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ATLAS Data Platform
ClickHouse · dbt · Airflow · Superset
Enterprise Data & Analytics · Growth-Stage Companies

Let your data
outsmart the crowd.

The way you run your business is about to change. Now.

An enterprise data platform and board-grade finance.
The Trap ATLAS

You raised the round.
Now your investors want numbers.

They expect mature, audit-ready reporting. But every euro is going into the product — and the traditional way to get there costs you time, money, and the risk of a bad hire.

Post-raise — or simply past ~$300k/mo, where spreadsheets and Grafana-on-prod stop scaling.

THE TRADITIONAL WAY €400k+ a CDO and a CFO · per year 6+ months to a first working prototype hire first prototype + the risk of a bad hire
Atlas — Confidential — 2026
The Answer ATLAS

Start with everything. Pay for what matters.

Atlas Core From Day One
The enterprise data platform

You've outgrown the spreadsheet — this is the real analytical warehouse you need. Free for single-node installations, straight from the public repo. The foundation everything else stands on.

Atlas Intelligence Partners Ongoing
Turn your data into a working asset.

Not just numbers — a methodology for your case. Built and run with you as full-engagement partners, end to end.

Access on every surface — from desktop to mobile.
Atlas — Confidential — 2026
Atlas Core ATLAS

One platform.
Three steps to make your data your business partner.

Collect, describe, analyze — three clean layers, each with one job. From a raw dump of your sources to the marts that run your business.

Why a real warehouse? Point BI at production and it takes your back-end down — back-ends are built for users, not analytical load.

Analyze Data Marts Describe Detailed Data Store Collect Operational Data Store
Atlas — Confidential — 2026
Atlas Core · Layer 1 ATLAS
Collect
Operational Data Store

A full, raw dump of your sources — on autopilot.

01 No hands — fully automated, from capture to typing.
02 Deduplication and type-casting handled for you.
03 Continuous drift-watch on your source schemas.
Deterministic, not AI — same data in, same result out.
Analyze Data Marts Describe Detailed Data Store Collect Operational Data Store AUTO hands-off
Atlas — Confidential — 2026
Atlas Core · Layer 2 ATLAS
Describe
Detailed Data Store

Your business, in clean domains.

01 Break your business into clean domains — documented automatically.
02 Trust, without the runaway cost — quality that holds as volume grows.
03 Explore in plain language — an AI on your semantic model, right in Slack.
you Which cohort drove last month's margin?
atlas Here's the breakdown — and the chart. replied in #analytics
Analyze Data Marts Describe Detailed Data Store Collect Operational Data Store
Atlas — Confidential — 2026
Atlas Intelligence Partners · Layer 3 ATLAS × MSM SMART
Analyze
Data Marts

Where methodology becomes numbers.

01 Live financial model — P&L and cash, always current.
02 Product analytics — what drives usage, retention, revenue.
03 Risk & behavioral modeling — trouble before it hits the P&L.
04 Anomaly detection & alerting — the number moves, you know.
05 Trust, without the runaway cost — quality that scales with volume.
06 Anything you can design — define the metric, we implement it.

No metrics for control's sake — just monitoring, and decisions. No more weekly Excel and PowerPoint: every number, any moment, any surface.

Analyze Data Marts Describe Detailed Data Store Collect Operational Data Store
Atlas — Confidential — 2026
Atlas Core · Real-Time ATLAS

Real-time, in real time.

UNION real-time track — ready, hands-off DMA DDS ODS BATCH MODE + DMA DDS ODS MINUTE MODE

Everything you just saw — the models, the analytics, the alerts — the platform can run live. Point at the mart you want; it builds the real-time track itself, batch and minute unioned. No second pipeline, no second team.

Atlas — Confidential — 2026
Atlas Core · Security ATLAS
One port
Attack surface

Your data never leaves your perimeter.

01 A single inbound port — 443/tcp. Everything else stays closed.
02 Every component talks internally. Nothing else is exposed.
03 Put it behind whatever you already trust — your VPN, your WAF, your edge.
True on-premise — tier-1 posture, entirely inside your contour.
YOUR PERIMETER ATLAS ClickHouse Airflow Superset dbt · storage internal network — nothing published 443 the only way in optional: CDN · WAF · VPN in front
Atlas — Confidential — 2026
How We Work ATLAS × MSM SMART

Bottom-up or top-down? Both!

The platform builds bottom-up — ODS live on day one — while your partners design the methodology top-down.

METHODOLOGY · TOP-DOWN DMA marts + final reporting DDS clean business entities ODS raw · automated foundation PLATFORM · BOTTOM-UP ~3 mo ~1.5 mo day one 3–4 months to turnkey

Turnkey in 3–4 months — for a business doing $300–500k a month.

Atlas — Confidential — 2026
The Team ATLAS × MSM SMART

The operators who've done this before.

30+ yrs
Combined CFO leadership
Tier-1
Banking · Sber · Alfa · BCS
Big-4
PwC-trained finance
300 TB
Data platforms at scale
Dmitry Sereznin
Co-Founder · Senior Partner
CFO — Alfa-Bank Ukraine (10 years)
CFO — BCS Group Dubai
CEO — MSM Smart Expertise DMCC
Treasury, transfer pricing, KPI systems, P&L architecture
Grigory Baev, FCCA
Co-Founder · Senior Partner
PwC (Big-4) — career start
Head of FP&A & IFRS — Alfa-Bank Group
CFO — Proxima Capital · turnaround CFO
IFRS, tax, M&A, multi-jurisdiction structuring
Artur Averyanikhin
BI & Data Lead · ATLAS Architect
CDO — FG BCS · 300TB DWH · 4× cost reduction
Exec Director — Sberbank · 1.2M MAU · 40GB/day
Bauman MSTU (CS) + NES (Finance)
Python, ClickHouse, Kubernetes, ML/AI

The team that ran finance and data at Tier-1 banks — now building yours, and running it with you.

Atlas — Confidential — 2026
Roadmap ATLAS

Today: Core. Next: Cloud. Then: Web.

Shipping Today
ATLAS Core
On-Prem

The full platform inside your perimeter. Sovereignty, GDPR / PCI-DSS, reactive ODS generation and custom engineering.

On the Roadmap
ATLAS Cloud
Cloud-Native

The same engine, fully cloud-native — more self-service and a faster start for teams that don't need on-prem early on.

On the Roadmap
ATLAS Web
Traffic Analytics · working name

Audience and web analytics pulled straight from frontend traffic — no tagging, no frontend rework. Timely, as platforms start blocking custom event tracking.

Atlas — Confidential — 2026
ATLAS Data Platform
Let's Begin

Let your data
outsmart the crowd.

The way you run your business is about to change. Now.

The Platform
atlasplatform.cloud
ATLAS — vendor
CFO & Implementation Partner
msm-smart-expertise.management
MSM Smart Expertise
Scan
Open the platform
& request a demo
Atlas Core · Layer 1 — where the cost goes
The layer nobody budgets for

Collect does three visible jobs: pulls the data, casts the types, decides how each table is materialised. The invisible work underneath is what eats the budget.

What actually lives in this layer
Ingest+ Type casting+ Materialisation+ Platform config & tuning+ Auth — OAuth · Keycloak · mTLS+ Dozens of components kept in step
The stack looks simple from outside — ClickHouse, Airflow, Superset. It is a few dozen moving parts that have to agree.
Your data team — and who this layer takes
DE
Data
Engineer
Up to 30% of data headcount sits here. This is the technical core of the warehouse — and the role behind it is Data Engineering.
In-house vs Atlas
Typical in-house
×Hire and keep Data Engineers — the largest single line of the data budget
×Platform configuration, authorization and upgrades are your problem
×Every new source is a ticket, a sprint and a deploy
With Atlas
✓No Data Engineers to hire. Not fewer — none
✓The platform configures, connects and runs itself
✓Kept by one system administrator under our guidance, or by our team on contract
DE
Data
Engineer
This role disappears from your org chart.
Atlas Core · Layer 2 — where the cost goes
The hire you cannot make

Describe is where your business becomes a model. The roles that do it well are the hardest to find — and the ones companies skip.

Who this layer actually needs
AD
Data
Architect
AE
Analytical
Engineer
The two hardest technical hires in data. Scarce, expensive, and the ones who decide whether your numbers will agree with each other in a year.
What usually happens instead
Business wants analytics→ Hires DA first→ No architecture underneath→ Numbers disagree→ Rebuild — this time with an architect
DA
Data
Analyst
Analysts are hired first because the need is visible. The architecture is invisible — until the year is lost.
Others vs Atlas
Building it yourself
×You must find an Architect before anything can be built correctly
×The standard is invented on the way — and rewritten after the first rebuild
×Analysts wait, or build on sand
With Atlas
✓The architectural standard ships with the platform — layers, dependencies, naming, aggregation classes
✓Your team starts building the right model on day one
✓After integration we train your people to the standard, or help you hire the Architect who keeps it
AD
Data
Architect
AE
Analytical
Engineer
Architect-level decisions are already made. You still want an Analytical Engineer — and now one is enough.
Atlas Core · Layer 3 — where the cost goes
From a question to a dashboard

Marts are built to the same strict standard as everything below them. That is what makes the next part both possible and accurate.

What an analyst task costs
Without a standard
With Atlas
DA
Data
Analyst
Days and hours → hours and minutes. The analyst is not rediscovering the data on every task.
A second way in — chat over the same marts
Plain sentence in Slack→ MCP into Superset · Grafana→ Context built from DDS semantics→ Dashboard, exact numbers
Other vendors vs Atlas
Chat bolted onto raw tables
×The model is handed raw tables and guesses what the fields mean
×A multi-storey prompt, then retries until the number looks plausible
×Your rows are shipped to an external model to give it context
×Token bill grows with every retry
×Numbers are approximately right — which is the dangerous kind of wrong
Chat over a described model
✓Context is built from the DDS semantics — the model already knows what every field means and how it may be aggregated
✓One request. A sentence of the kind your business already writes in Slack
✓Nothing leaves your perimeter — the context is the semantics, not your data
✓A fraction of the tokens, because there is no retry loop
✓Tested in production: dashboards in minutes, with exact numbers
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