Static answers
Dashboards answer yesterday’s questions.
They are useful for known measures, but novel questions still create queues, exports, and one-off analysis.
Product 01 · Data Analysis Agent
An agent for the question your dashboard can’t answer.
Connect approved company data, ask questions conversationally, follow the investigation, and turn the answer into a chart, table, report, or download with evidence the team can review.
Working product Configured around the operating context, systems, and controls of each engagement.
Why did net revenue fall in the West last month?
Answer · sources attached
Returns and lower order volume explain 82% of the variance.−12.4% Net revenue4 Sources9 Evidence rows
ordersreturnsregion_targetsView logic ↗
The queue behind the dashboard
Your next question should not wait for another analytics ticket.
Scattered context
The answer lives across systems.
Commercial, financial, operational, and customer data rarely arrive with the same structure or terminology.
Generic AI
A fluent answer can still be wrong.
Without approved joins, calculations, definitions, and source context, a model can produce confidence without business truth.
Working product foundation
The core analysis loop already works.
The Data Analysis Agent connects multiple structured and relevant company sources, joins information automatically or through controlled mappings, and follows an investigation through useful follow-up questions.
It can turn the answer into a chart, table, report, or download while keeping source context and analytical choices visible for review.
What exists today
Working product. Fitted implementation.
Working product
Conversational analysis, multiple sources, cross-source joins, charts, tables, reports, downloads, evidence references, permissions, guidance, and connection options.
Client fit
Approved sources, terminology, definitions, calculations, questions, user access, validation cases, outputs, and decision workflows.
Additional software
Proprietary connectors, data pipelines, new interfaces, recurring analysis, workflow actions, application integrations, and supporting applications.
Applied to your industry
Start with a problem the team already recognizes.
Each profile begins with an industry question, shows the sources the agent would examine, and follows the analysis to a human decision. Every demonstration uses clearly disclosed synthetic composite data.
Data Analysis Agent for
Finance
Important performance and exposure questions depend on reconciliations across positions, transactions, benchmarks, finance systems, plans, and notes.
Explore the Finance scenario Data Analysis Agent forRetail
Store, inventory, promotion, staffing, and customer signals sit at different grains, making apparently similar locations difficult to compare.
Explore the Retail scenario Data Analysis Agent forE-commerce
Channel, behavioral, order, margin, fulfillment, return, and support systems use different identities, clocks, and attribution rules.
Explore the E-commerce scenario Data Analysis Agent forTransportation & Mobility
Demand, service, asset, maintenance, cost, disruption, and passenger information are separated across systems and use mode-specific definitions.
Explore the Transportation & Mobility scenarioWhat the agent needs
Connect the data. Follow the answer. Check the evidence.
Connected context
Bring approved sources together with the joins, terminology, calculations, permissions, and instructions that shape a useful answer.
- Multiple business sources
- Automatic or controlled joins
- Business definitions and guidance
Analytical conversation
Ask a question in natural language, examine the response, and continue the investigation without rebuilding a dashboard.
- Questions and follow-ups
- Charts and tables
- Reports and downloads
Visible evidence
Design the experience so users can see which sources, definitions, and decisions support the response instead of accepting a mystery answer.
- Source references
- Reviewable logic
- Validation against known answers
Give the agent a real question
Go beyond the number in the report.
Why did net revenue move against plan?
Break the variance down by region, product, customer, returns, and volume.
Which accounts need attention this week?
Combine behavior, service, commercial, and relationship signals.
Where is performance falling outside tolerance?
Compare sites, teams, assets, or workflows and trace the exceptions.
Turn this analysis into the monthly report.
Carry the findings into a chart, table, narrative, or downloadable output.
From question to operating use
Prove the answers. Then prove the value.
Define the intended result
Choose the recurring questions and users, record the current time and effort, and agree which operating or commercial measure should improve.
Catalog sources and access
Agree what data is in scope, how it is accessed, which users may work with it, and where the system of record remains authoritative.
Establish business meaning
Agree joins, measures, terminology, calculations, exclusions, guidance, and the outputs the team actually uses.
Validate against known answers
Test an agreed question set, review failure modes, and make uncertainty and source evidence visible.
Measure and expand
Track use, time to answer, reporting effort, resolution time, or another agreed measure; add sources, actions, or applications only where the result supports it.
Bring us one job worth improving