Reports bottlenecked on a few people
Every new question becomes a request to the one analyst or developer who understands the database.
AI & Automation · Data Analysis
AI data analysis services that show their working
We help teams ask questions of their sales, stock and finance data in plain language, forecast what's coming and spot unusual activity, while every answer exposes the query behind it so it can be checked.
What is AI Data Analysis?
AI data analysis services apply language models and statistical or machine learning methods to business data so that people can query it in plain language, forecast future values and detect unusual patterns. A language model translates a question into a database query or analysis, and the result is computed from your actual data rather than written by the model. Because translation errors are possible, answers should display the underlying query, and significant findings should be reviewed by someone who knows the data.
Managers wait days for a report because only one person knows how to write the query, and dashboards answer last quarter's questions rather than today's. Natural-language querying can shorten that loop, but a language model that misunderstands "active customers" or joins the wrong tables will produce a confident, wrong number. The value comes from combining accessible questions with a well-defined data model, visible logic and people who check results before decisions are made.
Why business questions still take days to answer
Ambiguous business terms
Words like revenue, margin or active customer mean different things to different teams, and a model can't guess the right definition.
Data spread across systems
ERP, e-commerce, spreadsheets and marketing tools each hold part of the picture, often with mismatched identifiers.
Plausible but incorrect answers
A wrong query still returns a tidy-looking number, which is more dangerous than an obvious error.
Our AI Data Analysis service
Data model and semantic layer
Clean, documented tables and agreed definitions of key metrics, so both people and models interpret the data the same way.
Natural-language querying
An interface where users ask questions in plain language, the model generates a query against approved tables, and the result is shown with the query and the definitions used.
Forecasting models
Statistical and machine learning forecasts for demand, sales or cash flow, with uncertainty ranges and comparison against simple baselines.
Anomaly detection
Automated checks that flag unusual transactions, sudden changes in sales or stock, or data quality problems for someone to investigate.
Narrative summaries
Plain-language summaries of computed results, such as weekly performance notes, generated from the figures rather than invented by the model.
Data pipelines
Scheduled extraction from Odoo, e-commerce platforms and other sources into a reporting database, with checks for missing or duplicated data.
Included capabilities
- Plain-language questions over approved data
- Generated SQL shown with every answer
- Shared metric definitions
- Read-only access to reporting data
- Demand and sales forecasting
- Forecast uncertainty ranges
- Anomaly alerts with context
- Summaries generated from computed figures
- Question and answer logs for review
What AI Data Analysis changes for your business
Shorter wait for answers
Routine questions no longer queue behind a single analyst.
Answers you can verify
Visible queries and shared definitions let anyone check how a number was produced.
Earlier warnings
Anomaly checks surface unusual activity while there is still time to act.
Analysts focus on harder work
Specialists spend less time on repeat requests and more on interpretation.
Where it makes the biggest difference
Self-service sales questions
Sales managers ask for revenue by region, product or salesperson in plain language and see the query and definitions behind the answer.
Demand planning
Forecasts by product and location give purchasing teams a starting point, which planners adjust using market knowledge.
Finance anomaly checks
Unusual payments, duplicate bills or sudden expense changes are flagged for finance to review before month-end.
Weekly performance summaries
Automatically generated notes describing what changed in key metrics, with links to the figures they were drawn from.
Six steps from question list to trusted answers
Analyst review is a defined step, not an optional extra, because the model translating a question can misread business terms.
Collect questions
List the questions managers ask most often and the decisions those answers feed.
Audit data
Map where the data lives, its quality, and the gaps that would make answers unreliable.
Model the data
Build a reporting database and agree written definitions for the metrics that matter.
Build and test
Implement querying, forecasting or anomaly detection and test against questions with known correct answers.
Analyst review
Have someone who knows the business check outputs and refine definitions where the model misinterprets terms.
Roll out and monitor
Release to a small group, review logged questions and failures, and extend coverage over time.
What it connects to
AI Data Analysis
- Odoo ERP data
- PostgreSQL and MySQL databases
- E-commerce platforms
- Spreadsheets and CSV exports
- Marketing and analytics tools
- Data warehouses
- Email and messaging alerts
Built with
Security, quality and scale
Read-only access
The querying system uses read-only credentials limited to approved reporting tables.
Row and column restrictions
Users only query the data their role permits, such as their own branch or region.
Minimal data to the model
Schemas and questions are sent to the model, while bulk data stays in your database wherever possible.
Query logging
Every question, generated query and result is logged for audit and quality review.
What working with us looks like
Data foundations first
We fix definitions and data quality before adding AI, because a model can't compensate for unclear data.
Odoo data expertise
We know Odoo's data model well, which helps when your ERP is the main source of business data.
Transparent outputs
We design every answer to show its query or method, so trust is earned rather than assumed.
Practical forecasting
We compare models against simple baselines and only recommend complexity when it measurably helps.
What it costs
Fixed-price packages with the scope written down, paid stage by stage. Your project goes live once it's fully paid.
Related services, technologies and industries
Sources and further reading
Official documentation and guidance if you want to read more about this topic.
AI Data Analysis: questions we hear most
Can we really ask our database questions in plain English?
Yes, for well-defined questions over a clean, documented data model. The model writes a query, the database computes the answer, and the query is shown so it can be checked. Ambiguous or very complex questions are more likely to be misinterpreted and still benefit from an analyst.
How do we know the AI's answers are correct?
Each answer shows the generated query and the metric definitions used, and we test the system against questions with known answers before rollout. For decisions with real consequences, a person who knows the data should confirm the result.
Is our business data sent to an AI provider?
Usually only the database structure, the user's question and small result sets are sent, not your full data. Where even that is not acceptable, an open-weight model can be self-hosted on your infrastructure.
How reliable are AI forecasts?
Forecast reliability depends on the history available, how stable the patterns are and external factors the data can't capture. We show uncertainty ranges, compare against simple baselines and treat forecasts as inputs to planners' judgment rather than final answers.
Do we need a data warehouse first?
Not necessarily. Smaller businesses can often start with a reporting database fed from Odoo or other systems. What matters more is consistent definitions and reasonable data quality.
Next step
Tell us the questions you wait on
List the reports and questions your team repeatedly asks for and where the data lives. We'll assess whether AI can answer them reliably and what groundwork is needed first.