Every session
Cold start
New conversation, blank slate. The context you built yesterday is gone and you rebuild it by hand.
Power users
You are the retrieval layer.
You pay for Claude, Cursor or Copilot, and you spend your day as its filing clerk: locating the file, pasting it in, re-explaining the project, watching the window fill until it forgets how the conversation started. The model is extraordinary. It just has no memory of your work. Find and Seek gives it one.
The ritual
Four steps, and three of them are yours.
Find the file. Check it is the right version. Paste it in. Then the model does the part you are paying it for. You did the retrieval; it did the last ten percent — and tomorrow you will do it again, because none of it persisted.
Every turn
The window fills
Whole files stack up in context. Replies slow, the cost per turn climbs, and eventually it drops the beginning.
Every time
No provenance
It half-reads a document and gives you a figure with total conviction. Nothing points back at where it came from.
Connect it
One local link into the harness you already run.
Find and Seek connects to your agent via MCP. Claude Desktop, Claude Code, Cursor, or your own agent connects to it over a secure link — no rebuilding your setup.
Instead of a whole file, your agent gets a compact card: the relevant passage, its source, and a confidence score. If the card is not enough it asks for more. You keep the model you like — we are deliberately not in that business. Models are the commodity; the memory is the asset.
The mechanism
What bounded retrieval actually does.
by construction
Bounded, not tuned
The default response is a passage and its source, not the document. Context stays flat as the corpus grows, because the size of what comes back doesn’t depend on the size of the pile.
every result
Sources, not dumps
Passage, file, page or timecode, and a confidence score. If the card isn’t enough the agent asks for more — an explicit choice, not an automatic whole-file read.
every session
No cold start
The understanding was built at ingest and it persists. Your agent doesn’t rebuild context from raw files on every turn.
What this does and doesn’t mean for your plan
If you are billed per token, sending less of your material is the whole mechanism. If you are on a flat-rate plan, this is cap relief, not a refund — the same work fits inside a compute-weighted allowance that a whole-file workflow burns through. What we are optimising for is accuracy and efficiency; sending less of your material is what efficiency looks like from the outside.
This holds when an agent uses Find and Seek as memory against material it has indexed — not for general chat that never touches your files. And it does not hold on counting: ask how many of something exists across everything and plain grep still beats us unless the agent picks the count mode, which nothing yet prompts it to do. That is a gap in our interface, not a mystery, and we would rather say it than let you find it.
The part that matters at 2am
It refuses rather than guesses.
A retrieval layer that returns a plausible wrong chunk is worse than no retrieval layer, because you cannot tell. When the material cannot support an answer, this returns no confident match instead of the best-scoring thing it found. It checks whether the material actually answers the question asked — right entity, right party, right period — not just whether the text looks similar. A near-perfect textual match from the wrong company is still the wrong answer.
Where it’s up to
Shipped, and honestly labelled.
Shipped
The memory
Indexing, ranked cited retrieval, org-wide literal search with an exhaustiveness contract, campus isolation, and the honesty layer. Driven over the wire, not just in-process.
Behind a flag
The execution tier
Saved skills, governed runs and scheduled analyses are built and gated until they survive the same adversarial driving the shipped surface went through.
Scale
Continuous optimisation
The memory engine is live. What remains is scale — optimising throughput across massive collections.