Quarri
The AI adoption platform for mid-market.
The Problem
SaaS platforms are rigid - so mid-market uses slow, error-filled spreadsheets to bridge the gap.
90%
Of mid-market firms on spreadsheets
94%
Of spreadsheets contain errors
65%
Of analyst time on data gathering
Dig deeper
It's not a bug - it's a feature. Rigid SaaS systems force manual workarounds in Excel, manual workarounds are time consuming and introduce errors. Solving the root of the problem requires a risky full stack migration or building a data team at a cost most mid-market companies in low-margin industries can't justify.
SaaS is Rigid and Switching Costs Are High
In mid-market, ERP implementations cost $150–500k with an average of 189% cost overruns (Panorama 2025). Still 30–40% of total IT spend ends up on shadow workarounds (Gartner).
So Businesses Default to Excel
70–90% of firms still rely on spreadsheets across finance and operations (AutoRek 2025, n=500).
But Excel is Slow, Fragile, and Error-Prone
94% of business spreadsheets contain critical errors (Frontiers of Computer Science, 2024, peer-reviewed). Teams spend 65% of their time on data gathering and maintenance, not insight (FP&A Trends 2024, n=379).
The Answer Exists - But It's Prohibitively Expensive
The solution is well-understood: consolidate, clean, and warehouse your data. But that requires $400k+/year before any output and requires constant maintenance. Mid-market simply can't afford it.
Data Team Cost Breakdown
Chart
The Solution
Quarri connects your legacy ERPs and messy spreadsheets and puts AI to work on it. No data team required.
<1 day
To ROI
78
Tools · used autonomously
99%+
Data accuracy
Dig deeper
Mid-market businesses don't need more verticalized SaaS. They just need to be able to harness cutting edge agents.
Agentic infrastructure and white glove results
Quarri uses an in-house AI agent to clean, connect, and contextualise fragmented data, then delivers insights and automation through Anthropic's Claude. No data engineers. No months-long implementations. No platform risk. Instant value.
Data Agent
Automated SaaS - Central contextualised company platform for AI enablement.
78 Tools - Used Autonomously by Claude
Chart
Each tool is an MCP capability that Claude can invoke autonomously. They span extraction, transformation, modelling, analysis, visualisation, metrics, rules, general skills, and admin - covering the full stack traditionally requiring 5+ specialised roles.
Workflow Automation
Professional services - Accelerated automation and reporting deployment for instant ROI.
Deployable Skills
Detail
Teams are trained to use platforms - and we can support with implementations.
  • Natural language interface - non-technical users query data directly via Claude
  • Benchmarking - benchmark pricing, performance, market share etc. against public data sets
  • Data creation and capture - writes back into database based on workflows
  • Systems reconciliation - reconciles systems of records, replacing excel workflows
  • AI-generated dashboards - live, saveable, shareable
  • Deep analysis agent - commercial metrics (Churn, LTV, CAC, MRR, etc.)
  • Document ingestion - ingestion of handwritten documents into structured data
  • Financial reporting - data embedded directly into Excel and PowerPoint
Quarri data agent - messy data to AI ready
Quarri takes in structured data from legacy ERPs, spreadsheets, databases, and external data sets. No data engineers. No months-long implementations. No platform risk. Instant value.
Almost the entire pipeline is agent-managed. The warehouse has read and write abilities - becoming not only storage but also required for data capture and workflow outputs.
What It Looks Like
Automate recognisable workflows using the power of AI.
Natural language analytics
Financial reporting into Excel & PowerPoint
Systems reconciliation & data capture
External data benchmarking
Forecasting
I showed some of the Financial Op's team too, super impressed. Love the comparisons to CPI, benchmarking to golf clubs in the region for green fees, and the financial controls items on voids/discounts.
CFO
Private Golf & Country Club, North America
Why Now & Market
AI is transforming enterprise. Mid-market is being left behind.
$63.2B
TAM
70%
Struggling to adopt AI
60%
Of AI projects fail - data quality
Dig deeper
This leaves low-margin, operationally heavy PE portfolio companies with the most to gain from AI - but the least capacity to build it themselves. AI adoption isn't a nice-to-have - it's a real-terms EBITDA opportunity.
Market Opportunity
$63.2B
TAM - US + UK + EU + Canada
SaaS Mix > 80.0% · ~86% Gross Margin
$21.2B
SAM - Target Industries
SaaS Mix > 72.1% · ~81% Gross Margin
$3.9B
SOM - Addressable Now
SaaS Mix > 67.6% · ~79% Gross Margin
Bottom-up sizing: US Census Bureau, ONS, Eurostat, Statistics Canada firm counts by size band × Quarri ACV per tier. Filtered to target legacy verticals only. Gross margin: SaaS 97.5%, Services 40%.
Why Now
Mid-Market is Falling Behind
Larger companies are 2x more likely to scale AI. Companies under $500m revenue are stuck in experimentation and pilots.
Mid-Market AI Adoption Gap
Chart
Source: McKinsey Global Survey, State of AI 2025 (n=1,993)
C-Suite Demand Signals
60%
Of AI projects fail to move from pilot to production due to poor data quality - Gartner 2025
87%
Say AI Will Be Critical to Operations - Deloitte '25
54%
Prioritising AI Agents in Business - Deloitte '25
AI adoption has been more sluggish in business areas who rely on structured data as inputs and outputs
AI Agent Adoption by Function
Chart
LLM cost collapse makes AI affordable with strong net positive ROI
LLM Input Cost Per Million Tokens
Chart
Competitive Landscape
Two markets exist. Neither serves mid-market. Data tools require technical teams. Automation tools are built to stitch together modern, API-native SaaS - not the legacy systems mid-market actually runs on.
Data Stack - Fragmented by Role, Not Outcome
Detail
Every tool in the modern data stack requires a technical operator. Quarri delivers business outcomes directly.
Layer
Tool
Technical Team Member Required
ELT / Ingestion
Airbyte · Stitch · Whalesync
Data Engineer
Warehousing
Snowflake · BigQuery · DuckDB
Data Engineer
Modelling
dbt · Cube · Lightdash
Analytics Engineer
Visualisation
Tableau · Looker · Omni
Data Analyst
Analytics
Hex · Python · Qlik
Data Scientist
Quarri replaces the entire stack - no technical team required.
Automation Stack - Built Around Modern SaaS, Not Legacy Systems
Detail
Workflow automation tools are built to stitch together modern, API-native SaaS - not legacy ERPs, spreadsheets, and disconnected databases. Mid-market companies run on legacy. Quarri starts from messy reality.
Quarri
Seed / Series A
Series B-C / Acquired
Series D+
Listed
Bubble size ~ last known valuation
Sources (last known round & valuation): Rows AI - Growth round ~$8.7M, May 2024 (acquired by Superhuman Feb 2026); Datarails - Series C, ~$70M, Jan 2026; $175M total; Dataiku - Series F, $200M, Dec 2022; $3.7B val; IPO expected H1 2026; Zapier - Secondary sale, ~$5B val, 2021; only $1.4M primary VC; Make - Acquired by Celonis Oct 2020 for >$100M; n8n - Series C, $180M, Oct 2025; $2.5B val; Workato - Series E, $200M, Nov 2021; $5.7B val; Tray.io - Series C, ~$50M, Sep 2022; ~$600M val; Pipedream - Series A, ~$20M, 2021; Activepieces - Seed, ~$5M, 2024; HubSpot - Listed, NYSE: HUBS; Monday.com - Listed, NASDAQ: MNDY; Airtable - Series F, $735M, Dec 2021; ~$11B val; Notion - Series C, $275M, Oct 2021; ~$10B val; Smartsheet - Listed, NYSE: SMAR; Salesforce (Agentforce) - Listed, NYSE: CRM; UiPath - Listed, NYSE: PATH; Palantir - Listed, NYSE: PLTR; Celonis - Series D, ~$1.4B total (2021-22); ~$13B val; ServiceNow - Listed, NYSE: NOW; C3 AI - Listed, NYSE: AI.
Traction · 8 Months In
$42k
ARR Signed
$21k
Average ACV
6
Planned Pilots
4
Pipeline Partnerships
Low-margin, operationally heavy mid-market PE portfolio companies are our beachhead.
Financial controls, data accuracy, and speed directly impact portfolio P&L.
Dig deeper
Target Profile
50–500
Employees
$10–100m
Revenue
CEO / CFO
Buyer / Champion
Legacy
SaaS Systems
Target Verticals
Manufacturing & Forestry
Win Now
Hospitality & Resorts
Win Now
People Services
TBD - Future
Subscription Tiers
Premium
$500
/month
  • 3 live sources
  • 3 admins
  • Guided setup
    • Business
    $1,200
    /month
  • 5 live sources
  • 5 admins
  • Training included
    • Professional
    $2,000
    /month
  • 7 live sources
  • 10 admins
  • 1 dev day / month
    • Corporate
    $3,000
    /month
  • 10 live sources
  • Unlimited admins
  • 2 dev days / month
    • Account Expansion Model
    Chart
    Typical onboarding starts at Business tier ($1,200/mo). Within 6 months, customers add sources, users, and professional services - driving natural expansion. Blended margin steps down as services mix increases, but absolute margin grows.
    SaaS / Professional Services Margin Contribution
    Detail
    Entry (Business)Mid-size (Professional)Large (Corporate)
    Total ACV$16.8k$36.0k$80.4k
    SaaS / Services100% / 0%72.7% / 27.3%50.7% / 49.3%
    SaaS Margin97.5%97.5%97.5%
    Services Margin-40%40%
    Blended Margin97.5%82%69%
    SaaS margin 97.5% - LLM costs passed through to user. Services: contractor cost $600/day, charged at $1,000/day = 40% margin. Source: Quarri Business Plan v2.0.
    GTM: Private Equity Distribution
    Primary distribution targets low-margin, operationally heavy mid-market PE portfolio companies. These businesses run on legacy systems, rely on spreadsheets for critical operations, and have the most to gain from AI-driven data infrastructure - but the least capacity to build it themselves. Anthropic is in talks with Blackstone and other PE firms to form an AI deployment JV - deploying AI tools across portfolio companies. Quarri is positioned to execute this in mid-market PE where portfolio companies have exactly the data problems we solve.
    The Quarri integration has made me do more with AI in the past 6 weeks than I have in the past 6 months.
    Operations Lead
    Outdoor Recreation & Wildlife Enterprise, North America
    The Team
    Built by an operator and a data expert.
    Theo Leslie
    Theo Leslie
    CEO
    VP Growth at seed Fintech (0→$500k ARR). Dir. Strategy at Worldpay ($100m+ ARR launches) and a PE-backed mid-market hospitality group.
    LinkedIn
    David Jayatillake
    David Jayatillake
    CTO
    3x founder (1 exit). Former VP of AI at Cube. Founded 2 semantic layer startups.
    LinkedIn
    $13.5m+ previously raised in debt + equity across both founders.
    Dig deeper
    Theo Leslie - CEO
    Over a decade running processes in excel
    • VP Growth at seed Fintech/SaaS: 0 → $500k ARR
    • Dir. Strategy at PE-backed Worldpay: $100m+ ARR product launches
    • Strategy at PE-backed mid-market hospitality group and PwC
    • Over a decade frustrated by excel
    David Jayatillake - CTO
    Serial founder · data infrastructure expert
    • 3x founder (1 exit)
    • Former VP of AI at Cube
    • Founder of 2 semantic layer start-ups (one exit)
    • Leading influencer on the semantic layer
    • Data leadership roles at Lyst and Worldpay
    I can imagine tearing the current structure down and re-building it towards its actual objectives in a way that best leverages the data sources and the possible tools.
    Operations Lead
    Outdoor Recreation & Wildlife Enterprise, North America
    Vision
    Imagine a Spock in every company - on every call.
    Today
    Data foundation - connects, cleans, contextualises for humans.
    Next
    Proactive intelligence - alerts, root cause, proposes solutions.
    Future
    Agent on the call. Replaces professional services entirely.
    Dig deeper
    At scale: full end-to-end automation - including professional services delivery - unlocking both SaaS and services revenue autonomously.
    The Moat
    • A more powerful data agent - targeted skill set built in data and data transformation
    • Learned tools & skills - capabilities built custom for one client can benefit all clients
    • Tight workflow tailoring to mid-market - tools, context and solution layer built around legacy mid-market challenges
    Embedded Stickiness
    • Growing memory - every interaction makes the system smarter about your business
    • Infrastructure lock-in - embedded in data infrastructure, hard to rip out
    • Operational embedding - data capture, reconciliation, and operational workflows become business-critical. The deeper Quarri gets into daily operations, the harder it is to replace
    The Ask
    $2.4M
    Seed. 18 months runway.
    Dig deeper
    Raise Timeline
    First Meetings
    April – May
    Lead Term Sheet
    May – June
    Close
    Q2 2026
    Financial Projections
    CY2026CY2027CY2028
    Total Revenue$125k$3.1M$12.8M
    Exit ARR$482k$5.9M$22.0M
    Gross Margin77%80%79%
    Total Contracts9173550
    Headcount61627
    Rev / Employee$21k$193k$473k
    Source: Quarri Business Plan v2.0. SaaS/Services split: CY2026 64%/36%, CY2027 69%/31%, CY2028 68%/32%.
    Product (18 months)
    • Optimise for Claude as front end
    • Deepen analytical functionality & context memory
    • Develop agent orchestration
    Hires
    Engineering: 1 midlevel developer at ~5 months, +1 engineer by end of year. GTM: 1 BD hire at ~5 months. Marketing budget deployed from month 1.
    Ownership
    David Jayatillake 50% · Theo Leslie 50%.
    ARR Milestones
    Chart
    Use of Funds Breakdown
    Chart
    Get in Touch
    Theo Leslie
    Theo Leslie
    Co-Founder & CEO
    theo@quarri.ai · quarri.ai
    LinkedIn