multi-doc work
~3× less context
Typical measured band when an agent has to synthesise across files — cheaper and usually faster on the same model.
the honest multiple
We’re not 10× smarter AI. We save you from 10×–1000× rediscovery.
Same Claude, GPT, or Copilot you already pay for. Find and Seek stops agents from re-reading the company every turn — so you burn less context, wait less, and keep the answer cited on your hardware.
what “x” means
Better for the business ≠ a smarter model.
When someone asks how much better this is, they usually mean cost, speed, or risk. We answer those — and we refuse the fake one.
not this
10× smarter answers
We don’t host a better reasoning model. Your AI stays your AI. We make it usable on your files.
this
10×–1000× less waste
Tokens, re-reads, and agent thrash from rediscovering work you already own. Memory instead of archaeology every question.
how the waste disappears
Understand once. Triage first. Escalate only if needed.
01
Index on your machine
Documents, email, code, media — understood at ingest. The catalogue stays with you.
02
Agents get short cards
Default answer is a compact cited hit — not three PDFs pasted into the prompt.
03
Pull more only when required
If the card isn’t enough, fetch a few passages — still bounded, still sourced. Not the whole file dump.
kit vs product
RAG frameworks help you build this. We ship it.
Libraries like LlamaIndex are excellent plumbing — parse, chunk, retrieve. Your team still has to finish the product: permissions, cited cards, when to escalate, where it runs, and how agents connect.
| DIY RAG / LlamaIndex-style kit | Find and Seek | |
|---|---|---|
| What you buy | Framework + parse/index APIs | Finished organisational memory |
| Default to the model | Retrieved text chunks (you size them) | Compact triage cards; escalate if needed |
| When understanding happens | Mostly at query time | Mostly at ingest — retrieve forever |
| Where it runs | Whatever you wire | Your hardware by default |
| Who finishes the product | Your engineers | Checkout, MCP, local index — already there |
Great library. That’s the kit. We’re the product — so the waste cut is default behaviour, not a weekend project.
the honest range
What we can say without marketing fiction.
agent loops
up to 10×–1000× waste
What agents burn re-reading the corpus when nothing remembers between steps. We collapse that to memory + short cards.
your number
measure on your files
Not every chat is cheaper. Single-fact lookups are closer to parity. A pilot shows your multiple.
next step
Try it on your own ground truth.
Mac app available now. Or book a pilot and we’ll measure rediscovery on a folder you care about.