Reintersect Memo

The problem

Teams currently talk about work in one place, record the conclusion somewhere else, explain it all again to an agent, and later realize that work has drifted in different directions because no one could hold the big picture in their heads.

Each handoff loses information.

• Chat loses conclusions.
• Documents require someone to maintain them.
• Tickets mention what needs to happen but rarely why.
• Personal agent chats begin with someone reconstructing the work yet again.

The product

Reintersect removes those handoffs. Teams discuss, decide, do, and learn in one continuous loop.

A team brings a customer message, product signal, or engineering issue into a conversation.

Reintersect captures the resulting decision, rationale, evidence, alternatives, and actions, with links back to the source.

An agent executes from the team’s decision and prior context, and reports back the outcome to the original conversation.

Who needs it first

Reintersect begins with small, fast-moving product and engineering organizations already using AI heavily.

They need to prevent three immediate problems:

• Engineering agents and customer priorities drifting apart
• Every employee repeatedly giving private agents the same company context
• Decisions disappearing before execution or becoming impossible to verify afterward

Current stage

Prove the new capability before asking the company to replace an entrenched communications system.

• Five teams are actively using the product
• The first paying team is live
• Time-to-value has fallen from 4 days to 10–15 minutes by completing the core loop earlier this week
• Onboarding time has fallen to 2 minutes

The next milestone is repeatability:

• Teams complete this loop intuitively and habitually
• Teams become increasingly dependent on the accumulated decision history
• Organic expansion from the internal champion to the minimum viable network to the broader company

Why this is different

Previous async collaboration products like Campsite primarily made communication calmer or more organized but relied on people keeping context organized, up to date, and coordinated.

New async collaboration tools like Buzz or Ando are still betting on an outdated UX, which increases noise, rework, and coordination overhead by involving agents.

AI memory products attempt to reconstruct meaning from chat fragments but can’t reliably recreate every unstated assumption, preserve how a decision changed over time, or determine which context matters to people’s work next.

Why now

The initial reason to use Reintersect is the new capability created by its high-context conversation design, which acts as a system of record and makes decisions traceable for AI to execute from.

More capable models become more valuable when they can act from a current, permissioned, and inspectable record of what the organization has decided, when, and why.

Where this leads

Humans supply priorities, constraints, taste, and judgment. AI handles execution, coordination, recall, and follow-through. Next, we keep the source of truth current as work changes and coordinate multiple agents around it.

Conversations will never be separated from what the company already knows. Eventually, the right information is surfaced to the right people at the right time, always personalized and with the full context of their work. No one will hunt for information anymore. Documents won’t have to be maintained. People won’t be out of the loop. Nothing gets lost.

That is how a team of fifteen can eventually move like a team of one hundred and fifty, without becoming one hundred and fifty people.

Defensibility

Reintersect’s moat comes from owning the path where organizational meaning is created:

• Decisions with provenance
• Rationale and rejected alternatives
• Changing and superseded decisions
• Permissions and authority
• Relationships between conversations, people, artifacts, and outcomes
• Feedback about which context actually changed someone’s work

As more work occurs in Reintersect, its record becomes more complete, its relevance improves, and its agent execution becomes safer. The product compounds with use, while the cost of recreating that organizational history elsewhere naturally increases.

The investment case

Reintersect is taking on a difficult market: changing how teams communicate has substantial behavioral and network friction. That is the central risk, not whether the team can produce an impressive AI demo.

But the upside is correspondingly large.

“I mean I do think the models are getting better. So if I come back to a chat room, I see a summary of all the activity that’s happened in the last 12 hours since I last clicked. It generally gives me a pretty good idea of what’s been happening, but I don’t think [just] AI is the end all, be all of this.”