Trust infrastructure for prediction markets

Stop adjudicating disputes.
Plug in the protocol.

A B2B middleware layer that resolves contested outcomes for prediction markets, parametric DeFi, and onchain insurance — through AI evidence aggregation, reputation-weighted jurors, and onchain settlement. Customers stop building dispute teams. End users get transparency. You capture a take-rate on every resolution.

$3.2B
Polymarket annual volume (2024)
~5%
Of markets generate disputes
14h
Median resolution time today
0
Standalone B2B competitors
The problem

Disputes are how prediction markets lose user trust.

Every prediction market faces the same problem: ambiguous market wording, edge-case real-world events, and motivated reasoning by large stakeholders. When something goes wrong, resolution gets handled in a Discord thread or an opaque internal review — and users lose confidence.

Opaque rulings

Polymarket's Ukraine-suit market resolved against community sentiment via UMA whale votes. Kalshi rules internally. Manifold lets the creator decide. None of this scales as markets professionalize.

Operational tax

Every market builds the same ad-hoc dispute machinery. Lawyers, ops staff, on-call engineers reviewing screenshots. It's expensive, slow, doesn't scale across categories, and creates regulatory liability.

Vulnerable oracles

Existing decentralized oracles (UMA, Augur, Kleros) have known capture vectors: token-weighted voting favors whales, jurors copy obvious answers, and dark-DAO bribery markets are already a documented attack.

Real cost of a contested resolution

Polymarket lost an estimated $4-8M in user retention after the Ukraine suit dispute went viral. Kalshi has a four-person disputes team that's purely a cost center. Smaller markets (Manifold, Zeitgeist) lose users every time a creator-resolved market is alleged to be unfair. No one has solved this — we will, and sell it back to all of them.

How it works

A dispute, end to end.

Here's exactly what happens from the moment a market closes to the moment funds settle. Click any step to expand.

1

Optimistic resolution

~95% of cases end here

Anyone proposes the outcome with a small bond. If unchallenged within the window, it settles automatically.

What happens
  • Market closes, anyone proposes outcome with bond ($50–$500 default)
  • Optimistic challenge window opens (default 24-72h, configurable per market)
  • If unchallenged, market settles automatically; proposer gets bond back + small fee
Why it matters

Most outcomes are unambiguous. Optimistic resolution keeps the cost of routine settlement near-zero and reserves expensive jury machinery for the cases that actually need it.

2

Dispute filed

Triggers escalation

Anyone can dispute by posting a counter-bond. Both bonds enter escrow; the wrong side gets slashed.

What happens
  • Disputer posts matching bond + a fixed dispute filing fee
  • Market is flagged "in dispute" and frozen from further settlement
  • AI evidence agent triggered automatically
Skin in the game

Bonds are large enough to deter frivolous disputes but small enough that legitimate ones aren't priced out. Curve scales with market volume.

3

AI evidence aggregation

Our moat

An agent gathers and ranks evidence — news, official sources, onchain data, social signal — and produces a structured brief.

What happens
  • Agent crawls reputable news, official sources, court records, market data
  • Each source rated for credibility, recency, stance (YES/NO)
  • Generates a structured brief: headline ambiguity, key facts, confidence score
  • Outputs are non-binding — they inform jurors, they don't decide
Why this is the moat

Smart contracts and DAO voting are open-source commodities. The proprietary evidence engine — trained on our resolved disputes, with a credibility graph that compounds — is what nobody else can clone.

4

Jury empanelment

Domain-specialized

A reputation-weighted jury is randomly selected from the relevant domain pool (politics, sports, crypto, real-world).

What happens
  • Dispute classified by category, routed to specialized juror pool
  • 15 jurors selected via VRF, weighted by domain reputation
  • Stakes auto-locked from each juror at selection
  • Voting window opens (24–48h)
Why specialization

A juror who's resolved 200 NFL disputes correctly is more credible than a generalist. Reputation is non-transferable — earned by voting with eventual consensus, slashed otherwise. Whales can't buy influence.

5

Commit-reveal voting

Anti-collusion

Jurors submit encrypted votes with mandatory reasoning. Reveal phase exposes everyone's vote simultaneously.

What happens
  • Each juror commits hash(vote + reasoning + salt) — vote is hidden
  • After voting closes, reveal window opens — votes published all at once
  • Reasoning becomes part of the public record
  • Future juries can rate prior reasoning — recursive quality signal
Why commit-reveal

Prevents herding ("just copy what everyone else is voting"). Mandatory reasoning blocks lazy jurors. Recursive evidence ratings reward principled minority dissent on close calls.

6

Settlement

Onchain

Continuous slash curve based on consensus distribution. Bonds resolved, rewards distributed, market unfrozen.

Settlement flow
  • Winning side's bond returned + portion of loser's bond
  • Jurors slashed continuously (you lose more if your dissent was lopsided)
  • Reputation updated: ±votes-with-consensus
  • Market settled, payouts released
Where the money goes
  • Correct disputer~50%
  • Jurors (correct votes)~30%
  • Insurance pool~10%
  • Protocol fee~10%
7

Optional escalation

Rare

For irresolvable disputes: tiered appeals with increasing bonds, full DAO vote, or market fork as the nuclear option.

Tier 2 appeal

Larger jury (45+), 2x bond requirement. ~3% of disputes reach this.

Tier 3 DAO vote

Full token-holder vote. Highest cost, highest bonds. ~0.5% of disputes.

Market fork

When wording is fundamentally broken: split into two markets, holders self-select.

What you sell

Four surfaces. One protocol.

Each view is a separate sales artifact. Operators see one thing, jurors see another, end users see a third — and the public auditability of the archive is the marketing flywheel.

Revenue

Five revenue streams. Compounding.

Ranked by realism for years 1–3. The protocol is decentralized for credibility. The company is a SaaS business with traditional revenue, traditional accounting, and an obvious acquisition path.

01

Take rate on dispute bonds

Highest priority Recurring No sales cycle
Year 2 estimate
$1.2–4M ARR

1–3% protocol fee on every bonded resolution. Scales linearly with prediction-market volume. Predictable, defensible, no enterprise sales motion required.

Math: 5% of $5B annual market volume = $250M disputed → 2% take = $5M/yr at saturation. Even capturing 20% of crypto-native markets = $1M ARR by year 2.
02

SaaS subscriptions for premium features

High margin Predictable
Year 2 estimate
$400K–1M ARR

Tiered pricing for customers who need more than the base protocol: priority resolution SLAs, custom jury composition, white-label UI, dedicated support.

Free
$0/mo
< 50 disputes/mo
Growth
$2k/mo
SLAs, dedicated jurors
Enterprise
$15k+/mo
White-label, custom
03

Enterprise contracts

Big checks Long sales cycle
Year 3 target
$2–8M ARR

Polymarket, Kalshi, sportsbooks, parametric insurance protocols. $250k–$2M/yr contracts with custom terms, on-call support, and integration engineering. These take 6–12 months to close but each one is a significant revenue line.

04

Data licensing

Long tail Optionality
Year 3 estimate
$200–800K ARR

Anonymized resolution patterns, accuracy benchmarks, juror behavior data. Sold to event-driven hedge funds, research firms, and competing oracles. Becomes meaningful only after volume builds — a strong year 3+ play.

05

Insurance pool spread

Speculative Reg-sensitive
TBD

Small spread on dispute insurance pool yield. Real money but raises serious regulatory questions (running an insurance pool ≠ running a protocol). Probably year 3+ once legal posture is settled.

Roadmap

What it takes to get there.

Four phases, 36 months. Honest about what we don't know, what we need, and what we'd want to validate before each step.

Phase 1 · Months 0–4

MVP and design partners

Pre-revenue
Build
  • AI evidence engine v1 (proprietary)
  • Bond + escrow smart contracts (single chain)
  • Operator dashboard + juror console (this demo, productionized)
  • SDK for integration (REST + onchain)
Validate
  • 2–3 design partners (Manifold, Zeitgeist, a small DAO)
  • Run 50+ test disputes against historical data
  • AI evidence accuracy > 90% vs. human ground truth
  • 3 LOIs from prediction markets
Team needed: 2 engineers (smart contracts + AI/ML), 1 founder doing BD/design. Capital: $300k–$500k pre-seed.
Phase 2 · Months 4–10

Live pilot and tokenomics launch

First revenue
Build
  • Reputation graph + juror onboarding
  • Commit-reveal voting infrastructure
  • Token contracts (governance + work tokens)
  • Insurance pool primitives
Validate
  • Live disputes with real bonds (capped, small)
  • Bootstrap 100–250 reputation-staked jurors
  • $50k MRR via take-rate by month 10
  • Smart contract audit complete (Trail of Bits / Spearbit)
Team needed: 5–6 (add: BD lead, second smart-contract engineer, ops). Capital: $2–3M seed round.
Phase 3 · Months 10–24

Scale to enterprise customers

Growth mode
Expand
  • Multi-chain deployment (Base, Arbitrum, Polygon)
  • Tiered escalation (multi-jury, DAO appeals)
  • White-label UI for enterprise customers
  • DeFi integrations (parametric insurance, derivatives)
Validate
  • Polymarket / Kalshi / major sportsbook signed
  • $1.5–3M ARR run rate
  • 500+ active jurors across domains
  • Series A ($10–15M) closed
Team needed: 15–20. Engineering, BD, legal, marketing. Capital: Series A.
Phase 4 · Months 24–36

Default settlement layer

Acquisition target

Become the default resolution layer for crypto-native event markets. Launch the data product. Sign exclusive deals with category leaders. Acquisition window opens — the buyers are the markets themselves (Polymarket, Kalshi, FTX-successors), oracle protocols (Chainlink), or compliance/data firms (S&P, Bloomberg).

ARR target
$8–15M
Comparable exits
$80–250M
Path
Acq. or B
Compare

Why this isn't UMA, Kleros, or Augur.

Existing oracles solve part of the problem and get acquired by their largest customer or stagnate. Our differentiation is the AI evidence layer (proprietary), domain-specialized reputation (compounding), and being a B2B middleware company (not a token bet).

Capability Us UMA Kleros Augur Internal teams
AI evidence aggregation
Domain-specialized reputation
Whale-resistant voting
Public auditability
B2B middleware (sells to markets) N/A
Continuous slash curve
Insurance pool
Cost to customer per resolution $5–50 $0 + bond $10–100 High $200–2k
The honest read

UMA is locked in with Polymarket and won't be displaced for that customer. Our wedge is the long tail — the other 50+ prediction markets, parametric DeFi protocols, and DAO governance systems UMA doesn't serve well. Win those, then pitch the whales.

The real risk

UMA or Chainlink build their own AI evidence layer. Mitigation: ship first, capture juror network, build proprietary reputation graph that compounds. By the time they react, switching costs are real.

The ask

What we need to make this real.

Honest list. This is what gets us through Phase 1 and into the first revenue.

People

  • Smart-contract engineer (Solidity, audited prod experience)
  • AI/ML engineer (LLM agents, evidence retrieval, eval)
  • BD lead with crypto/prediction-market network
  • Securities lawyer on retainer (not optional)

Capital

  • $300–500k pre-seed for MVP and design partners
  • $2–3M seed to launch tokenomics and audit
  • Likely investors: Variant, Multicoin, Paradigm, 1confirmation, crypto-native angels
  • Audit budget: $80–150k (Trail of Bits, Spearbit, OpenZeppelin)

Partnerships

  • 2–3 design-partner prediction markets willing to pilot
  • 1 DAO with governance disputes (Optimism, Arbitrum)
  • 1 parametric insurance / derivatives protocol
  • Data partnerships: Reuters, AP, Bloomberg API access
Open questions we'd want to validate before raising
  • Will prediction markets actually pay rather than build internally? (LOIs from 3+ markets)
  • Can our AI evidence engine outperform a human ops team on accuracy? (offline eval)
  • Where are jurors going to come from in cold-start? (incentive design test)
  • What is our exact regulatory posture in the US? (counsel opinion)

Build the trust layer
prediction markets are missing.

The demo is real. The market is real. The path to revenue is concrete. What's needed now is a team and the runway to ship Phase 1.