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PolicyCheck · Confidential · March 2026
01 — Cover
Confidential · Pre-Seed · March 2026
Structured Policy Intelligence
Lloyd's Lab Cohort 16  ·  1 of 3 AI companies from 225
"I built SPI after watching a client's $1.2M claim get denied on a clause no one had read. I knew AI could fix this — so I built the fix." — Simon Archer, CEO & Co-Founder
02 — The problem
A real scenario
"We reviewed the policy. Everything looked fine."

A commercial broker places a $4M property policy for a food manufacturer. The broker reads the schedule. The client signs. Then the pipes burst.

What the broker missed

§7.1 excluded "gradual deterioration." §11.3 defined "sudden" as under 72 hours. The $1.2M claim was denied. The broker faced a professional indemnity suit.

This is not a rare edge case. It happens every day — not because brokers are careless, but because modern policies are structurally too complex to read manually.

The problem
Three questions
no broker can answer.
1
What am I covered for?
2
What am I NOT covered for?
3
What should I be covered for?
$2.4B
Annual value of claims denied or disputed in ANZ due to coverage gaps
34/40
Brokers interviewed had experienced a client dispute over a policy they'd recommended
03 — The product
What we built
Structured Policy Intelligence
000's of policies normalised 10,000+ terms indexed Working — not a prototype
Document Ingestion PDF Ingests & indexes Policy Analysis & Understanding Clause mapping ! Exclusions & gaps Definitions & terms Analyses clauses, exclusions & definitions Findings & Recommendations Coverage gaps Exclusions Recommendations Auditable output ! Gap identified Coverage gap found Exclusion highlighted Exclusion detected Recommendation Actionable insights
04 — The insight
The insight that changes everything
A general LLM
reads words.
SPI reads
structure.

That's the difference between a summary and an audit. Between a search result and a liability-proof recommendation.

✕

General LLM

"Section 7.1 excludes gradual deterioration." — and stops there.

✓

SPI

§7.1 exclusion + §11.3 definition of "sudden" + §4.2 coverage trigger = this client has no cover for pipe failure. Flag it. Recommend endorsement.

Why no one else can replicate this
000's

Policies normalised

Each policy annotated deepens the graph. Every new customer adds policies. The corpus compounds — it is not a head start, it is a network effect that widens with every upload.

10,000+

Insurance terms indexed

SPI knows that "occurrence" means different things in liability vs property. That "bodily injury" cascades through 6 dependent clauses. A general model does not.

12 mo

Exclusivity window

Lloyd's Lab gives us their proprietary databases and coverholder networks. No competitor touches this for at least 12 months. Each month widens the gap.

05 — Traction
Traction
Validated by market
and institution.
3
Brokers in active pilot — converting to paid Q2 2026
14
Coverage gaps found in first pilot: 6 policies in 2 hours vs 3 days manual
1/3
AI companies selected — Lloyd's Lab Cohort 16, from 225 global applicants
2
ANZ broker groups in commercial LOI discussion
Lloyd's of London

Europe's #1 Fintech Sandbox · Cohort 16 · Financial Times ranked #1 globally among insurance innovation programmes · 97% of alumni still operating · $1.7B raised by alumni

225
applicants
12
selected

Pilot broker quote

"PolicyCheck found 14 gaps we missed across 6 policies in under 2 hours. That's 3 days of work."Pilot broker, ANZ (NDA)

Platform status

Multi-tenant, cloud-native. SOC 2 certification in progress. Onboards broker groups — not individual brokers one at a time.

Lloyd's Lab April 2026

James Farrell embedded full-time. Revenue engagements with Coverholders & MGAs commencing. This is not a research pilot — it's commercial.

06 — Market
The market
$300B+

Global insurance brokerage commissions. ANZ is the beachhead. Lloyd's is the bridge. The world is the market.

ANZ beachhead: $26B+
Lloyd's + UK market: $90B+
Global addressable: $300B+
Bottom-up: ANZ beachhead
ANZ insurance brokerages
25,000+
Realistic Y2 penetration (0.6%)
150
Target ACV per brokerage
$24K
Y2 ARR (ANZ alone)
$3.6M
Pricing model
$2,000/mo per brokerage ($24K ACV) · Scales by policy volume
LTV estimated at $72K+ (3yr avg retention)
CAC target: <$4K via broker group channel
LTV:CAC ratio: 18:1
07 — Business model
Business model
Two phases.
One platform.
1
Policy Intelligence Platform Now → Year 3
Subscription SaaS · ANZ → UK/Europe via Lloyd's Lab · $24K ACV · grows with every policy uploaded
$3.6M
ARR · Year 2
2
Global standard for structured insurance data Year 3–5
The Bloomberg Terminal of insurance — every policy in every market structured, comparable, and auditable. Platform licensing to carriers, MGAs and regulators.
$50M+
ARR · Year 3–5
$2K/mo
Per brokerage · scales with policy volume · annual contracts
$72K
Estimated LTV at 3yr retention · 18:1 LTV:CAC ratio
Q2 '26
First paid contracts converting from current pilot cohort
Why only two phases: Phase 1 is defensible SaaS with a clear sales motion. Phase 2 is a natural extension once the corpus validates AI-generated advice. We are not a brokerage roll-up — we are a software company that compounds data advantage into an advice business.
08 — Competition
Competitive landscape
The honest
comparison.
PolicyCheck
Clause-level AI
Exclusion interaction
Gap recommendations
Auditable output
Lloyd's data access
Insurance-native
Weakness: early, limited languages
General LLMs
(ChatGPT, Gemini)
Surface reading only
No exclusion logic
No gap analysis
Not auditable
No Lloyd's access
Generic, not insurance
Strength: fast, cheap, known
Doc-review SaaS
(Kira, Luminance)
Clause extraction
Basic exclusion flags
No gap analysis
Enterprise audit trail
No Lloyd's access
Legal — not insurance
Strength: enterprise UX, scale

The structural difference: SPI understands that §7.1 modifies §4.2 via the definition in §11.3. No competitor models clause interactions at this level. That cascade logic is built from thousands of policies. It cannot be prompted into existence.

09 — Team
The team
The Critical Question: Why will these four people beat every team that could attempt this?

Simon Archer

CEO & Co-Founder

Built the AI from scratch. Insurance technology architect for 12 years. Spent 3 years annotating 270 policies at clause level before writing a line of product code. The only person in ANZ to have done this.

Unfair advantage: the corpus. No one else has it.

Andrew Clouston

Co-Founder & Strategy

30+ years at AMP, BT Financial, and IAG. Has sat in the boardrooms of every institution we are selling to. Structured the SAFE, governs the cap table, leads investor relationships.

Unfair advantage: the rolodex. Every door opens.

John Peters

GM Customer Relations

20+ years in NZ broker and insurer relationships. Former national sales lead at a top-5 ANZ brokerage. Personally knows the decision-makers at every broker group we are targeting.

Unfair advantage: the relationships are pre-existing.

James Farrell

GM UK/Europe

Former Lloyd's market practitioner. Direct relationships across Coverholders and MGAs. Embedded in Lloyd's Lab full-time from April 2026. The UK is the primary global scale market — James is already inside it.

Unfair advantage: inside Lloyd's. The world's most important insurance market opens from within.
10 — The ask
$600K
NZD · SAFE note · 20% discount cap
Runway to Sep 2026

Lloyd's of London has already validated this — 1 of only 3 AI companies selected from 225 applicants. You are getting in before the seed round.

Come with us
The opportunity
is right here.

The problem

Policies too complex to read manually. Brokers pay with their reputation.

The product

SPI reads clause structure — not just words. 000's of policies. Working today.

The validation

Lloyd's Lab Cohort 16. FT #1 globally. 3 pilots. LOIs in progress.

The vision

The Bloomberg Terminal of insurance. Every policy in every market structured and auditable. Upstream of Guidewire, Applied Systems, every carrier.

Use of funds · $600K NZD

3 engineers hired (75%) · Infrastructure & AI compute (13%) · SOC 2 certification (8%) · Legal & Lab travel (4%) — all directed at converting pilots to revenue and executing Lloyd's Lab

[email protected]
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