AI doesn't understand your business
Even with a well-built DWH, AI has no idea how to read your data: which metrics matter, how revenue is calculated, what a unit is in your model.
Data Warehouse + Knowledge Base
We change the way companies work with data
We build a Knowledge System that lets AI work like your CFO, COO and CRO: on your methodology, in your language of business.
DATAKNOWLEDGEAI
Who we are
Data engineers with financial expertise
MSM Smart Expertise builds enterprise data warehouses and Knowledge Bases end to end. We combine two disciplines that rarely meet: industrial-grade data engineering and hands-on CFO practice.
The firm's partners have held CFO and CDO roles at Alfa-Bank, BCS Group, Proxima Capital and PwC. The technology foundation is our own ATLAS platform.
300 TB
Data warehouse under management
BCS FINANCIAL GROUP, ATLAS ARCHITECTURE
−4 ×
Cost of data
AFTER MIGRATING TO ATLAS
1.2 M
Digital bank MAU
40 GB OF DATA PER DAY
Problem
Why data + AI isn't the answer yet
As AI spreads, companies are plugging it straight into the data warehouse. That's a step forward, but only the first one.
Generic answers instead of analysis
Without context about your specific business, AI gives one-size-fits-all answers. It isn't wrong. It simply doesn't know your specifics.
Wasted time and money
AI burns tokens rebuilding context on every request. That slows the work down and multiplies the cost of every answer.
Inconsistent results
Without a clear methodology in the knowledge base, the same question returns different answers. Trust in the system erodes.
What has to change
The Knowledge Base is the missing link between the data warehouse and AI that actually works.
| Aspect | Today | What's needed |
|---|---|---|
| Formula | Data + AI | Data + Knowledge + AI |
| Context | Enough data, no context | Methodology and context written down |
| Result | Generic answers | Accuracy and repeatability |
Solution
DATA WAREHOUSE
KNOWLEDGE BASE
What we build for the client
Data Warehouse + Knowledge Base = AI and BI running at full power.
A DWH on the ATLAS platform
Built fast, built for AI.
- ATLAS, our own platform: a proven toolkit for building a DWH fast
- The right architecture from day one: ODS → DDS → Data Mart
- Integration with every source: on-chain, off-chain, ERP, CRM
- Deployment several times faster than the market
Expertise and methodology for AI
Expertise you can't buy off the shelf.
- A description of the data structure: what every field and table means
- Calculation methodology: revenue, margin, unit economics, KPIs, risk
- Business context and rules: seasonality, exceptions, special cases
- Regular updates: the Knowledge Base lives alongside the business
ATLAS platform
An architecture ready for AI to consume
Six layers, one context. Every layer is described in the Knowledge Base, so AI doesn't guess, it knows.
SourcesON-CHAIN · OFF-CHAIN · ERP · CRM · BILLING · APIS
We connect every one of the client's data domains, no exceptions. Each source is documented in the Knowledge Base the moment it's connected.
ODSOPERATIONAL DATA STORE
Raw data at source granularity, versioned. No transformations, just reliable landing and quality control.
DDSDETAIL DATA STORE
The normalised core of the model: entities, relationships, change history. This is where a single reading of business objects lives.
Data MartsPRE-COMPUTED VIEWS
Pre-computed marts built for the specific questions a CFO, COO and CRO ask. This is what BI and the AI layer query.
Knowledge BaseSEMANTIC LAYER
The semantic layer: field meanings, calculation methodology, business rules and exceptions. Everything the CFO knows and AI doesn't.
AI + BICONSUMPTION
Conversational analytics and scheduled reporting on the very same context: the answers match, wherever you ask them from.
Services
01
02
03
04
05
06
Six services, one system
We take responsibility for both halves: the data and the knowledge. We can do them separately, but the value comes from the pair.
Enterprise data warehouse
Design and production rollout of a DWH on ATLAS: ODS → DDS → marts, quality control, load procedures.
Enterprise knowledge base
Calculation methodology written down, a reference of fields and tables, business rules and exceptions, all machine-readable.
AI layer for executives
Conversational analytics for CFOs, COOs and CROs on top of the Knowledge Base. Answers in the language of the business, not the language of tables.
BI and management reporting
P&L, unit economics, KPI tree, treasury reports. One methodology for the reports and for AI.
CFO-level financial expertise
IFRS, transfer pricing, tax, M&A and structuring, delivered by partners with Big Four and banking backgrounds.
Data audit and turnaround
A diagnostic of the existing stack, a total-cost-of-ownership assessment and a migration plan that doesn't stop operational reporting.
Methodology
How the work runs
Every stage ends with an artefact the client keeps: a data map, a model, a procedure, a knowledge base. Nothing stays "in people's heads".
30 MINUTES
Strategy call
We work out which decisions you make on data today and where the context gets lost. No slides, no commitment.
2 WEEKS
Discovery
An inventory of sources, metrics and methodologies. You get a data map, a gap map and an architecture plan.
6–8 WEEKS
ATLAS pilot
We deploy ODS → DDS → mart on real data and assemble the first version of the Knowledge Base for your priority domain.
3–6 MONTHS
Production rollout
Full source coverage, data-quality procedures, the AI layer for executives, and handover of ownership to the client's team.
ONGOING
Growing the Knowledge Base
The Knowledge Base lives alongside the business: new products, new metrics, new rules get written down, not passed on by word of mouth.
AI + Knowledge Base
QUALITY
SPEED
COST
What the client gets
Data warehouse (ATLAS DWH) + Knowledge Base → AI and BI move to a new level across three dimensions.
Precise answers instead of generic ones
AI knows your specifics, your methodology, your language of business. Reports answer real questions, not industry averages.
ACCURACY AND RELEVANCENo time lost rebuilding context
AI doesn't spend time guessing at the data structure. The Knowledge Base hands over the context instantly.
AN ORDER OF MAGNITUDE FASTERFewer tokens, lower spend
Every AI request costs money. The Knowledge Base removes redundant iterations and clarifying round-trips.
SAVINGS ON THE AI BUDGETBuilt properly, a Data Warehouse + Knowledge Base becomes a strategic asset of the company.
QUALITY · SPEED · COST
Why us
A rare combination of two disciplines
CFO practice, not slide-deck consulting
Our partners have closed the books, defended budgets and owned the P&L themselves, in banks and private equity.
Our own ATLAS platform
The technology and the methodology are ready in advance, which is why deployment runs several times faster than the market.
One team for the data and for the meaning
No gap between engineers and finance people: the methodology is written by the people who use it.
Results in stages, not one big launch
The first working system in 6–8 weeks. Every stage leaves the client a finished artefact.
Team
Partners who have been the client
Three partners with more than 80 years of combined experience in finance, banking and data engineering.
DS
Dmitry Serezhin
Partner · Dubai, UAE- 30 years in banking and finance
- CFO · Alfa-Bank
- CFO · BCS Group
- CFO of crypto and investment companies
- Treasury, KPIs, transfer pricing, P&L
GB
Grigory Baev, FCCA
Partner · Dubai, UAE- 25+ years in finance and banking
- PwC, Big Four
- CFO · Proxima Capital (private equity)
- Turnaround CFO, IFRS, tax, M&A
- Structuring across jurisdictions
AA
Artur Averyanikhin
Partner · Dubai, UAE- CDO at BCS Financial Group · 300 TB DWH, costs down 4×
- Digital bank · 1.2M MAU, 40 GB/day
- ATLAS architect, Python, SQL, ML/AI
- Technical university (CS) + finance school
Ways of working
DISCOVERY
PILOT
PRODUCTION ROLLOUT
From a call to a production system
Three engagement formats. You can start with any of them, but almost everyone starts with Discovery.
STEP 130-minute callWe understand the context and decide whether it makes sense to go further.
STEP 2DiscoveryData map, gap map, architecture plan.
STEP 3PilotA working system on real data in 6–8 weeks.
STEP 4Production rolloutFull coverage, procedures, handover of ownership.
2 weeks
Fixed scope
An audit of the current stack and methodologies, plus a total-cost-of-ownership assessment.
- Source inventory
- Context gap map
- Target architecture plan
- Timeline and budget estimate
6–8 weeks
One business domain
A working system on real data with the first version of the Knowledge Base.
- ATLAS DWH: ODS → DDS → mart
- Knowledge Base v1
- AI layer for one stakeholder
- Data-quality procedures
3–6 months
Full coverage
All sources, all reporting domains, ownership handed over to the client's team.
- Full source integration
- Knowledge Base across all domains
- AI for CFO, COO and CRO
- Training and handover of ownership
Case studies
FINANCIAL GROUP
DIGITAL BANK
PRIVATE EQUITY
Partner projects
Public case studies are being prepared for release. Below are projects our partners ran as CDO and CFO.
A 300 TB warehouse and a 4× cost reduction
300 TB · −4 ×1.2M MAU and 40 GB of data a day
1.2M MAU · 40 GB/DAYTurnaround of a portfolio company's finance function
IFRS · M&APLACEHOLDERS · CLIENT CASE STUDIES WILL BE ADDED ONCE CLIENTS APPROVE THEM
Next step
Let's start with a 30-minute call
We'll work out where the context gets lost between your data and your decisions, and what a Knowledge Base would give you in your model.
30 MINUTES · NO COMMITMENT · THE CALL IS LED BY A PARTNER
Contact
Let's start with a 30-minute call
msm.smart.expertise@gmail.com
+971 54 353 6631
Dubai, UAE · DMCC Business Center
The call is led by a partner, not a sales rep. You don't need to prepare anything, just a sense of which decisions you make on data.