Everything else helps you find. None of it bounds what gets sent.
Copilot, Glean, knowledge graphs, DIY RAG, million-token windows, cloud video AI — almost every one of them helps an agent locate something. Not one of them governs how much then gets shipped into the model, which is where the money goes. That’s the gap Find and Seek fills, and it’s the honest way to tell us apart.
Two questions empty the field.
Most comparisons are fought over “who retrieves better.” For a business that actually runs agents, two different questions matter — and almost nothing scores on both.
- Does it cut your token bill? Agent workloads burn on the order of a thousand times the tokens of a normal chat. A system that finds well but hands back whole documents doesn’t move the number you pay.
- Can your agents actually operate through it? An open surface your Claude, Copilot or Cursor can plug into — with governed execution and a permission trail — not a closed product with its own login.
| System | Cuts your token bill? | Agents can operate through it? |
|---|---|---|
| Microsoft 365 Copilot / Google Workspace | — bundled per seat; the cost is hidden, not cut | ~ their agents, their estate |
| Enterprise search (Glean-class) | ~ retrieves documents; never measured or sold as savings | ~ their agent platform, their UI |
| Knowledge graphs / GraphRAG | — often net worse; building the graph is token-hungry | — it’s an index, not a runtime |
| DIY RAG (Pinecone, pgvector + your pipeline) | ~ modest; returns chunks, not cards | ~ whatever you build yourself |
| Long context (1M-token windows) | — maximises tokens by design; re-sent every query | — no memory between queries |
| Cloud video AI (Twelve Labs-class) | — billed per minute of footage | — a video API, not an agent surface |
| Agent-memory tools (mem0, Claude Projects) | ~ small facts, not corpus scale | ~ memory, no organisational execution |
| Find and Seek | ✓ 69–76% fewer input tokens, measured, live | ✓ open MCP surface + governed local execution |
The field is full of systems that help you find, and a handful that let agents act — but the overlap is effectively empty. Bounded retrieval is what makes agent-run work affordable; the open surface and the execution runtime are what make it possible. Find and Seek is the only one holding both, on hardware you own.
To rebuild one Find and Seek, you’d buy five products.
Compared feature by feature, every rival looks fine — because each one is a slice. Line up the whole stack and the picture changes: nobody sells the composite, because it only works if one engine understands your record once and everything else is built on that.
| System | Video & speech | Continuous / pay-for-change | Bounded cited cards | Open agent surface | Saved skills + governed execution | Runs on your hardware |
|---|---|---|---|---|---|---|
| Copilot / Workspace | — | ~ | — | ~ closed | — | — |
| Enterprise search (Glean) | — images, no A/V | ~ | — | ~ their platform | — | ~ VPC / Dell on-prem |
| Knowledge graphs / GraphRAG | — text | — rebuild | — | — | — | ~ |
| DIY RAG | — | — | — | ~ DIY | — | ✓ |
| Long context | — | — | — worst case | — | — | — |
| Cloud video AI | ~ video only | — | — | — API | — | ~ on-prem option |
| Agent-memory tools | — | — | ~ | ✓ MCP | — | ✓ |
| Find and Seek | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Assemble the same thing yourself and you’re buying enterprise search plus a cloud video vendor plus a graph layer plus an execution platform plus a media library — five contracts, five indexes of the same record — and it would still be fragmented across five vendors, un-auditable as one trail, and it wouldn’t cut a single token off your bill.
What each one is good at — and where it stops.
We’re specific about where these systems genuinely win, because that’s what makes the rest credible. We don’t claim to be more accurate — equal, on the same model and the same files. We claim a cheaper, bounded, owned path to the answer.
Productivity copilots
- Good at
- Zero setup, in-tenant permissions, “summarise this doc I have open.”
- Where it stops
- It works inside Microsoft’s or Google’s own estate on a bundled per-seat price — it can’t reach the footage, site archive or case files outside it, and the cost of context is hidden in the seat, never cut.
- We’re the memory underneath whatever AI you already use, including Copilot.
Enterprise search
- Good at
- Broad connectors, strong pre-indexing, a mature agent platform, and single-tenant deployment in your own cloud — or on-prem via Dell. The closest system to us on deployment.
- Where it stops
- It reads text and images, not video or speech as first-class; and enterprise search returns documents — it’s never measured or sold as a cut to your token bill. The catalogue is a managed subscription, not an asset you keep.
- They deploy near your data too — so we don’t fight them there. We compete on the footage and audio they can’t read, and on a bill measured and bounded, not just answered.
Knowledge graphs
- Good at
- Explicit multi-hop over a stable, well-defined ontology — “everything connected to supplier X through subsidiary Y.”
- Where it stops
- You pay the ontology tax up front and forever, on a corpus that’s mostly unlabelled — and building the graph is itself token-hungry.
- We extract typed facts without asking you to define and maintain a schema first — and we work on the footage too.
DIY RAG
- Good at
- Full control, cheap to prototype, no vendor.
- Where it stops
- Most of it returns chunks, not cards, so the bill barely moves — and the finished version, with video, dedup and continuous ingest, is a permanent engineering team.
- You can wrap a model in a weekend. The finished version is payroll, indefinitely.
“Just use long context”
- Good at
- No infrastructure; genuinely good for a one-off deep read of a small, bounded set.
- Where it stops
- Nothing persists — you re-send the corpus on every query, so cost scales with usage forever, with no citations or permissions.
- Long context rents comprehension by the query. We build it once and you keep it.
Cloud video AI
- Good at
- Genuinely strong semantic search and summarisation over video — the specialist if a pure-video workload is the whole job.
- Where it stops
- Video only — not one catalogue of documents, video and speech — and billed per minute of footage. An on-prem option exists, but the model is cloud-first and metered.
- Superb if video is the whole job. We fold video in with your documents and audio, retrieved by the token, not the minute.
Agent-memory tools
- Good at
- Lightweight, self-hostable, user- and session-scoped memory — good for personal or agent-level recall, with image memory now in beta.
- Where it stops
- It remembers extracted facts from interactions, not a company’s whole file estate — no video or speech ingest, no campus-level isolation, no governed execution over the live record.
- Those remember your chats. We remember your company.
Data platforms
- Good at
- Powerful, governed analytics over curated structured data — serious programmes.
- Where it stops
- They assume the data programme already happened. Our customers are the ones who never did it, whose record is documents and raw footage as it actually lands.
- They’re superb once your data is curated. We’re what you use because it isn’t.
Where they genuinely win.
Knowledge graphs beat us on explicit relational multi-hop over a stable ontology. Copilot and Workspace, inside their own estate with zero deployment, are the path of least resistance. Glean deploys single-tenant in your own cloud or data centre, has a mature agent platform, and reads images as well as text — the closest system to us, and a good product. Long context is simpler than any retrieval system for a one-off deep read of a small set. Palantir and Databricks do governed analytics over curated data at a scale we don’t chase. Cloud video AI is a strong specialist if a pure-video workload is all you need.
What we don’t claim: not “more accurate” — equal, same model, same files. Not “cheaper on everything” — we win the document and structured-data workload, which is the workload. And never a lone headline multiplier without naming the run it came from. The numbers are on the evidence page, with their caveats.