The Customer Proof Platform
Your customers said it best. Keep it on the record.
The Proofbase is your system of record for customer proof: verified stakeholder interviews, approved quotes, and consent — indexed, quality-scored, and served to the AI your team already uses. Captured once, deployed everywhere.
- Approve once, use everywhere
- Your proof, in your AI
What it replaces
Asset managers hold what you made — not what it was made from
The interview, the consent, the words your buyers actually used — none of it enters those systems. That missing layer is the Proofbase.
Drives & folders
Google Drive, SharePoint, Notion — and the Zoom and Gong piles feeding them. Storage, not structure: findable if you know it exists, deployable never.
Content management platforms
Seismic, Highspot, Showpad — and their marketing-side counterparts. Built for enterprise scale: distributing approved assets across hundreds of reps. Everything upstream of upload never enters.
The Proofbase
The layer upstream of both: the source itself, on the record, consent attached — so every asset traces back, and keeps producing.
What each one actually covers
| The job | Drives & folders | Content management platforms | The Proofbase |
|---|---|---|---|
| Storing the finished assets | Handled | Handled | Built inWith lineage back to the source. |
| Getting content to the team | Partly | HandledA volume play — built for enterprise headcount. | Built inLog in and search, or ask through MCP — the right proof for the right buyer at the right moment. |
| Capturing the source interview | Not there | Not there | Built inWe run it — on video, consent in the session. |
| Consent and usage rights on each quote | On you | On you | Built in |
| Tracing a claim to the person who said it | Not there | Not there | Built in |
| Making the next asset from the source | Not there | Not there | Built inThe interview keeps producing — new assets, buyer language, fuel for your canon. |
The fastest way to understand this is to watch one work. Thirty minutes, live.
See a proof base running →The flywheel
Capture. Verify. Encode. Deploy.
Proof enters the Proofbase through one motion, run continuously. Every stage exists to remove a reason the proof would otherwise be unusable later.
It starts with a conversation
We run structured interviews with your customers, executives, and practitioners as a neutral third party — on video, on the record, with consent and usage handled inside the session rather than chased afterward.
- People say sharper things to someone who isn't their account manager.
- Recorded properly from the start, because you can't cut a testimonial out of a call recording later.
REC12:34Mike: We cut reporting cycles from two weeks to three days.
Q: How did leadership react?
One source, compounding leverage
Everything that crosses the Proofbase comes out as leverage.
Capture a source once, and everything made from it — assets, quotes, distillations — lands back in the base as new context: ready to distill again, deploy somewhere new, or build the next thing from.
Sources in
- Verified, on the record
- Consent attached to every quote
- Quality-scored and indexed
Leverage out
All of it is context where the work already happens — Claude, ChatGPT, and your own tools draw on the base through the Proofmap MCP.
And then it compounds
The second interview is worth more than the first.
Each source enriches every other. With a handful in the base, things start forming that no single conversation contains:
Trends
Patterns across stories that no single interview could show you.
Cross-customer buyer language
The phrases different customers reach for independently — the words that convert, in their voice.
Highlight reels
Clips from different sources, cut together into one argument.
Segments
Personas and industries that emerge from what people say — not from your CRM fields.
From invisible to acquired — in 12 months.
Spendgo turned customer interviews into on‑record proof, broke into the restaurant‑tech ecosystem, and exited to Olo in a year.
Read the full case study →
Why this layer, now
Engines don’t cite claims. They cite people.
Buyers increasingly get their shortlist from an assistant rather than a results page, and the assistant is picking what to repeat. Our research on how AI engines choose what to cite points at one conclusion for anyone building a content strategy around it.
Quotations were the single biggest lever on AI visibility
In the Princeton-led study that named generative engine optimization, adding quotations lifted a page's visibility in AI answers by about 41% — a larger gain than statistics (~30%) or citing authoritative sources (~28%). Quotations are the thing engines reach for, and a real one requires a real person who agreed to be quoted.
Almost nobody clicks the citation
When Google shows an AI summary, users click a source inside it roughly 1% of the time, and sessions end after the summary far more often. The click is no longer the prize. Being the substance of the answer — named, attributed, quoted — is.
But the ones who do click are buyers
ChatGPT referral traffic converts at 7.1% — second only to paid search, ahead of every other channel. The traffic is small and unusually qualified, which is exactly the profile that rewards being cited accurately rather than being cited often.
Figures from our own published research on AI citation behaviour — the GEO study findings and the Pew click-behaviour panel we analysed in our writing on why AI engines cite anything at all.
Where Proofmap sits — and what breaks when this layer is missing.
The World’s First LLM Knowledge Graph
Every AI tool claims a better retrieval model — RAG pipelines, vector databases, fine-tuned knowledge graphs. This is half the battle. Proofmap is the first video-indexed knowledge graph for B2B Proof-Led Growth — where every node is a real person, on record, for stronger semantic intelligence and authority.
Schedule a callBuyer's checklist
What to ask any customer proof platform — including us
The category is young enough that the evaluation criteria matter more than the feature lists. These five questions separate proof infrastructure from content storage — use them on us too.
Did the person choose to participate?
Scraped call recordings capture people who didn't know they were being quoted. An intentional interview — the contributor showed up to share, on the record — produces sharper intelligence and proof you can actually use.
Is consent structural, or a spreadsheet?
In the Proofbase, approval is one-pass and inherited: the customer signs off on their words once, and every downstream use carries that consent. No re-asking, no contributor burnout, no legal surprises.
Can your AI actually query it?
A folder of transcripts is not a data layer. The Proofbase is structured and relational — quotes tied to sources, sources to themes, themes to personas — so the AI answering from it retrieves deterministically, not by vibes. And it's served over MCP, so Claude and ChatGPT query it natively — no export, no copy-paste.
Is every output traceable?
When content cites a quote, the buyer can follow it to a real person, on video, who approved it. That traceability is what makes proof believable to skeptical buyers — and citable by AI engines.
Does it compound?
Every interview enriches the graph: new themes, sharper personas, more deployable proof. A competitor who starts later isn't behind by a tool purchase — they're behind by every conversation you've already captured.
And the questions people ask us
Is this software we buy, or a service you run?
How do we start?
What does it cost?
Doesn't our CRM or call recorder already do this?
What if our customers won't go on video?
Who owns the proof?
Want the full picture first?
The Proofbase is the foundation layer — see how capture, verification, and deployment run across a whole revenue motion, then come back through whichever door fits.
See how Proofmap works →If one of these is closer to the truth
Case Study ServicesIf you want to see the motion before committing to the layer: one customer, one interview, the full asset set — and the proof base is real when it's done.ProofcanonIf the harder problem is that your team can't agree on what the company claims, the canon comes before the proof that supports it.Fractional GTMIf you have the proof and the problem is nobody senior is driving the motion that uses it.