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Codag

The first AI talent agency.

AI employees that
share a brain.

Deploy teams of AI employees with shared organizational memory. They never misalign. They never forget. They get smarter every week.

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Operations Research Scheduling Data Entry Reporting Coordination

The problem

$2T

Lost every year to misalignment in U.S. businesses. And it's getting worse, not better.

Human alignment is broken.

Teams spend 30-40% of their working hours on meetings, standups, Slack catch-ups, wikis, and status updates. Knowledge is scattered across people's heads, threads, and docs. Nobody has the full picture. Everyone fills the gaps with assumptions. Work gets done wrong, corrected, and redone.

AI agents made it worse.

Every AI employee product on the market executes tasks with zero organizational context. They don't know your team, your conventions, or your history. They can't learn from corrections. They start from zero every session. Humans now spend time aligning AI tools on top of aligning each other.

Both problems share one root cause: there is no persistent, shared memory of how your company actually works — not for people, and definitely not for AI.

The solution

A department that shares a brain.

Human teams spend a third of their time staying on the same page. Codag's employees spend zero. That recaptured time, multiplied across an AI department, is the ROI.

Each AI employee gets its own cloud computer, Slack account, and tool credentials. Managers delegate tasks conversationally. They execute through a real browser, mouse, and keyboard — not API integrations that break when a UI changes.

Real computer use

They see the screen, move the mouse, type on the keyboard. No brittle API integrations. If a human can do it on a computer, they can do it.

Shared memory

Every AI employee draws from the same organizational context before acting. Team structure, naming conventions, project codes, decision history, what's been tried and failed.

Conversational delegation

Assign work in Slack like you would a human colleague. No workflow builders. No drag-and-drop configurations. Just tell them what to do.

// shared memory in action

"This is high priority" → Uses P1 label — knows the team's convention
"Send this to Sarah" → Routes to Sarah Chen, Design — knows the org
Task resembles last month's failure → Avoids the approach — knows the history

No telephone game. No drift. No conflicting interpretations.

How it works

Deployed in days, not months.

Forward-deployed. We embed with each customer and configure everything. Your team talks to AI employees by day three.

1

We embed with your team

We learn your tools, workflows, and org structure. Seed shared memory from your Slack history, docs, and project boards. Years of organizational knowledge, captured in hours.

2

We configure each AI employee

Each gets a cloud computer, Slack account, and role-specific tool credentials. Configured for their function — ops, research, data entry, coordination.

3

Shared memory activates

Team structure, naming conventions, project codes, decision history, who to escalate to. One brain for the whole department. Every employee sees the same truth.

4

They get to work

Managers delegate in Slack. AI employees execute through real browsers. Memory compounds with every correction, every preference, every completed task.

The moat

Corrections become guardrails.
Preferences become defaults.

DAY 1

They follow instructions.

Execute tasks based on seeded organizational context. Fast, accurate, but still learning.

DAY 30

They anticipate needs.

Corrections have become guardrails. Preferences are defaults. They know how your team actually works.

DAY 90

They know things half the human team forgot.

Six months of accumulated organizational knowledge. Switching costs no competitor can replicate.

A company doesn't just get cheaper headcount. It gets a workforce that is structurally incapable of the misalignment that burns $2 trillion a year.

Proof

Built and benchmarked.

Codag's organizational memory layer is live. Blind A/B tests across 50 prompts on real repositories. The technology that makes our AI employees learn works today.

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Win rate (blind A/B)

31.6%

Fewer tokens

36.5%

Lower cost

Beat GitHub's own MCP server 10–5 head-to-head using 62% fewer tokens. 14 out of 15 known pitfalls avoided.

50+ team conversations. Consistent signal: ops and engineering leads at 20-80 person companies would deploy this tomorrow.

Pricing

Priced as headcount, not software.

The addressable market is labor, not software. We replace missing capacity at a fraction of the cost.

Traditional hire

$80K /year fully loaded
× Recruiting fees
× Months to ramp
× Churn risk
× Alignment overhead
× Knowledge leaves when they leave

AI employee

$1-2K /month
✓ Deployed in days
✓ Zero recruiting fees
✓ Zero ramp time
✓ Zero churn
✓ Shared organizational memory

More coverage at a third of the price.

Your competitors will deploy AI employees. The question is whether yours share a brain.

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