AI-powered retail decision engine

OMNIA

AI-powered system for retail to optimize profitability
📅 2026 👥 Adrien Naeem & Laetitia Lamari 🎯 Enterprise AI SaaS 💰 Seed — €2M
The Founders

Who we are

A team of key opinion leaders and e-commerce (B2B/B2C) & retail tech experts.

Adrien Naeem
Adrien Naeem
Co-Founder
Repeat Founder
20+ years working in SaaS tech (Adobe, Salesforce, Narvar).
VP Europe for US SaaS ecom scale-ups for +10 years.
10 years collaborating with Laetitia.
LinkedIn influencer & public speaker.
Laetitia Lamari
Laetitia Lamari
Co-Founder
Repeat Founder
20 yrs in global marketing (agency, software).
Expert in social, payment solutions & content.
10 years collaborating with Adrien.
Diversity & inclusion champion.
LinkedIn influencer & public speaker on national TV.
Julien Nouet
Julien Nouet
Head of Omnichannel
Lacoste / Kering / Louboutin.
Senior omnichannel expert.
Nadia Lyulintseva
Nadia Lyulintseva
Head of Growth
6 years working with Adrien at Narvar (Solutions Engineer, Strategy & Sales EMEA).
Imperial College London — MSc Business Analytics.
Deep expertise in post-purchase CX & retail SaaS.

Our Heritage: Butterfly Agency

Scaled Butterfly Agency from €0 to ~€1M in 2 years
Adrien and Laetitia co-founded Butterfly Agency, where they spent 7 years working with top retail brands. Through decades of accumulated experience in operational complexity, margin optimization, and retail transformation, we identified the core insight: there is no intelligent decision infrastructure for retail profitability. OMNIA is the spin-off born from these insights — a focused, vertical AI operating layer for retail.
Proven ability to pivot fast — adapted business model 3x in 2 years while maintaining growth.
AwardRanked #1 Ecommerce Influencers in France
The Problem

Retailers are facing more and more complexity

📈

Increasing competition

The market is becoming increasingly competitive — China, low customer loyalty, downward price pressure. Retail complexity has exploded: omnichannel, global supply, margin pressure, rising logistics and tax costs.

📊

Fragmented data

Retail data is fragmented across ERP, OMS, BI, pricing tools, etc. Teams rely on dashboards but lack actionable intelligence.

⚡

Too slow to act

Decisions remain manual and reactive. Retailers lose 5–15% margin due to inefficient decision making — poor inventory, pricing, and supply decisions.

Their challenges are underserved by enterprise software. We need to achieve more with less people, less money, and limited resources.
Market Opportunity

Addressable market

$32T
Global retail market
3–5%
Average retail net margin
60–70%
Of costs tied to operations
(inventory, pricing, supply chain)
~30%
Of inventory decisions still manual or spreadsheet-driven
OMNIA is built for mid-size retailers that are too complex for spreadsheets but underserved by enterprise software.

< €50M

Simple operations. Limited data. Not priority.

€50M – €1B

Fragmented data. Complex decisions.
→ OMNIA sweet spot

€1B+

Existing enterprise tools. Internal data teams. Longer sales cycles.

The Solution

OMNIA — AI Retail Decision Engine

A 360° view of retail operations with contextualized, proactive recommendations.

NOT Analytics — A Recommendation Engine

OMNIA is NOT a dashboard. NOT a BI tool.
It's a recommendation engine that tells you what to do next and estimates the margin impact.

Data sources OMNIA ingests

ERP Systems
Order Management (OMS)
Warehouse Management (WMS)
Point of Sale (POS)
E-commerce Platforms
CRM / CDP
Footfall & Traffic Counters
Staffing / HR Data
Loss Prevention / Shrinkage
Logistics & Carrier Data
Third-party Market Benchmarks

Data Lake / Warehouse

Google BigQuery
Snowflake
Databricks

Semantic Layer

dbt
LookML (Looker)
AtScale
MicroStrategy
What we don't do: CRO. Web personalization. OMNIA focuses on operational profitability & retail decision intelligence.
🔒

Zero-Knowledge Data Processing

No human access to raw client data. All processing is encrypted, anonymized, and fully auditable. Your data never leaves your infrastructure with our self-hosted option.

End-to-end encryption SOC 2 ready On-premise option GDPR compliant
OMNIA App Screenshot
14
SKUs analyzed
67
Units at risk
6
Locations
Competitive Advantage

The Data Moat

Three layers of proprietary data that no LLM or generic AI tool can access or replicate.

🔐

First-party Client Data

Direct integration with ERP, OMS, POS, e-commerce systems. Real operational data from customer businesses — the proprietary foundation.

📊

Third-party Data & Benchmarks

Price per sqm, market benchmarks, open data, purchased databases. Combined with first-party data to provide contextualized recommendations.

📈

Cross-client Anonymized Benchmarks

Aggregated insights across all OMNIA customers. Network effect: the more clients, the better the benchmarks, creating exponential competitive advantage.

Why OMNIA

Why Claude / ChatGPT can't replace OMNIA

Addressing the #1 investor concern: what's to stop a generic LLM from doing this?

OMNIA's Unfair Advantages
🔐 Proprietary Data
First-party, third-party, and cross-client benchmarks that LLMs have no access to. Data moat compounds daily.
🎯 Vertical Specialization
20+ years of domain expertise baked into the model. Retail decision logic, not generic AI chat.
📊 Network Effect
More clients = better benchmarks = better recommendations. Self-reinforcing competitive moat.
⚙️ Integrated Layer
Connected to actual operational systems (Shopify, SAP, OMS). Not just chat — it orchestrates real action.

Grounded & Ranked Insights

Every recommendation is ranked by estimated margin impact (€K) and grounded with a transparent justification — no black box.

#1
+€340K

Rebalance overstock: Paris → Lyon

Transfer 1,200 units of slow-moving SKUs from Paris flagship to Lyon where sell-through is 3x higher.

Why this ranking: Lyon has 89% sell-through vs. 31% in Paris for this category. Transfer cost is minimal (same logistics hub).
#2
+€210K

Adjust price index: Accessories

Accessory line is priced 12% above market average with declining conversion. A 5% adjustment recovers volume.

Why this ranking: Price elasticity analysis shows 5% reduction yields 18% volume increase in this category.
#3
+€95K

Enable Ship-from-Store: Bordeaux

Bordeaux store has excess inventory and sits within 2-hour delivery radius of 240K online customers.

Why this ranking: Fulfillment from Bordeaux saves €4.20/order vs. central warehouse. Estimated 22K eligible orders/year.
Core Engine

11 Performance Drivers

Each driver answers a strategic question, ranked and scored. Organized across 3 pillars of retail profitability.

Demand — 4 drivers
OTB Efficiency
Active
Are we investing inventory budget where it generates revenue?
Assortment Relevance
Active
Does each product earn its shelf space in every location?
Stock-out Impact
Active
Where are we losing sales because shelves are empty?
% Inventory Exposure
Soon
What share of our inventory is actually visible and sellable?
Commerciality — 4 drivers
Price Index
Active
Are we priced right vs. competition in every market?
Retail Network Efficiency
Active
Is every location contributing positively to the P&L?
E-commerce Services
Soon
Are our digital services (C&C, SFS) driving conversion?
Geo-fulfillment Coverage
Locked
Can we reach every customer within optimal delivery windows?
Network Efficiency — 3 drivers
Transportation Spending
Active
Are logistics costs optimized across our distribution network?
Tax Scheme Efficiency
Locked
Are we maximizing fiscal optimization across jurisdictions?
E-retail Relevance
Soon
How effectively do we compete on marketplace visibility?
Strategic Planning

Strategy Builder

Build multi-layer strategies with initiatives, track impact over time, and align operational decisions with business goals.

Quarterly
Quick wins & tactical actions
Full Year
Annual business plan alignment
3 Years
Long-term transformation goals
🎯

Initiative-Based

Each strategy contains concrete initiatives with owners, deadlines, and estimated margin impact.

📊

Impact Synthesis

Real-time aggregated view of expected revenue impact, cost savings, and margin improvement across all initiatives.

🔗

Driver-Linked

Strategies tie directly to Performance Drivers — every initiative connects to measurable KPIs.

User Architecture

CFO as primary user

OMNIA's user hierarchy: decision-maker at the top, operational teams implementing.

🤖

My Agent — Proactive AI Assistant

OMNIA's AI agent continuously monitors all Performance Drivers and pushes prioritized alerts to each user based on their role. Alerts are classified by severity so teams focus on what matters most.

Critical Attention Opportunity

CFO / Finance Director

Primary user. Logs in daily.
Views margin optimization dashboard → Approves & dispatches recommendations
Retail Director
Executes store initiatives
Head of Logistics
Manages supply chain recommendations
Supply Chain Manager
Implements procurement changes
Category Manager
Owns assortment decisions

Proactive Alert Channels

OMNIA pushes actionable insights directly to your team's existing tools — no need to log in.

📧
Email Digest
💬
Slack
👥
Microsoft Teams
🔗
Webhook / API
Go-to-Market

Sales motion & network

Leveraging founder expertise and relationships to accelerate enterprise adoption.

🎓

20 Years SaaS Sales

Adrien's experience scaling enterprise SaaS across Europe. Direct B2B sales expertise.

📻

Café de l'E-commerce

Premier e-commerce & retail media in France. Direct channel to top decision-makers.

🤝

Decision-Maker Network

Personal relationships with CFOs, supply chain leaders, and brand CMOs across luxury & retail.

📈

Land & Expand

Personal relationship-driven entry. Expand from initial CFO sponsor to full organization adoption.

Opening is the New Closing

AI spam killed cold outreach. The only way to access decision-makers now is through real human networks.
The juxtaposition: Sales used to be automated → now human again. Margin optimization used to be human → now automated. OMNIA combines both.
1,500+
Decision-makers in WhatsApp community
Tens of thousands
On LinkedIn network
Prestige Collaborations
Store tours with Hermès, consulting with L'Oréal, B2B events at Salesforce
Timing

Why now

🚀

The market is accelerating

Retail complexity is only growing — the need for intelligent decision infrastructure has never been greater.

🤖

AI adoption is mature

AI technologies are now ready for industrialization — AI agents can cross systems, reason contextually, and propose prioritized actions.

🏗️

Ready for infrastructure

Retail has analytics. It lacks autonomous profitability and efficiency orchestration.

Retail is now ready for AI decision infrastructure.
Strategic Focus

OMNIA roadmap: Product → People → Customers

OMNIA starts with deep product intelligence, then expands to people, then customers.

Product
Intelligence
Starting here
→
People
Collaboration
Next phase
→
Customers
Ecosystem
Many tools exist here
Vision

3-Year Vision

Vertical focus first, then expansion.

2026

Launch in Retail (France)

First clients, product-market fit. 5 enterprise clients, €1M ARR.

2027

Scale & Expand

12+ clients, €4M ARR. Expand across Western Europe. Series A.

2028

Market Leadership

25+ enterprise clients, €15M ARR. First moves into Travel, Hospitality, B2B Distribution.

Expansion

Industry expansion roadmap

Starting with Retail as the anchor market, expanding into adjacent high-margin sectors.

Retail
(Current)
Travel &
Hospitality
B2B
Distribution
CPG &
Manufacturing
Fashion &
Luxury

Relationship Building: Already building relationships in B2B distribution through Salesforce B2B events (Brands to Buyers). The expansion thinking has started.

Positioning

Competitive Landscape

Every platform is adding AI — but only in their silo. Nobody connects it all. OMNIA is the transversal layer.

AI & Reporting
Analytics

Palantir
Snowflake
Tableau
Power BI

Order
Management

Manhattan Associates
Fluent Commerce
Kibo

Warehouse
Management

Blue Yonder
Körber
Manhattan

Supply Chain
Solutions

Kinaxis
o9 Solutions
Coupa

E-commerce
Platforms

Shopify
Salesforce Commerce
SAP Commerce
OMNIA — Transversal Layer
AI Retail Decision Engine that connects across ALL systems
Note: Zipline does next-best-action, but only from their silo data. OMNIA spans the entire retail operation.
Traction

Early market traction

Strong interest from top-tier luxury, retail & lifestyle brands. Contract values ranging from €80K to €160K.

~€1.3M
Qualified pipeline
10+
Enterprise prospects
Coralie Sitbon · Adrien Martin
COO · Head of Digital Operations
~€110K
Giorgia Carastro
VP Commercial
~€145K
Julien Godefroy
Global Planning Director
~€130K
Stéphanie Pigasse
Controlling Director
~€95K
Arnaud Pignerol
Head FP&A
~€120K
Thomas Voisinditlacroix
CDO
~€155K
Imen El Karoui · J. Vallet
Data & AI Director · Korea Supply-Chain Director
~€140K
F. Chancholle
Head of E-commerce
~€105K
Marie Laure Cassé
Marketing & Digital Director
~€125K
Marie Laure Cassé
Ex — Marketing & Digital Director
~€115K
Revenue

Commercial Traction Trajectory

€0 €500K €1M €1.5M Q3 2026 Q4 2026 Q1 2027 Q2 2027 2027–2028 €200K €500K €600K €1M Growing
Revenue (ARR)
Quarterly milestones
Business Plan

Business model & projections

Business Model

Enterprise AI SaaS for retail decision intelligence

Annual license€80k–€160k / retailer
UpsellData modules + analytics + enterprise deployment
TargetRetailers & brands €50M–€1B revenue

Pricing Options

Base License
€80K–€160K annual SaaS fee
Performance-Based
Base + % of margin improvement realized

Projections

Y1 — 5 clients€1M ARR
Y2 — 12 clients€4M ARR
Y3 — 25 clients€15M ARR

Key Metrics

ACV~€120k
Gross margin~80%
NRR120%+

Client ROI

Average ROI: 5–10x license cost

A €200M retailer recovering just 0.5% margin = €1M/year in additional profit from a €120K license.

Investment

Funding Ask

Raising
€2M
Seed
Runway
18–24
months

Use of funds

35%
Product
25%
Sales & Partnerships
20%
Key Hires — CTO + AI/Data
15%
Data Infrastructure
5%
Operations

Path to Series A: Reach €5M ARR within 24 months to trigger next round of funding.

Goals

Advisory

The Advisory Board

GB
Guillaume Beaumont
CTO Hermès

Ecommerce CTO at CapGemini for 10 years
AG
Anthony Gavin
VP WW for SaaS tech for 20 years
Marie Cappelaere
Marie Cappelaere
Chief Data & AI Officer
Boulanger
N
Nijad
Google

Senior AI Engineer

Sources & Backup

McKinsey & Company
Data & Analytics / AI adoption research
Deloitte
Global CIO Survey / Tech Trends
Microsoft
Work Trend Index / Security reports
IBM
Data governance & risk reports
Gartner
Data & Analytics Governance research