Aelix Echo · Data Analytics
Know why the numbers moved,
and show your working.
Your data sits in five systems and a few spreadsheets. We bring it together, check it, and build the analysis and reporting your team can rerun, question, and trust.
Agentic AI · Analytics agents
The prep work is done before an analyst opens the file.
Most of an analyst's week goes on finding, cleaning and reconciling data, not on analysing it. Aelix Agents take that legwork: they prepare the data, answer routine questions with their sources shown, and draft the recurring reports. Your analysts spend their time on judgment.
Data Preparation Agent.
Profiles each new source, flags missing values, duplicates and unit mismatches, and drafts the cleaning rules. An analyst approves the rules before they touch the working data.
Question Answering Agent.
Turns a plain-language question into a query against approved datasets, runs it read-only, and answers with the query, the source tables, and the date range shown beside the figure.
Reporting Agent.
Assembles the recurring report from approved queries, writes the commentary on what changed, and holds it as a draft. Nothing is published until a named analyst signs off.
Data Quality Watch Agent.
Watches the pipelines feeding your reports, notices when a source stops updating or a column changes shape, and tells the owner before the report goes out wrong.
The trust architecture.
Read-only by default.
Agents query the data, they do not edit it. Any write to a source system is a separate, gated action that a person approves.
Every figure cites its query.
Each number in an answer or report links to the exact query, source and date range behind it. A figure without a citation is dropped before display.
Approved datasets only.
Agents work from the datasets your data owner has approved, with role-based access applied to every question, and a log of who asked what.
A person publishes.
Drafts, cleaning rules and reports wait for a named analyst. The agent does the legwork, and your team makes the call.
Human in the loop
No agent publishes a report, changes a source system, or shares data outside your perimeter until a named person on your team approves it. Every run is written to an audit trail.
The problem
Most reporting problems are data problems.
The same number, three answers.
Sales, finance and operations each count a customer, a shipment or a claim differently. We agree one definition per measure and write it down.
The report nobody can rebuild.
The monthly deck lives in one person's spreadsheet. We turn it into a repeatable analysis that runs the same way next month, whoever is on leave.
The question that waits a week.
A simple question queues behind the data team. We set up governed, self-serve answers so routine questions stop needing an analyst.
What we do
From scattered data to an answer you can check.
Bring the sources together
ERP, CRM, spreadsheets, databases and exports, connected into one governed place without moving what should stay where it is.
Clean and reconcile
Duplicates, gaps and mismatched definitions found and resolved, with every rule recorded so the result can be audited.
Analyse and explain
Descriptive and diagnostic analysis that answers what happened and why, with the drivers broken out and the working shown.
Report and self-serve
Dashboards and recurring reports your team can rerun, plus a governed way to ask the routine follow-up questions.
Question
Why did returns rise in the second half of the quarter?
Most of the increase traces to one product line and one supplier batch. The other lines are flat.
Illustrative example. Simulated interface, not client data.
Where it applies
The same method, in the language of your industry.
FinTech
Portfolio and delinquency analysis, transaction monitoring reviews, regulatory reporting that reconciles to the ledger.
Healthcare
Claims and denial patterns, capacity and throughput, quality measures built from several clinical systems.
Manufacturing
Downtime, scrap and yield analysis across lines and shifts, built from machine and quality data.
Logistics
On-time performance, cost per shipment, and exception root causes across carriers and lanes.
LegalTech
Matter profitability, billing realisation and workload across practice groups.
Energy and Utilities
Outage and reliability analysis, asset performance, and consumption patterns by region.
Real Estate and Construction
Occupancy, maintenance cost and project variance across a property or site portfolio.
Related work
Built on the same discipline: check the data against a standard.
Two engagements where the core of the work was checking data against a standard before anyone relied on it: invoices against supplier records, and model behaviour against agreed criteria.
Simulated interface. Not client data.
See all case studiesData Analytics
What happened, and why.
Consolidated data, diagnosed drivers, reports you can rerun.
Predictive Analytics
What happens next.
Forecasts and risk scores, each with its explanation.
See Predictive AnalyticsHow we work
One real question first. Then the rest.
Pick one decision
We start with a question your team already asks and cannot answer quickly, and agree what a good answer looks like.
Map and connect
We find where the data lives, who owns it, and which definitions disagree, then connect the sources we need.
Build and check
We clean the data, build the analysis, and reconcile it to numbers your finance or operations team already trusts.
Hand over
Your team gets the reports, the definitions and the documentation, and can run and extend it without us.
Bring us the question your data should already answer.
45-minute working session · One real question, scoped live