Data and analytics
Get reliable answers from the data you already have
We connect only the data a specific business question needs and build analytics, alerts or AI search that help you not just see information, but act on it.
Trusted by
CRM
ERP
In the accounting system
In Excel files
Google Sheets
In email
In documents
In internal systems
In cloud storage
The problem appears when, every single time, somebody has to:
Find it
Export it
Combine it
Check it
Reconcile different versions
Interpret it
And only then make a decision
In that situation the data exists, but it isn’t yet working as a decision-making system.
You don’t need to tidy up all your company data first. We start from one question and only the sources it needs.
Getting started
We start from one business question
We don’t suggest starting by rebuilding the company’s entire data infrastructure. First we pick one specific question that matters to the business right now.
For example:
How many open orders do we have, and where are they getting stuck?
What does the product actually cost us to make?
Which customers are late paying?
Which process generates the most errors?
What is the real state of the sales pipeline?
Which stock levels put production at risk?
Which procedure covers this particular situation?
What does the contract say in this case?
Then we establish:
What answer is needed
What data that requires
Where it sits today
How to join it up reliably
How to present the answer
What should happen next
That lets us create value from a small, clearly bounded scope.
1. Business analytics
When the question is “what is going on?” We connect structured business data and build a dependable information layer you can decide on. That might be:
A KPI system
An automated report
A view of process status
Operational analytics
Discrepancy checks
Alerts
Data-quality signals
Automatic actions triggered by a specific signal
The goal isn’t another dashboard. It is that the right person gets reliable information in time and knows when to act.
2. Company knowledge
When the question is “where is the answer?” We connect approved documents, procedures, project material and other internal sources, and build a search or AI knowledge layer. Your people can ask in plain language and get:
A current answer
A link to the specific source
The context
Only the information they are allowed to see
The goal is that nobody has to know which folder or document the answer is hiding in.
Outcome
What you are actually buying
Not a “BI project”. Not a data warehouse. Not a “RAG system”. Not another screen full of charts. What you get is a dependable information layer for a specific business decision. It can deliver:
A single KPI
A report
A process status
A signal
A Q&A answer
A document analysis
An API result for another system
The technology follows the question.
Principle
Data → Insight → Decision → Action
Our aim isn’t to stop at displaying data. We design the process like this:
Data
We collect only the data needed, from your systems and sources.
Insight
We turn it into a metric, an answer, a discrepancy or a signal.
Decision
We define exactly which decision this information is meant to support.
Action
Where possible we connect the result to a real process:
A notification
A task
An escalation
An approval
An automatic action
The next step in a workflow
Analytics should be part of the process, not a screen someone has to remember to open.
Starting point
You don’t need to tidy up all your data first
This is one of the most common worries before starting a data project. Companies usually know the CRM isn’t perfect, the Excel files disagree with each other, different systems use different fields, some historical data is incomplete and document structure varies. None of that means you have to fix the whole organisation first. We start from:
One question
The objects it needs
The fields it needs
The sources it needs
We clean up only the part of the data needed to create real value. Only then do we decide whether extending the data layer is worthwhile.
A single KPI layer
We join up the sources needed and define exactly how each metric is calculated, so different departments stop having their own separate version of the truth.
Automated reports
Reports are generated automatically:
On a schedule
After a specific event
When an important metric changes
Nobody has to export data every week and stitch Excel files together by hand.
Process-status analytics
We show not just the end result but where the process currently stands. For example:
How many orders are waiting
How many are late
Which stage work is piling up at
What the current load is
Where exceptions are arising
Data-quality checks
The system can automatically check for:
Missing values
Discrepancies
Duplicate records
Implausible values
Data that has stopped refreshing
Integration failures
Action signals
Instead of another dashboard you can set a rule: “when X happens, notify Y and trigger Z.” For example:
When stock drops below a threshold
When a customer exceeds their credit limit
When an order sits too long at one stage
When a metric deviates from the norm
When data doesn’t match between systems
An AI knowledge base
We connect approved internal sources and let the team search in plain language. The system can draw on:
Google Drive
SharePoint
Procedures
Internal rules
Product information
Contract documents
Technical documentation
Project material
Training material
The answer is grounded in a current source, not just the model’s “memory”.
A document analysis system
AI can:
Extract information
Classify documents
Compare versions
Check terms
Find discrepancies
Pass the result to another system or process
01
A clearly defined business question
We agree which question the system has to help answer. Not “we want more analytics”, but a specific result.
02
A map of data sources
We establish:
Where the required data lives
Who owns it
How often it refreshes
What quality it is in
What the main discrepancies are
What access restrictions apply
03
A shared data logic
We align:
The core objects
Fields
Relationships
KPI definitions
Calculation logic
So different sources get interpreted the same way.
04
An automated data flow
We build the collection, transformation and synchronisation needed. Data no longer has to be prepared by hand every time.
05
A usage layer
Depending on what is needed, that could be:
Dashboard
An automated report
A signal
Search
AI Q&A
Document analysis
An API for another system
We build what helps a decision get made, not what looks most impressive.
06
Monitoring
We check:
Whether the data is refreshing
Whether the integrations are working
Whether discrepancies have appeared
Whether the answer is grounded in a current source
Whether the KPI calculation still holds
07
Documentation
Your team needs to understand:
Where the data comes from
How the metrics are calculated
Who owns the data
How often the information refreshes
Where to check for errors
What to do when a discrepancy appears
How we work
How a project runs
We pick one business question
Step 01
We start from a real decision that is hard to make today. For example: “where are orders getting stuck right now?”, “what does this product actually cost us?” or “where in our documents is the answer to this situation?”
We establish what data is needed
Step 02
We answer:
Which fields are needed
Which systems are needed
Which documents are needed
How often the information has to refresh
What margin of accuracy is acceptable
We assess data quality
Step 03
We review only the data actually required. We identify:
What is missing
What doesn’t match
What is duplicated
What has to be fixed
What can wait until later
We build the data flow
Step 04
We automate:
Collection
Transformation
Validation
Joining
Refreshing
The goal is for the answer to rest on continuously refreshed information.
We build the answer layer
Step 05
That might be:
KPI
A report
Dashboard
A signal
AI search
Document analysis
We connect it to a real action
Step 06
Where it makes sense, the system doesn’t just show information — it triggers the next step. For example: a metric crosses a threshold → the responsible person gets a signal → a task is created → a process starts
We measure the result
Step 07
We assess:
How long it takes to get an answer
How many manual exports have been eliminated
How much data correction has dropped
How often the information refreshes
How much time people spend looking for information
How many signals turned into real actions
Q&A accuracy and source coverage
AI knowledge base
An AI knowledge base — when the answer is already inside your company
Often the problem isn’t that the company lacks knowledge. It is that nobody can find it fast enough.
Someone may have access to:
Hundreds of documents
Several Drive folders
SharePoint
Procedures
Contracts
Product information
Project material
And still end up asking a colleague: “where was that document again?”
An AI knowledge layer lets them ask in ordinary language
“What is our returns procedure for a customer in Germany?”
The system:
Finds the relevant documents
Locates the information needed
Returns an answer
Cites the specific source
The goal isn’t a “clever chatbot”. It is finding a reliable answer quickly, from your own information.
First project
One question for the first project
We don’t recommend starting with “let’s connect all the company’s data”.
For the first project we pick:
A single KPI
One report
One process status
One signal
One knowledge-search use case
That lets us:
See a result sooner
Keep the project scope small
Reduce technical risk
Test data quality
See how it is really used
Once the first case works, we decide what is worth connecting next.
Involvement
How much your team will need to be involved
From your side
We usually need:
Someone who understands the specific business question
Access to the relevant systems or documents
An explanation of the KPIs or business rules
Someone who can confirm whether the result is correct
Feedback during testing
From the Milios side
We handle:
Framing the business question
Analysing the data sources
Data modelling
Integrations
Transformations
Validation
The analytics layer
AI search, where it is justified
Testing
Monitoring
Documentation
Your team provides the business context. We build the technical path from data to answer.
Security
Data security and access
Trustworthy analytics isn’t only about correct numbers. Who can see them matters too.
As required, we design:
User roles
Access permissions
Data separation
Filtering of sensitive information
Audit logs
Logic for how long information stays valid
Source citations in AI answers
EU-region infrastructure where required
In AI knowledge systems, access to an answer must match access to its source.
Example
Production data and accounting
In a production process, information was scattered across orders, Excel files, stock, production data, cost calculations and the accounting system.
The problem wasn’t “we need a dashboard”. First these had to be answered:
What counts as an actual order
Which raw materials it requires
How stock is reserved
Where production output is recorded
How cost price is calculated
Which data has to reach Rivilė
Only once that data logic was sorted could process status be shown reliably and the information used for decisions.
The principle: a dependable data flow first, analytics second.
Fit
When this service is a good fit
This service suits you if:
Reports are still put together by hand
People are merging exports from several systems in Excel
Different departments have different numbers for the same metric
Managers see the result too late
The information exists but is hard to find
People spend a lot of time hunting for documents
You want to connect AI to your internal knowledge
You have plenty of data, but it isn’t yet helping you act faster
When you probably don’t need this service
The service may be too broad if:
You only need one simple report from an existing system
All the data you need is already in one system and configuring it is enough
The real problem is process automation, not a lack of information
You need a custom business application in which analytics is only a small part
In that case we can point you towards a different direction.
We start from the question, not the technology
We don’t start from:
“we need Power BI”
“we need RAG”
“we need a data warehouse”
First we need to understand which decision has to be made.
We only sort out as much data as the result requires
We don’t ask you to fix the whole organisation first. We start from a small scope that creates value.
We connect analytics to the process
If a metric calls for action, the system can flag it or trigger the next step in a workflow.
We use AI where it is justified
Where information is structured, we use classic data logic. Where documents have to be understood or unstructured information searched, we can add an AI layer.
We can build the whole path from source to action
From CRM / ERP / document / Excel through to a reliable answer / KPI / signal / process action
Do we have to sort out all our data first?
No. It is usually enough to sort out only the data needed for one specific business question. That means starting sooner, without committing to a large data project with no clear value.
Do you build dashboards?
Yes, when a dashboard is the most suitable way to use the information. But a dashboard isn’t the goal. Sometimes an automated report, an alert, a periodic summary, a Q&A system or an automatic process action is the better answer.
Can you work with our existing systems?
Usually yes. The aim generally isn’t to replace your CRM, ERP or accounting system. We connect the information needed and build the missing data, analytics or usage layer.
What is RAG?
RAG is a technical method that lets an AI look up current information in your approved sources before answering. In business terms it means something simple: someone asks → the system finds the information → it returns an answer with its source.
Can AI answers be 100% accurate?
No. That is why, depending on the use case, we apply source citations, test suites, access control, constrained answers and human review for critical decisions.
How long does a project take?
It depends on the question chosen, the sources and the quality of the data. We deliberately narrow the project to the first use case that creates value. Before starting we agree which question we are solving, which sources we connect, what result we deliver, how often it refreshes, how we judge its reliability, the project stages and the delivery dates.
Can we start with a single report or a single KPI?
Yes. That is often the best first project. Once one case works reliably, we decide what is worth extending.
Which business question takes too much manual work today?
Let’s take one specific question. On the first call we will work out what answer you need, where the data currently sits, how it is prepared, where discrepancies arise, whether you need analytics, AI search or something simpler, and the smallest scope worth starting from. If we see a clear use case, we will propose a concrete next step. If the problem can be solved more simply, we will say so plainly.