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FOUNDED 2026 · DELAWARE C-CORP
Enterprise AI Data Scientist

Hey OctOpus,
Connect my data warehouse.
Transform my sales data.
Show my top customers.
Build a dashboard.
Forecast next quarter's revenue.

Point OctOpus at your source — Databricks, Snowflake, BigQuery, Postgres, a file — and have a normal conversation. It connects, cleans, analyzes, builds live dashboards, and trains & deploys forecasting models — the work of a full data-science team, without the scarce, expensive hires. 10× the buyer base of Cursor: every analyst, consultant, and operator is a potential user.

FOUNDEDMar 2026
BASEDStockholm → SF
REVENUE$8.2K / month
CUSTOMERS13 paid · 9 enterprise PoCs
OctOpus mascot
CONNECTS CLEANS ANALYZES VISUALIZES FORECASTS DEPLOYS
Partners & Programs
NVIDIA Inception Google Cloud for Startups Microsoft for Startups AWS Startups Founders Inc AI Sweden AI Gothenburg Gothenburg AI Alliance Ignite Nordic Station F VivaTech Dakar Angels Network ADEPME
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THE PROBLEM
The problem

Reliable models, on demand, for decisions that cannot wait.

Getting from a raw warehouse to a decision still means exports, SQL, notebooks, packages, validation, retries, and deployment — work that demands data-science talent that's scarce, expensive, and already stretched thin. The whole loop is too technical and too manual.

01 Obsolete AutoML
Benchmarks models. Still needs an expert to frame the task, iterate, debug, and deploy.
No reasoning No context No self-debug No deployment
02 Pain / Complexity
Reliable models still mean notebooks, coding tools, packages, MLOps, and expert back-and-forth.
Notebooks Coding tools Packages MLOps Expertise Time
03 Reliability and speed
OctOpus is AI-native. It does the reasoning and turns one question into a reliable deployed model.
Data scientists
Build and deploy reliable models faster and more frequently.
Business users
“Forecast X per Y for Z.” Ask on real data, get visuals and dashboards, then build and deploy the model.
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THE PRODUCT
One conversation

From your raw warehouse to a clean answer, a live dashboard, and a deployed model.
You just talk — and get senior data-science work, on demand.

octopus AGENT THINK make a plan TRY test the idea CHECK did it work? FIX try better
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DEMO
The story behind the company

I went to bed. It went to work.

Open the demo how-to-use.html www.octoopus.dev
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TRACTION
Built nights and weekends · with a day job

Real customers. Real money. Real problems solved.

Customers don't just want a prediction. They want OctOpus to do the whole job — analyze the data, fill in their reports, hand it to their team ready to use. That's what we shipped.

Monthly revenue · USD
Mar
$1.2KApr
$8.26KMay
200
People using it
Analysts, researchers, consultants, big-company teams
13
Paying customers
$20 a month each · sign up themselves, no sales call
9
Enterprise PoCs
Logistics, lean management, deeptech, oil & gas, SaaS · US, France, Sweden, Africa
$9K
Paid PoC revenue
4 of the 9 already converted to paid · the rest in late-stage trial
INBOUND CONVERSATIONS · Q2 2026
IBM · Volvo Group · SKF · Ericsson · AstraZeneca · Actemium
Active inbound discovery calls with Fortune 500 enterprises. No outbound sales — they came to us.
A REAL EXAMPLE
A factory client makes bottles. OctOpus analyzed their quality data, found the defects, and filled in 10 sections of their existing client report — automatically. Their team just had to sign and send.
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MOAT
Why now · why us

Writing code is easy. Specialized knowledge and reasoning are the moat.

Claude, Codex, and Cursor can all write code. The hard part is knowing what to test, what to fix, what to trust, and what to ship for a specific forecasting problem. OctOpus compounds domain knowledge and reasoning on every run.

AutoML toolsDataRobot, H2O, leaderboard-style ML
Coding agentsClaude, Codex, Cursor, generic copilots
Hire a data scientistStrong, but slow and expensive to scale
OctOpusAI-native data science for specialists and operators
What they do best
Benchmark modelsGood for narrow structured ML flows
Write codeUseful, but still waiting for instructions
Own the workflowDeep judgment, but one person at a time
Reason, analyze, train, reportOne system across analysis and data science
Specialized reasoning
✗No real business context or adaptive judgment
~General coding help, not forecasting expertise
✓But expensive and not always available
✓Specialized reasoning for real model-building decisions
Fixes failures and retries
✗Stops at the benchmark
✗You still have to diagnose and steer
✓If the person has time
✓Auto-repair, retry, and continue in hours
Who can use it
ML teamsStill needs technical setup
Technical usersPrompting plus code review required
A data scientistUsually a bottleneck for everyone else
Data scientists and other professionalsSales, finance, growth, ops, and analysts can do data science in their jobs
Time to usable answer
Days to weeks
Depends on you
Weeks to months
Hours

WHY NOW · This wasn't possible 18 months ago. AI couldn't understand why it was wrong. Now it can. The category leader will be decided in the next two years — and OctOpus is shipping today.

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MARKET
How big is this

Every team that decides with data is a customer. That's the whole economy.

Data science used to be locked behind scarce specialists. OctOpus turns it into software anyone on a team can use — which opens a market far larger than the data-science tooling category itself.

TAM
$300B
Global spend on data science, ML platforms, analytics and the specialists who run them.
SAM
$70B
The categories OctOpus replaces in one product: BI & dashboards (~$38B) + data prep & transformation (~$10B) + predictive analytics & AutoML (~$25B).
SOM
$1.2B
Analysts, operators and SMB-to-enterprise teams we can realistically win in 3 years.
Bottom-up

90M+ knowledge workers touch spreadsheets and data every day. Winning just 1% at $20–2K / month is multi-billion ARR — before a single enterprise contract.

Tailwind

Predictive analytics is compounding ~20% a year, and every company is now budgeting for AI in 2026. The spend is moving toward outcomes — exactly what OctOpus sells.

SOURCES · Big data & analytics $300–450B by 2026: Fortune Business Insights · Straits Research  |  BI $32–41B: Fortune BI · Mordor  |  Data prep $8–12B: Mordor · Grand View  |  Predictive analytics $20–28B: MarketsandMarkets · Fortune  |  Growth 20–28% CAGR: Grand View · Figures directional; we grow seat-by-seat today and expand into outcome-based enterprise spend.

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BUSINESS MODEL
How we make money

From $20 a month
to enterprise contracts.

For individuals
$20/month
A student, consultant, or analyst who needs answers from their data — without hiring anyone.
  • Use it as much as you want
  • Upload any spreadsheet
  • Get a working model
  • Download a clean report
For teams
$500–2K/month
A small team that needs many predictions but can't afford to hire more data scientists.
  • Whole team has access
  • Connects to your systems
  • Custom reports
  • Email + chat support
For big companies
Custom
A large company that needs OctOpus inside their existing tools, with their security rules.
  • Runs in your environment
  • Dedicated support
  • White-glove setup
  • Pay per result

LONG-TERM · As OctOpus gets more reliable, we shift to charging per result delivered — not per seat. Customers pay for outcomes. The math gets better for both sides.

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FOUNDER
Solo Founder
Doudou BA
FOUNDER & CEO · 100% OWNER

I built the product, the early traction, and the first sales on my own. I move faster because I do not wait for cofounder decisions. Deep technical depth, real enterprise AI experience, and direct sales instinct all sit in one founder. I am open to hiring exceptional people later — especially in engineering or enterprise sales — but I do not need a cofounder to make progress.

EDUCATIONPhD Machine Learning · Charles University · MSc Software Engineering (Blekinge) · MSc Data Science (CZU)
RESEARCH4 peer-reviewed publications across forecasting, hydrology, and ML systems
MAERSKBuilt production AI and automation used in 130+ countries · owned systems where a single outage cost an estimated $50K within hours
SALESBuilt early OctOpus sales myself · prior background in recruiting and retail sales
TEAMCommission-based sales partners for distribution — variable cost, scalable reach
AWARDSWinner — Big Angels Day AI category · Winner — UNDP AI Hackathon · multiple Maersk Star Awards
ECOSYSTEMNVIDIA Inception · Founders Inc · AI Sweden Ignite · AWS for Startups · Google Cloud Credits · Station F Fighters
Doudou Ba

"Doudou, if passion got you funded, you'd be at Series C already." — Mark Sear · Director of AI & Engineering, Maersk

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THE ASK
OctOpus mascot
RAISING $500K PRE-SEED · DELAWARE C-CORP · YC ROADMAP

Raising $500K to build
the category leader in
AI-native data science.

USE OF FUNDS
~12–18 MONTHS RUNWAY
01 · FOUNDER
Quit the 9-to-5
This round takes the founder full-time on OctOpus. No split focus. Maximum speed.
02 · TEAM
Hire exceptional people
Bring in freakishly good sales talent and key engineering firepower to scale faster.
03 · SCALE
Win fast
Scale the platform and go aggressively after the US and European markets. The category is wide open — speed decides who wins it.
www.octoopus.dev →
OCTOPUS · DELAWARE C-CORP
FOUNDER · DOUDOU BA
STAGE · $8.2K MRR · LIVE
RAISING · $500K PRE-SEED