Private proof-layer workspace · Handshake × Hive · not indexed, not linked publicly
Complement, not competitor
Handshake measures. Hive proves.
Handshake builds the expert judgment, evals, rubrics, and verifier environments that decide whether a frontier system is good enough to ship. Hive doesn’t re-measure any of it — Hive takes each measurement and turns it into an independently signed, portable, exportable receipt under a post-quantum signature. You keep owning the verdict; Hive makes the verdict provable long after the run.
Handshake decides whether it’s good. Hive proves that the decision happened, on what evidence, and that it holds up on export.
ACT IProven now · what Hive can do for Handshake today
Why third-party attestation is not optional
Handshake supplies the expertise; Hive supplies the independent proof. The two roles stay separate on purpose — that separation is what makes the proof worth anything to a buyer.
A claim, and how it becomes evidenceindependent by design
Handshake supplies the expertise
The verified expert network, the evals and rubrics, the verifier environments — the judgment that decides whether a system is good enough to ship. That work is Handshake’s, and stays Handshake’s.
A self-written receipt is still a claim
A receipt Handshake writes about its own work is a claim about itself — credible, but self-referential. An independent receipt, signed by a party with no stake in the outcome, is what can support a buyer’s or reviewer’s audit, review, and evidentiary workflows.
Hive supplies the independent proof
OriginProof works because Hive is independent: it does not create the expert data, employ the expert, or judge the commercial outcome. It signs the attested conditions of production — and nothing more.
Separation is the product: “trust our experts” becomes “verify our experts.” OriginProof attests conditions, never a verdict — a human-conditioned process with disclosed model-use boundaries, not “provably human output,” and not omniscient proof of hidden off-platform behavior.
What Handshake keeps: the expert relationships, rubrics, scoring, marketplace, and customer workflow. Hive only signs the independent proof layer around them — nothing about the methodology has to change.
Pick a surface. Each shows what Handshake already does, the one place a durable-proof gap tends to appear, and the Hive receipt that closes it. Nothing here asks Handshake to change its methodology — the receipt wraps around it.
Expert data & human model validationverified expert network
Handshake already does
Connects a verified network of experts with frontier labs, supplying human judgment and subject-matter validation of model behavior across many specialties.
Where the proof gap appears
Expert validations are trusted internally, but who validated what, when, and under which mandate isn’t a portable, independently verifiable artifact once the label leaves the platform.
How Hive plugs in
A signed contribution + model-identity receipt binds expert identity (pseudonymous is fine), task, rubric, and timestamp under ML-DSA-65 — attesting the validation occurred and stayed in mandate, verifiable offline.
Evals + rubrics as strategic IPrubric + RL environment
Handshake already does
Treats evals as a rigorous framework to measure and improve AI systems — rubrics, simulation / RL environments, and a governance and quality layer described publicly as strategic IP.
Where the proof gap appears
Scores live in dashboards. Later it’s hard to prove a given score came from a specific rubric version against a specific, hash-bound dataset — exactly what an enterprise or regulator asks for.
How Hive plugs in
An eval-attestation receipt signs the rubric version, dataset hash, and per-criterion result with contamination-proof positioning — a portable object that outlives the dashboard.
Gandalf is described as a reactive agent-as-judge that runs inside the rollout environment, uses the same tools and state as the agent, inspects evidence, and grades rubric criteria.
Where the proof gap appears
The judge’s verdict is authoritative during the run, but it isn’t a signed, portable artifact a downstream auditor can independently re-verify without relying only on the harness.
How Hive plugs in
Wrap each Gandalf judgment in a signed verifier receipt — judge identity, criteria graded, evidence pointers, and verdict — verifiable offline, no access to the original environment required.
Publishes end-to-end investment-banking tasks with multi-file deliverables and expert-authored rubrics, scored by Gandalf — a domain benchmark that mirrors real regulated work product.
Where the proof gap appears
A multi-file deliverable plus its score is exactly the kind of artifact a regulated reviewer wants chain-of-custody on — which file, which version, scored under which rubric, by which judge.
How Hive plugs in
A receipt bundle over the deliverables and their scores, in a Verified Regulated posture — audit-grade, content-sealed, and exportable to a reviewer as one verifiable package.
With the Cleanlab acquisition, deepens work across evaluations, AI safety, RL environments, and frontier data specifications — including finding and repairing label / data-quality issues.
Where the proof gap appears
The fixes themselves are process steps — issues flagged, corrections made — that customers and regulators increasingly want demonstrated, not just asserted, and tied back to the eval that used the data.
How Hive plugs in
A data-quality receipt records dataset version in / out, issues flagged, what corrected them, and the mandate — then links to the contamination-proof eval attestation that consumed the cleaned set.
Production monitoring & runtime callsevals from day one
Handshake already does
Publicly describes running evals from day one — golden datasets, pre-launch validation, and production monitoring / tracing across LLM use cases.
Where the proof gap appears
Traces show behavior, but they aren’t independently signed evidence per call, and the pre-launch eval attestation isn’t bound to what the model actually did once live.
How Hive plugs in
AFiR emits an independent ML-DSA-65 receipt for every runtime move; AFiR-Stream covers voice / audio and streaming turns — tying day-one eval attestation to live behavior.
Hive primitives — and which one fits which Handshake workflow
Every Hive receipt is signed with ML-DSA-65 (NIST FIPS 204 post-quantum signature). Tap a Handshake workflow to see which primitives apply; the matching cards light up green.
applies ✓
AFiR™
Attested Fragmented Inference Routing
Every AI inference sub-step cryptographically attested with ML-DSA-65 (NIST FIPS 204).
A signed conditions-of-production receipt for expert human work. Independent, tamper-evident attestation of the conditions under which the work was produced — a credentialed human under attested conditions, with disclosed model-use boundaries. Attests conditions, never a verdict.
Teams many receipts into one signed proof vector — verification depth, the weakest proof boundary, a healing / recoverability state, and the permitted next actions. Honest about the group; never over-claims what the underlying receipts carry.
Takes an R3Pv vector, applies a declared policy pack, and returns a signed decision — permit · permit_with_evidence · require_approval · hold · recover · reroute · escalate · block — with permitted next actions and an exportable evidence bundle. The meter is a quote, not a settlement; overrides can only make policy stricter.
Every receipt lands in a searchable ledger with a public verifier — anyone can re-check a receipt offline against the public key, without asking Hive for permission. The ledger is convenience; the receipt is proof.
Signs payment and economic events at explicit evidence tiers — self-attested, relay-observed, chain-verified — for expert-marketplace and agentic settlement. Never claims a stronger tier than it earned.
OriginProof · Human-Origin Attestation — proof the expert data was human-conditioned
OriginProof is the market-facing name for the Human-Origin Attestation primitive: a signed conditions-of-production receipt for expert human work. One of the core risks in expert data is undisclosed model substitution: work presented as human where the model-use boundary was never disclosed. As a credentialed expert produces work, OriginProof binds a signed record of the production conditions — credential clearance, session integrity, environment, behavioral process signals, and a disclosed-tools declaration — into the same provenance graph as the output. A buyer can independently verify the work was produced under attested conditions with disclosed tool-use boundaries, without relying only on platform-side assertions and without exposing the work’s content.
OriginProof attests conditions, never a verdict. It asserts a human-conditioned process with disclosed model-use boundaries — not “provably human output.” It does not assert definitely_human or certainly_uncontaminated; it makes undisclosed model-substitution detectable and costly to conceal, not impossible.
Hive proves the conditions it observes or receives. It does not claim omniscience over hidden off-platform behavior — that boundary is explicit in the receipt, which is exactly what keeps it credible to a buyer or reviewer.
OriginProof receipt — per-label verification, toggle the bound conditions5 / 5 conditions signed
signed verification output · per label · conditions, not a verdict
Attests process conditions and declarations only — never a human verdict on the output. Composes on Imprimatur (credential clearance), AFiR / AFiR-Stream (output signing), assembly-graph / dataset provenance, mission-coherence / task-conformance, and the constitutional mint / independent signer.
Model routing, receipted — SmartAgent™ & SmartMorphAgent™
SmartAgent™ routes a task across model stops and signs a route receipt at every hop. Flip the constraints to reshape the route; turn on SmartMorph mode when an agent mutates capability, payload, or footprint mid-run — each morph is receipted alongside the route, with a live x402 quote. No model prices are invented here; tiers are illustrative stops.
Resolved route + receipt chainSmartAgent™
Proof-state — honest boundaries, grouped into a decision
A receipt states how an event was witnessed, and never dresses a weaker witness up as a stronger one. That honesty is what makes a receipt worth exporting. R3Pv groups receipts into one signed proof vector; Protected Flow turns that vector into a signed, policy-bound decision. Both are live now — the practical guardrail and evidence layer on top of the receipts above.
Evidence tiers — the proof boundary is stated, not hiddenhonest by design
self-attested
Another system described the event and Hive signed the description as stated. The signature proves the claim was made and unaltered — not that it is independently true.
relay-observed
Hive itself made the call and signed hashes of the request and response, so the receipt reflects what Hive directly saw, not only what it was told.
chain-verified
Confirmed against an independent source — e.g. reading a transaction back from a public chain when a tx hash is supplied.
A chain is only as strong as its weakest link. R3Pv carries the weakest proof boundary forward, so a mostly-verified group is never marketed as fully verified. Read: proof-state →
R3Pv proof vector → Protected Flow signed decisionlive now
Protected Flow returns one of eight decision classes with permitted next actions and an exportable, offline-verifiable evidence bundle. The meter is a quote, not a settlement — no funds move, and overrides can only make policy stricter, never looser. Like R3Pv, it never reverses finalized chain transactions. Live endpoints: GET /v1/protected-flow/health, POST /v1/protected-flow/assess.
Proof-State Fleets — organize the proof, and it becomes a product
A receipt is not paperwork — it is infrastructure. Once expert-data work is independently receipted, the proof-state can be organized at scale and packaged: a premium independently-receipted expert-data tier, dataset-provenance bundles labs can check without trusting platform logs, and a machine-readable substrate other systems can query. This capability is live now; how Handshake packages it is Handshake’s call.
Atomic
Receipt
One signed proof object — an eval run, an expert session, a model output — with machine-readable provenance.
Grouped
R3Pv proof vector
Related receipts grouped into one signed, queryable vector that carries the weakest proof boundary forward.
A whole workflow held as a signed, policy-bound proof-state decision with an exportable evidence bundle.
Enterprise-scale
Protected Flow Fleet
Protected flows organized by customer, dataset, expert cohort, task type, eval rubric, model family, or delivery batch.
Organize a fleet by customer · dataset · expert cohort · task type · eval rubric · model family · delivery batch — each grouping is a unit a lab can be handed, an auditor can re-verify, and Handshake could attach a trust tier to.
How Handshake capitalizes — proven-now capability, Handshake’s packaging choice
Trust tier
Premium independently-receipted expert-data tier
Offer buyers a tier where the expert data arrives with independent receipts, not only platform logs — the customers who must prove provenance opt in.
For labs
Dataset provenance bundles
Ship a lab a whole-dataset provenance bundle — sources, expert cohort, conditions of production — that survives audit as its own artifact.
Receipt the evaluator identity, the session, and the produced output — the three places a lab’s reviewer asks “who, under what conditions, and can I check it?”
Human-origin
OriginProof attestation
Attach human-origin attestation to a fleet — conditions of production, disclosed model-use boundaries — conditions, never a verdict.
Fleets land in an independent, ledger-backed record other systems and agents can query programmatically — proof-state as machine-readable infrastructure.
Recovery is honest by rail and state: a fleet’s protected flows record only the recovery the system can genuinely offer — a pre-broadcast stop or revoke, an escrow / quarantine hold, an issuer / custodian freeze where integrated, a counter-transfer, or evidence-only once settlement is final. It never implies a finalized public-chain transaction can always be reversed. See also Receipt Relay for cross-boundary carriage.
Monday pilot — one fleet: pick one high-risk workflow (an eval batch, a dataset delivery, an expert cohort) → receipt it → group into an R3Pv vector → protect the flow → export a verification bundle a lab can check offline. One fleet, one workflow, one exportable proof.
Act I · proven now · interactive
Set up Handshake — see your FlowFleet. Enter your product lines and operating scale. Every number is a mechanical planning estimate from your inputs — not a quote, not a legal guarantee, no pricing.
Your setup in · FlowFleet benefit outlive · updates as you type
progs
/day
/day
Signed session receipts / yr0
Output / label / eval receipts / yr0
Total signed evidence events / yr0
R3Pv proof vectors / yr0
Addressable evidence bundles0
—
Fleets group by customer, dataset, expert cohort, eval rubric, model family, and delivery batch.
Planning estimates only — receipt and vector volumes scale mechanically with your inputs. No revenue, pricing, dollar savings, or legal guarantees are computed or implied. The manifest is an illustrative example, not a live integration.
Enter your own assumptions to estimate the operational proof footprint — how many signed evidence events your workflow would produce and export. This is a planning estimate, not a quote: no pricing, no economics, just receipt volume.
Your assumptions in · proof metrics outlive · updates as you type
runs
/eval
/eval
calls
%
/call
%
Eval-attestation receipts / mo0
Verifier + inspection receipts / mo0
Runtime receipts / mo0
Total signed evidence events / mo0
Daily receipt rate0
Export / audit bundle volume / mo0
Suggested pilot size (2-week slice)0
Operational proof metrics only. No revenue, pricing, subscription, or deal-size figures are computed or implied — commercial terms are handled separately.
Proof-bundle builder
Click the evidence a pilot would sign. Each component adds its fields to the exportable bundle preview — the same shape a reviewer would re-verify offline.
Receipts for authority, consent, and fair-process steps around a human decision.Attests that the process step occurred — Hive does not make or automate hiring decisions.
This hits Hive’s public, no-auth x402 quote endpoint and returns a live settlement envelope with an ML-DSA-65 (FIPS 204) signed quote receipt. No key, no secret, nothing to install.
POST · https://receipts.thehiveryiq.com/v1/x402/quote
The response includes the rail (USDC on Base), a signed quote receipt, and its ML-DSA-65 / FIPS 204 post-quantum posture. To emit a paid receipt end-to-end, walk the live flow at /x402-checkout/; to start a pilot, request an activation key at /activate/. No secrets are ever placed on this page.
What could start Monday
Four concrete artifacts, each scoped to a single workflow — small enough to stand up quickly, real enough to hold and re-verify.
Expert workflow
One signed expert session
A single expert contribution wrapped in an OriginProof conditions-of-production receipt — credential, session, disclosed tool-use.
ACT IIBuild next · where the same proof layer can expand the offering
Where the same proof layer could take Handshake next
Act I proves today’s expert-data work — making the existing supply chain independently provable. Act II shows where the same independent proof layer can expand the offering: new categories the existing verified expert network could open over time. These are strategic options to weigh, not a promise that everything is live today.
Every category below rests on the same principle: an independent attestor. An organization can’t meaningfully attest its own work — which is exactly why an outside proof layer adds value, and what makes these lines harder to replicate. Each primitive below maps to Hive proof infrastructure and should be reviewed with counsel before any external launch.
Candidate line 01
Certified human-conditioned work
OriginProof · human-origin attestation
The categoryProvably human-conditioned work, offered outward — to journals, publishers, hiring platforms, marketplaces, and review networks facing the “is this AI?” question.
Where Handshake fitsThe existing verified expert network is the supply. Work already produced under attested conditions could be certified for others’ use, not only the labs’.
OriginProof attests conditions, never a verdict — a human-conditioned process with disclosed model-use boundaries, not “provably human output.” That honest boundary is what keeps it credible to a buyer.
Candidate line 02
Portable Credential Attestation
Imprimatur · credential clearance
The categorySigned, portable proof of a verified credential — e.g. “a credentialed subject-matter PhD” — that any third party can check without calling back to the platform, and without exposing private details.
Where Handshake fitsCredential verification already happens to onboard experts; today it stays on-platform. Here it could become a portable product, built on the credential-verification relationships already maintained.
A candidate product line: portable credential proof, resting on verification work already performed.
Candidate line 03
Provable Expert Review
Mission Coherence · conformance
The categorySigned conformance attestation for expert-reviewed regulated work — a protocol, a legal review, or an audit deliverable checked against a rubric, provably.
Where Handshake fitsExperts already review and validate, not only label. This could turn expert review into a signed, regulated-grade service.
Regulated industries value provable expert review; the experts are already in place, and the proof is what would be new.
Candidate line 04
Regulated Research Assembly
Assembly Receipt · dataset provenance
The categoryProvably-sourced, expert-assembled research datasets for clinical trials, regulatory submissions, and evidence packages — where the provenance graph is itself the compliance artifact.
Where Handshake fitsWhole-dataset provenance isn’t only for AI training — it applies to any assembled expert body that must survive audit.
Audit-surviving research is a candidate category; the provenance graph is the audit trail.
The categoryAI agents purchase provable human expertise on demand — an agent reaches a step that needs a credentialed human, buys that verification in a marketplace, and receives it against signed proof, settled on-rail via x402.
Where Handshake fitsThe existing verified expert network could become the human-in-the-loop layer the agent economy calls into — where agents that need provable human sign-off buy it, delivered with receipts.
A future platform option: the network supplies provable humans on demand; Hive supplies the rail and the proof. This category doesn’t exist yet — part of the upside, and part of the risk to weigh.
The categoryPackage proof-state at scale — fleets of protected flows organized by customer, dataset, expert cohort, task type, eval rubric, model family, or delivery batch — sold as a premium trust tier, a compliance export, and an insurer- or regulator-grade evidence layer.
Where Handshake fitsThe receipts, R3Pv vectors, and Protected Flows already exist per workflow. This line is the organizing and packaging model on top — turning the proof already produced into a recurring surface, plus a ledger-backed substrate other systems can query.
The receipt is infrastructure, not paperwork. Once proof-state is organized into fleets it becomes a product surface an independent layer makes possible — a strategic option to weigh, not a claim that a packaged SKU is live today.
Act I proves what Handshake sells today. Act II points the same independent proof layer at new categories — each a strategic option to weigh, harder to replicate because independence can’t be self-supplied, and none of them a claim that the line is live today.
Handshake measures. Hive proves. Together the work isn’t only certified — it can open categories an independent proof layer helps unlock.
Sources
Public references for the Handshake statements cited above. Where numbers appear on those pages, treat them as source-reported, not asserted here.