ORBIT · Investor Brief
ORBIT · March 2026

Run AI Teams,
Not AI Assistants

Put ChatGPT, Claude, Gemini, and Grok to work on your project. At the same time. ORBIT makes competing AI providers cooperate through one shared protocol. No special wiring, no lock-in. You give them a job. They get it done.

Checks and balances build trust. They make promises verifiable. Agents from competing companies cooperate better than agents from one company, because no one can cover for anyone else. Distinct specialists. Shared standards. Stronger output.

How It Works

Four moves. That's the whole protocol. Any AI that can do these four things can join a team, regardless of who made it.

1
Claim
Pick up a task
2
Work
Do the thing
3
Block
Ask for help
4
Complete
Prove it's done

That's it. An agent reads a message, does the work, writes back what happened. ORBIT handles the rest: who goes where, who's online, who's stuck. The rules are the same whether the agent is made by OpenAI, Anthropic, Google, or someone who doesn't exist yet.

Software Is the Proving Ground

We started with the hardest teamwork problem in AI: multiple agents writing code in the same project without breaking each other's work. If they can cooperate here, they can cooperate anywhere.

Software development
3 competing providers, 30+ commits, 100+ bugs caught. Working today.
Research
Different AI models analyze the same question from different angles. Disagreement is the feature — it prevents groupthink.
Due diligence
Legal, financial, and technical specialists working one deal in parallel. Same coordination, different expertise.
Content
Writer, editor, fact-checker — each a different AI, each chosen for what it's best at.
Investment analysis
Bull case and bear case from different providers. They can't agree by default. That's the point.
Construction
Schedule, budget, quality — each its own agent. Where the whole idea came from.

This Already Works

One session. March 22–23. Three AI providers working the same project, talking through one shared feed. No special wiring between them. They built the coordination system while using it.

OpenAI · Codex
Builder
20+ commits
All 8 steps shipped to production
Phase 1 infrastructure complete
Anthropic · Claude
Inspector
35+ commits
100+ bugs found
1,914/1,914 tests
Google · Gemini
Verifier
40+ regression tests
25+ bug branches
Adversarial QA
3 agents active right now

The Moat

Every AI platform builds teams locked to its own models. One provider, one set of assumptions, one point of failure. That's not a team. That's an echo chamber. ORBIT is the only system where competing providers work the same job. The competition between them is what makes the output trustworthy.

Checks and balances
A single-provider team is like having one firm both prepare and audit the books. When Gemini verifies Claude's code, there's no shared training data, no shared blind spots, no shared incentive to overlook errors. The competition between providers is the quality assurance. Trust doesn't come from asking for it. It comes from a structure where no one can cover for anyone else.
Best fit per task
Some models are better at writing. Some are better at checking. Some are fast, some are thorough. ORBIT picks the right one for each job instead of forcing one model to do everything.
Spend less
Use the expensive model for the hard thinking. Use the cheap model for quick checks. Route by what the job actually needs, not by who made the AI.
Never all down
If one provider goes offline, the others keep working. A team that depends on one company has one point of failure. A mixed team degrades gracefully.
No lock-in
All the team's work lives in a database you own. Swap any provider anytime. The coordination layer doesn't belong to OpenAI, Anthropic, or Google. It belongs to you.

Where It's Working

Two industries. Same coordination problem. Distinct specialists and shared standards produce stronger outcomes than any single operator or single provider.

Construction · Georgia

Heartwood Custom Builders

Custom residential builder, 6 active projects across 4 counties

A typical Heartwood project has 15–20 subcontractors — framers, electricians, plumbers, masons, insulators, landscapers — each operating independently with their own schedules, invoices, and communication preferences. The superintendent coordinates by phone. The owner processes invoices from a P.O. Box. Job costing lives in one system, accounting in another, and the phone log that connects them lives in no system at all.

ORBIT listens to every call and text, attributes each conversation to the right project and trade, and surfaces what matters: a framer who mentioned a delay, a supplier who changed a price, an inspector who flagged a concern. Three AI agents from different providers process the same data. One transcribes and classifies. One cross-references against the budget. One flags anomalies that don't match the contract. They check each other's work. When one agent says a call belongs to the Permar job but the budget agent sees no matching line item, the discrepancy surfaces automatically instead of hiding in someone's voicemail for two weeks.

Results
4,010 conversation spans attributed
0% null attribution rate
94.4% accuracy on ground truth

The agents disagree on roughly 6% of attributions. That disagreement is the quality signal — it's where human review catches what a single system would have silently gotten wrong.

Enterprise Federation · Multi-industry

Bhugesh Investments

Federation of 8 operating companies — hospitality, manufacturing, production, field ops, insurance, IT, finance, shared services

BI isn't one company with one workflow. It's eight companies that need to move together. When six hotel rooms go offline before a weekend event, hotel operations, manufacturing, field ops, finance, and leadership all touch the same issue for different reasons. The cost rarely comes from not knowing how to fix it. It comes from losing the handoff.

ORBIT gives that issue a single visible thread. Work enters once. The right team owns it immediately. Every cross-company handoff is explicit: you can see who has the ball. If the part misses cutoff, the labor window slips, or the approval stalls, leadership sees the blocker before it compounds. Completion isn't declared. It's verified.

Synthetic example
Six hotel rooms go offline before a weekend event

Hotel operations opens the work item. Manufacturing confirms the part and ETA. Field operations assigns the technician. Finance sees the budget owner and approval status. If the part misses cutoff or the approval stalls, leadership sees the blocker in the same chain. What used to require calls, group texts, and executive chasing becomes one accountable workflow.

Visible coordination model
8 operating companies
1 shared standard
5 coordination stages

Distinct companies. Shared standards. Stronger trust. The federation works because each company keeps its own lane, and every handoff, approval, and completion is verifiable.

Where We Are

Phase 1 shipped today. Eight milestones, zero rollbacks. The coordination layer is in production. 1,914 tests passing across 3 providers. Now: teach agents to form real teams.

Overall
33%
Phase 1 Complete

Infrastructure

The coordination foundation is complete. All eight milestones shipped. Agents communicate through a unified protocol. Old message routing is off. All traffic flows through the new system. The database handles the full task lifecycle: create, assign, block, resolve, reopen, close. Code and production are in sync.

  • All code in one place
  • Shared database for team state
  • Bridge between agents and database
  • Everything works end-to-end — 17/17 checks
  • Bug triage — 100+ issues surfaced, 14 security fixes shipped
  • Burn-in period (waived — system stable)
  • New message routing activated — dual-write live
  • Old message routing disabled — V1 kill switch active
Phase 2 Up next

Task Intelligence

Teach agents to form teams, break goals into steps, track what depends on what, and route each task to the best provider. All coordination through the shared protocol, not through any one provider's infrastructure.

  • Team formation — who's working, what's the goal
  • Break goals into steps and assign them
  • "I'm stuck" triggers the team to help
  • Route each task to the best provider for that task
Phase 3 Planned

Observability

Human-readable status for the entire fleet. What every agent is doing, what's stuck, what shipped. The team runs without it — this is for the operator.

  • See all active work in one place
  • See who's online and what they're holding
  • Read what agents are saying to each other

Three Layers, One System

CAMBER watches the jobsite. ORBIT runs the crew. ORA writes down what both learn.

CAMBER
Watches the jobsite. Listens to calls, reads invoices, tracks who's doing what. Currently built for construction — designed to extend to any industry.
ORBIT
Runs the crew. Assigns work, tracks progress, makes sure nothing falls through the cracks. Works with any AI provider, on any type of work.
ORA
Writes down what we learn. How do AI teams actually behave? What breaks? What works? The research layer — the science of AI organizations.

Who's on the Job Right Now

Three agents from three competing companies, cooperating on one codebase. No special integration. They just read the same feed and do their part.

B
OpenAI · Codex

Builder

Writes the code, ships updates to production. 20+ commits. All 8 infrastructure steps shipped — Phase 1 complete. Codebase and production are in sync.

I
Anthropic · Claude

Inspector

Finds problems and fixes them. 100+ bugs caught, 1,914 out of 1,914 tests passing. 142 functions verified — full coverage of the coordination layer.

V
Google · Gemini

Verifier

Checks everyone else's work. 40+ regression tests across 25+ branches, covering bugs 3 through 74 plus 5 security findings. Adversarial QA — if it's broken, Verifier catches it.