Finance
Margin, costs, profitability, delinquency.
Find out what's changing in your operation before it shows up in the results.
DataTeam builds an analytics layer on top of the data your company already has: it watches your metrics, flags relevant deviations, digs into the data, and shows the evidence of what changed — not just an alert.
How the deviation gets caught today
Nobody notices
Deviation beginsShows up in the result
BI shows the result. Someone still has to understand why.
Your company probably already has an ERP, a CRM, a database, and a BI tool. Even so, when something starts drifting off pattern, someone has to:
- 01Notice the deviation
- 02Investigate
- 03Cross-reference data
- 04Understand the cause
- 05Decide what to do
That's the work the analytics layer cuts down.
Where AI comes in
Calculations stay deterministic — built in code and SQL. AI comes in afterward, to interpret, contextualize, and explain what the numbers mean. It doesn't decide what counts as a deviation; that's a business rule.
Code / SQL
- Calculations
- Metrics
- Comparisons
- Rules
- Thresholds
- Validations
AI (LLM)
- Interpretation
- Context
- Explanation
- Written diagnosis
- Suggested actions
AI interprets the results. It doesn't replace calculations that can be done deterministically.
How it works
- 01
Observe
Metrics, history, and behavior.
- 02
Detect
Deviations, changes, and anomalies.
- 03
Diagnose
Understand what changed and where.
- 04
Evidence
Show the data that backs the conclusion.
- 05
Recommend
Suggest possible actions.
- 06
Track
Log the decision and its status.
A real diagnosis, not a generic alert.
Indicator
Margin
Current
21.4%
90-day average
24.1%
Deviation
-2.7 pp
Diagnosis
The decline is concentrated in three categories and two regions.
Evidence
Those categories account for 74% of the identified impact.
Likely cause
Lower average price combined with higher discounts.
Suggested action
Review the commercial policy for the affected categories and reassess the discounts granted.
The decision stays with the business owner.
Illustrative example.
Not just an automated analysis.
A diagnosis needs to answer:
- ✓What happened?
- ✓How big was it?
- ✓Where did it happen?
- ✓What contributed to it?
- ✓What evidence supports it?
- ✓What's the likely cause?
- ✓What could be done about it?
When there isn't enough evidence, that gets stated too.
You stay in your BI. The analytics layer works underneath it.
Results can be delivered into:
- Power BI
- Tableau
- Looker
- other tools
No need to build a new BI tool for the end user.
How the data is handled
- Calculate metrics
- Apply rules
- Compare periods
- Detect deviations
- Produce diagnostics
- Generate recommendations
- Write results to structured tables
The architecture and LLM usage are defined based on the client's environment and policies.
Where to start
Sales
Customers losing momentum, conversion, concentration, ticket size.
Operations
SLA, productivity, delays, capacity.
Supply Chain
Inventory, turnover, stockouts, suppliers.
Or any area where discovering things late has real consequences. We start with one specific operation.
What's delivered
- ✓ Analytics layer
- ✓ Diagnostics
- ✓ Evidence
- ✓ Likely cause
- ✓ Recommended actions
- ✓ Alerts
- ✓ Result tables
- ✓ 1 management dashboard, when needed
Environment setup and data ingestion are carried out according to each client's needs.
Investment
Investment is defined after a conversation about your operation, your data volume, and the complexity of your environment. Talk to us for a tailored proposal.
Talk about my operation