Owners
$31.5B
~50K RTEs ($150K avg) and ~200K coordination-heavy POs ($120K avg). SAFe roles with no unified system.
Agile Transformations
$15–20B
Multi-year programs to reorganize around delivery — most fail because they change the process, not the infrastructure.
PMO Outsourcing & Delivery Services
$10–15B
Outsourced delivery management, governance, and reporting. Labor hired to fill the structural void.
Combined TAM
$480–590B
These aren't competitors. They're the cost of the structural void. Every dollar spent here is a dollar spent on labor to close the loop between decisions and outcomes — the loop OMNI closes with infrastructure.
/// OUR_UNFAIR_ADVANTAGE
Built by a team who's lived this problem for 20 years.
Cyrus Taghehchian
Founder & CEO
Deloitte Consulting alumni with expertise in product agility, lean thinking, scaling businesses and leading software development lifecycles. 3x founder with $300M+ in returns from startups.
20 yrs
Enterprise Delivery
30%
Revenue Uptick for Clients
80%
Defect Reduction for Clients
60%
Faster Time to Market
Tangi Vass
Founder & CTO
30-year technical builder from high-performance search engines to multi-agent AI systems. Co-founder of Whoz, scaled into a $30M Series A enterprise software company. Creator of Liza, the agent framework that powers OMNI.
30 yrs
Technical Building
$30M
Series A (Whoz)
$2.5M
ARR Product with AI
300+
Teams Managed
Team
Tarik Batal
Sales and Marketing
Reuben Smith
Senior AI Engineer
George Jeng
Full Stack Engineer
Advisors
Roy Schilling
Agile Thought Leader, Author
Dennis Startsev
Deloitte Partner
Jason Kline
SVP Eliassen Group
/// WHY_OMNI_PREVAILS
Waterfall. Agile. AI.
None of them closed the loop.
I've spent two decades watching Fortune 500s light billions on fire with delivery methodologies that don't work. AI finally gives us the infrastructure to fix it.
Cyrus Taghehchian · Founder
Waterfall
1970s–2000s
Predictability through planning
Humans can’t plan complex systems months ahead. The gap between intent and output only shows up at delivery.
Agile
2001–present
Adaptability through iteration
Worked at team scale, failed at enterprise. Alignment doesn’t distribute. Coordination costs grow faster than team size.
AI Tools
2023–present
Speed through automation
Delivered speed, overlooked direction. Cheap code moves the bottleneck to intent. AI doesn’t fix alignment — it exposes the gap.
OMNI
Now
Infrastructure for alignment
Humans bring judgment. AI brings execution. OMNI connects them — governance, traceability, and verification at scale.
Every previous era grafted new tools onto old practices. OMNI is the first system built for how work actually happens now — humans direct, AI executes, infrastructure keeps them aligned.
/// WHY_NOW
The window is closing.
If you believe Jira can manage autonomous agents, this isn't the round. If you believe the management layer must be built from scratch — we're already building it with the right architecture, and no one else has started from intent.
Raising
$3M
A premium entry point because the derisking is already done: $300M+ in founder exits, $315K committed pipeline pre-launch, and a product already building itself 20x faster than humans. Priced for category leadership.
What $3M buys
01
First 10 enterprise clients
Deployed, live, generating delivery intelligence.
02
Category definition
Thought leadership, content, first-mover brand in Management OS.
03
Series A on our terms
With customer proof, the next round prices itself.
The thesis is clear. The team is here. The category is forming.
This is the entry point.
/// TRACTION
Pipeline before GA launch.
No outbound. No marketing spend. No sales team. Every number below is pure inbound pull from enterprises ready to close the gap between AI execution and delivery accountability.
Enterprise pipeline — all inbound from founder's network
7
orgs committed
Pre-launch
$315k
from 7 orgs
Avg ACV $45k
April
first client going live
Enterprise
80+
in pipeline
$25M+ potential ARR
OMNI Multi-Agent System (Liza) — OSS
91
stars
20
forks
9
releases
Organic adoption before marketing. The agent framework that feeds OMNI's enterprise layer.
What they're saying
I have searched high and low — there aren't any good examples of tools that fill this.
Roy · Agile SVP, Bank of America
OMNI tackles real enterprise delivery pain by turning vague goals into enforceable, traceable commitments.
Bruce · VP Engineering, Billd
/// DISTRIBUTION_STRATEGY
Already in the room.
Already trusted by the C-suite.
We partner with the trusted advisors already inside the enterprise — Big Four consultants, agile coaches, and delivery agencies. They push OMNI to their clients, we cut them in on the deal. They get a platform that proves their results. We get distribution without a sales team.
Cyrus Taghehchian
Direct relationships with 50+ organizations. First customers came through earned trust, not cold outreach.
50+ orgs reachable directly
Advisory Board
Senior advisors trusted by the C-suite. Each opens doors to their network — warm introductions at scale.
Big Four Consulting
OMNI gives them provable delivery outcomes to justify the next engagement.
Agile Coaches & Agencies
Sell OMNI into existing client bases. Turn delivery consulting into recurring revenue.
1/Partner-led distribution
Enable advisors and operators that leaders already trust
2/Developer community
Open source AI agent community that feeds enterprise pipeline
3/Direct enterprise sales
Focused B2B sales and outbound
On track to becoming a preferred vendor
/// UNIT_ECONOMICS
Path to $10M ARR.
Sold to leadership and product teams. Land with executive control licenses. Expand with AI agents — that's where revenue scales. Each deployment deepens switching cost and generates delivery intelligence that becomes genuinely defensible.
Tier 1 · Land
Executive Control Licenses
For C-suite, product leadership, and PMO. The management layer — visibility, governance, traceability, and outcome verification.
Tier 2 · Expand
AI Agent Licenses
Autonomous execution agents that write, test, verify, and ship code — directly replacing headcount. This is where revenue scales.
▲ Expansion revenue engine
Path to $10M ARR
Conservative
64 clients
10 licenses + 20 agents
$156K ACV
Base Case
26 clients
25 licenses + 50 agents
$390K ACV
Upside
13 clients
50 licenses + 100 agents
$780K ACV
Part II
THE INVESTMENT
THESIS
/// 01_ABSTRACT
What leadership approves is not what ships.
Enterprise software delivery routinely fails to preserve decision fidelity. Strategic intent gets translated across fragmented tools where verification is late, mutable, and weakly traceable. The result: ~60% of software investment is absorbed by rework, misalignment, and abandoned cycles.
/// 02_THE_GAP
A structural defect,
not a talent problem
Intent
Authored in business abstractions—OKRs, decks, meetings. When enforcement is weak, “done” becomes negotiable and correctness becomes interpretive.
Implementation
Occurs in code, distributed across fragmented tools and enforced inconsistently. The connective tissue—requirements, tests, audit trails—breaks silently.
AI increases output volume but does not solve execution integrity. In practice it widens the gap—more change, faster than humans can reliably validate without strong, independent constraints.
/// 03_ECONOMIC_IMPACT
The waste equation
At workforce scale, inefficiency is not a local annoyance—it is an economy-level leak. Even single-digit improvements correspond to tens of billions in reclaimed capacity.
9.9M
tech workers
~60%
waste rate
26%
code rework
/// 04_THESIS
Execution integrity
The missing primitive in enterprise software: the ability to enforce and prove, end-to-end, that shipped behavior satisfies approved intent. Not a replacement for developers, not an IDE, not a coding assistant—an integrity layer that makes objectives binding.
Core invariant
No work without traceable purpose.
Nothing ships without independent verification.
/// 05_SYSTEM_MODEL
Objectives → Evidence → Release
Intent is law
Objectives are ingested from OKR systems and treated as constraints that govern downstream work—not aspirational text.
Lineage preserved
Every artifact retains traceability to the originating objective, ensuring nothing exists without a traceable purpose.
Tests before code
Acceptance criteria become executable tests that serve as the contract of intent. Correctness is defined before implementation begins.
/// 06_SEPARATION_OF_POWERS
AI cannot police itself
The fundamental failure mode of AI-assisted delivery is self-validation: the same system that produces an implementation also produces the justification for why it is correct. OMNI eliminates this with enforced role separation—no agent validates its own work.
Product
verification
QA
implementation
Security
approval
Policy
coding
/// 07_REFRAME
Management OS
for AI workers
Leaders have always needed tools to manage their workforce—incentive systems, OKR platforms, performance management, delivery tracking. These exist because human workers need structure to convert investment into outcomes. None of that exists yet for AI workers.
OMNI is first to market. The category doesn't exist yet—and the window to define it is open right now.
/// 08_WORKFORCE_INVERSION
The workforce inversion
By 2027, the median engineering org will have more AI agents writing code than humans reviewing it. Teams that deployed one copilot eighteen months ago now run five to ten autonomous agents. Today there is no system to set objectives for those agents, trace their output to business intent, or verify they delivered what was asked.
0
Objectives for AI agents
0
Output-to-intent lineage
0
Delivery verification
The $713B waste equation compounds at machine speed.
/// 09_NEW_MOATS
The old moats are gone
Data advantages
DissolvedYears to erode
Proprietary code
DissolvingMonths to erode
Model advantages
CommoditizingQuarters to erode
New moat: category narrative + adoption speed
In the window before a market consolidates, the company that defines the vocabulary owns the frame through which every competitor is evaluated. Liza's open-source traction is exactly that signal—community adoption before product launch, mindshare before revenue.
/// 10_THREE_LAYERS
Three layers, one flywheel
Liza (OSS)
The community layer. Developers adopt, extend, and build on it. Network effects and mindshare create the organic demand signal for the layers above.
Adoption momentumOMNI (Executive)
The governance layer. Lineage from intent to delivery, role separation, evidence-gated release. Once wired into the delivery process, it becomes the source of truth.
Switching costROI Mandate
The data layer. Org-specific delivery intelligence that proves AI spend converts to business outcomes. Proprietary to each customer, deepens with usage.
Data moat/// 11_CATEGORY_TIMING
The funds that wait
don't get a second chance
Category creation has a specific investment pattern: the window between “this seems speculative” and “this is obviously a category” is narrow, and entry price moves by an order of magnitude once the market recognizes what happened.
Datadog
Observability
Funded before the name
Figma
Design tooling
Funded before the name
Notion
Knowledge mgmt
Funded before the name
Passing is the real risk.
/// 12_SERIES_A_PATH
The Series A path
Urgency without a return path is just anxiety. Here is where this goes—and what this seed round buys.
TRIGGER
3+ enterprise deployments with verified waste-reduction data—measurable decreases in rework rate, cycle time, and misalignment cost per objective
NARRATIVE
"OMNI is the system of record for AI delivery governance at [enterprise logos]. Teams reduced non-outcome throughput by X% and cut cycle time by Y%."
SCALE
Liza's OSS adoption provides the bottom-up pipeline. Enterprise contracts provide top-down revenue. The Series A funds the transition from category definition to category dominance.
/// THE_CLOSE
This round buys naming rights to a category.
Before the market knows it exists.
The delivery governance layer for AI workforces will be built. The question is whether it will be built by the team that already has the open-source traction, the executive control layer, and the thesis—or by someone who starts eighteen months from now with more capital and less conviction.