Quarri - Teaser deck (Cooley)

Quarri

AI-native operations for mid-market industrials.

Problem

The optimisation problem.

Supply in

Purchased / extracted

→

Upstream inventory

Pre-transformation

→

Transformation

Manufacturing

→

Downstream inventory

Finished goods

→

Customer POs

Sales pipeline

Regulatory reporting

e.g. stumpage returns

AP / vendor payments

Suppliers, extraction fees

AR / accounts receivable

Customer collections

Extraction and manufacturing — multifaceted and complex optimisation.

Stock-outs

Lost revenue

Cash flow

Working capital tied up in inventory

Aged inventory

Carry cost erodes margin

Throughput vs lead time

Production vs input availability

SKU margin

Invisible across systems

Account margin

Customer-level profitability

The Gap

Mid-market lacks the tools to optimise.

SMB

Mid-Market ($10–200M)

The Gap

Enterprise

Products

NetSuite · QuickBooks

Excel + legacy ERP

SAP · Snowflake

Customisation

None — off-the-shelf is enough

None affordable → Excel workarounds

Palantir, in-house data teams (~$400k+/yr minimum)

Optimisation

Simple — not required

Stuck in Excel

Custom data teams

94%

Spreadsheet error rate

65%

Analyst time on data gathering

The Stack

Deterministic toolkit. Agentic orchestration.

Horizontal AI General-purpose LLMs

Anthropic

OpenAI

Open source

Model-agnostic surface. Skills ship to whichever LLM the customer uses.

Quarri · An MCP plugin

PRODUCT LAYER

Skills

Cash flow Account margin Throughput vs lead time Inventory optimisation

INFRASTRUCTURE LAYER

Deterministic execution

100+ MCP tools Versioned workflows

Context layer

Semantic model Vector store Company glossary Memory of work

Data layer

Data Warehouse Agentic modelling Company schema RBAC Read + write

Connectors & ingestion

Legacy ERP Spreadsheets Databases PDFs External

Deterministic infrastructure.

Persistent · Cheaper · Faster · Contextualised · Vastly larger data.

Market

$52B global market. $2.25B serviceable today.

$52.2B

Global TAM

Mid-market industrials · GDP-scaled

$28.1B

4-geo TAM

US + EU + UK + Canada · bottom-up

$10.7B

SAM

Target industries · SaaS >74% · ~81% GM

$2.25B

SOM · Addressable now

SaaS >65% · ~79% GM

Key SOM verticals

Forestry & Lumber

Existing customer base · 60% SOM

Manufacturing

Target vertical · 60% SOM

Wholesale & distribution

Target vertical · 60% SOM

Bottom-up: US Census + Eurostat + ONS + StatCan firm counts × Quarri ACV in 4 geos. Global TAM extrapolated by IMF 2025 GDP. SOM modifiers: US/UK/CA 1.0× (signed deals), EU 0.1× (regulatory/language friction). Russia excluded.

Traction

3 months post-MCP.

Research & Development · months 1–6

No target verticals

100+ product interviews across industries
Hand-cranked pilots
Stress testing pricing
Developing infra
No live deployments

Commercial proof · months 7–9

$47k ARR

Working pipeline

$130k

Contract expansion / pilot conversion

$300k+

Qualified pipeline

3

Anchor customers

Pipeline concentration: Forestry, Mills, Manufacturing

Concentration: 1 anchor account currently 77% of ARR. Q3 2026 target: 5+ accounts, no single account >25%.

Revenue mix & margin

SaaS 75% 90%+ gross margin

Services 25% 40% gross margin

Already SaaS-dominant. Services is the wedge into mid-market; agentification pulls services margin up over time → see The Flip.

Market Context

Why foundational models won't build this.

100× cheaper per execution

LLM only

$1

per invoice · probabilistic, 300+ pages of context

Quarri tool

$0

per invoice · deterministic, cached

Every $1 of compute Quarri saves
is $1 of lost revenue for the horizontal AI provider.

Model companies will never ship a cheaper tool — even from their own deployment teams.

Plus · no model lock-in Quarri is the deterministic layer. Skills and tools lift-and-shift to whichever LLM or harness wins next.

Example Workflows

Three things Quarri enables that AI can't do alone.

4 hrs → 2 min

Forestry organisation · Automated reporting

Weekly reconciliation replaced. Same workflow, encoded once.

$300k

Forestry organisation · Mis-accrual caught

Mis-accrual flagged in pilot week 1, missed across 8 months of invoices.

$60k

Marina operation · Pricing exception

Pricing exceptions surfaced across 1,000s of line items — margin unlocked.

This has saved me days of work and is actually accurate versus what we were doing.

Operations Lead

Forestry customer, North America

Why Now

Every AI tailwind.

MCP shipped

Feb 2026 · the mid-market stack became buildable. Quarri shipped 100+ tools in week 1.

App store inevitable

Distribution flows through LLMs. Build for them, not against them.

Vertical AI on foundational models

Inject verticalisation into general-model power. Don't lose horizontal AI — extend it.

Build for LLMs. Don't build against them.

The Moat

Moats compound outward.

Vertical data set

Opt-in benchmarking + optimisation. External data sets.

Skills corpus

Productised customisation; lifts and shifts to new customers and becomes industry-specific insight.

Context layer

Semantic model learns from previous deployments.

Data layer

Agentic modelling skillset deepens.

Connectors

Built once — less lift every new client.

The Flip

Quarri will run deployments solo.

Three forces drive services-to-software economics.

Margin trajectory

Services GM Blended GM

As services agentify, margin tail rises toward SaaS economics.

Growing data corpus

Every deployment is captured — call scripts, emails, workbooks, final automations.

Trains the deployment agent.

Horizontal AI gets more powerful

Foundational models improve. Our skills ride the tailwind.

Cheaper compute, smarter orchestration.

Skills become reusable products

First deployment of a skill is custom work. Every next deployment is lift-and-shift.

Skill creation cost falls. Margin recovers.

Three flywheels: data, model, product. All compound to margin.

The Team

Operator + architect.

Theo Leslie

CEO · Operator

Director of Strategy at Worldpay — $100m+ ARR product launches
VP Growth at fintech (Red Sky) — built lending product 0 → $500k ARR
Founding team for delivery division at major mid-market hospitality group
Chartered accountant (PwC)
Lived the Excel-heavy data pain for 10+ years

David Jayatillake

CTO · Data architect

3x founder (1 exit — Delphi Labs acquired by Cube)
Former VP of AI at Cube (semantic layer)
Founded 2 semantic layer startups
Leading voice in the semantic layer space (high-profile thought leader)
Data leadership at Lyst and Worldpay
20 years in data infrastructure and engineering

The operator who lived the pain + the architect who owns the technical stack. $13.5M raised between us — both founders have shipped venture-backed products at scale before. Expanding to 6 FTEs (including founders) in 2026.

Get in Touch

Theo Leslie

Co-Founder & CEO

theo@quarri.ai · quarri.ai

linkedin.com/in/theo-leslie-quarri Download as PDF