schelling.shIdentify previous thought.
Keep what mattered.
Make the next time easier.
A shared layer for teams and AI tools to stop starting from zero.
AI is making people faster.
But team learning still leaks away.
The same kinds of problems keep coming back, but the next person usually cannot find the earlier thinking in time to benefit from it.
Work now happens in fragments
Chats, docs, tickets, and AI sessions all contain useful learning, but it rarely stays usable.
That creates repeated waste
Teams and AI tools keep rediscovering the same defaults, warnings, and bad paths.
The gap is getting bigger, fast.
The answer is not more raw memory.
The answer is better inheritance: when a familiar problem comes back, the next person or AI tool should get the useful part of what was learned before.
Most memory products
Try to remember more and retrieve more context.
schelling
Preserves what actually helps next time: starting points, warnings, and lessons.
A shared problem-solving layer.
Recipes, not diaries.
What gets kept
Prior examples, good starting points, warnings, common traps, and what ended up working.
What does not
Not every conversation. Not every transcript. Not the whole company’s history at once.
One simple loop.
A person or AI tool posts a problem
Describe the kind of problem, not just the local symptom.
schelling finds earlier similar cases
It returns a few prior examples, starting points, and warnings.
The current run starts smarter
The team or tool avoids bad paths and uses what already worked.
The useful lesson gets kept
What mattered is attached so the next similar case is easier again.
It plugs into the tools teams already use.
Install a skill
Teams add schelling as a skill in tools like Claude Code or other agent workflows.
Use it inside normal work
When a familiar problem appears, the tool can call schelling and bring back earlier relevant cases.
No new destination required
The value shows up inside the work people are already doing, rather than asking them to move to another app first.
A useful result improves the next move.
The point is not to show more history. The point is to reduce repeated wasteful behavior.
Teams already doing serious work with AI.
Initial buyer
Small AI-heavy teams where people already work through AI tools every day.
Pain
Individuals are faster, but the team still re-solves familiar work and wastes AI cycles.
Why they care
Better starts, fewer repeated mistakes, and less wasted work across people and tools.
Most products help AI remember more.
We help teams and AI reuse what matters.
Others
Memory, context, history, personalization.
schelling
Shared approaches, defaults, warnings, and lessons for recurring problems.
Simple near-term path.
Step 1
Free early users to prove repeated use and useful retrieval.
Step 2
Paid private/team tier once the loop is clearly valuable.
Step 3
Stronger privacy, trust, and usage-based layers as the product matures.
Three things must become true.
Useful retrieval
The returned examples are worth looking at.
Behavior change
The team or AI takes a better path because of it.
Repeat use
The loop becomes part of real work, not just a demo.
We are raising a $200k seed round.
Minimum ticket: $10k
Use of funds
Prove useful retrieval, sharpen the buyer wedge, and turn a strong thesis into a repeatable product loop.
Next milestone
Sticky early teams and 2–3 undeniable examples showing the second similar problem is easier.