Data Agents & Conversational Analytics
Ask questions about your business. Get answers you can check.
A data agent turns plain-English questions into queries on your approved business data, respects each person’s permissions, and shows how it reached every answer.
Which product lines grew fastest last quarter compared with the quarter before?
- Understand the questionRevenue, completed quarters onlyDone
- Use the approved semantic modelSales model, agreed definitionsDone
- Query with your permissionsRead-only, row-level security appliedDone
Support plans grew fastest: up 18% quarter on quarter.
Source: Sales model. The query and filters behind every answer can be opened and checked.
Problems this solves
- Simple questions wait days for an analyst
- Dashboards answer yesterday’s questions, not today’s
- People export data to spreadsheets to answer follow-up questions
- Leaders want AI on company data but worry about accuracy and access
What you receive
- A data agentConnected to your semantic model or warehouse with read-only access.
- Business definitionsAgreed metrics and terms the agent uses to interpret questions.
- PermissionsEach person sees only the data they’re allowed to see.
- Answer checksTested against known questions and answers before launch, with the query visible for every answer.
Typical use cases
Each project starts with an agreed way to measure success.
Sales performance questions
“Which regions missed target last month, and why?” answered from the sales model.
Measured by: answer accuracy on a test set and analyst requests avoided.
Finance questions
Margin, cost and spend questions answered with the underlying query shown.
Measured by: accuracy against finance-approved figures.
Operations questions
Order, inventory and service-level questions answered on demand.
Measured by: time to answer and repeat questions resolved.
How we deliver it
Prepare the data
Confirm the semantic model, definitions and permissions are ready.
Define questions
Collect real questions and their correct answers to test against.
Build and evaluate
Configure the agent and measure accuracy on the test set.
Roll out
Start with a small group, review answers, then widen access.
Technologies We Work With
Data agents work best on a well-modeled data foundation, such as a Power BI semantic model or a governed warehouse.
- Microsoft Fabric data agents and Power BI semantic models
- Azure OpenAI and Azure AI Foundry
- SQL warehouses and lakehouses
- Row-level security and audit logging
Product names are trademarks of their owners. TriAxiom Tech is independent and does not hold partner status with these vendors.
Business performance
Sales and finance model, fiscal year, revenue in US dollars
Revenue and target by month
Revenue by region
Product lines
| Product line | Revenue | vs. target | Gross margin |
|---|---|---|---|
| Subscriptions | $3.37M | +4.7% | 51.6% |
| Professional services | $2.41M | −2.5% | 33.8% |
| Hardware | $1.87M | +1.7% | 22.1% |
| Support plans | $1.26M | +7.9% | 57.6% |
| Total | $8.91M | +2.4% | 41.5% |
Days sales outstanding
| Month | Revenue | Target |
|---|---|---|
| Jan | $615K | $600K |
| Feb | $584K | $620K |
| Mar | $649K | $640K |
| Apr | $716K | $660K |
| May | $696K | $680K |
| Jun | $734K | $700K |
| Jul | $785K | $720K |
| Aug | $748K | $740K |
| Sep | $817K | $760K |
| Oct | $858K | $780K |
| Nov | $823K | $800K |
| Dec | $886K | $820K |
One semantic model, two ways to use it
A data agent works best on the same governed semantic model that powers your dashboards. This Business Performance Dashboard shows the kind of model a data agent can answer questions from.
This is a demonstration built with synthetic data. It shows the kind of work we deliver; it is not a client project or a client result.
See all demonstrationsSupport and handover
How we work with you during and after the project.
- Project communicationA named contact, a shared plan, and milestone reviews at times that suit your time zone.
- Access and data handlingWe use accounts you create and control, least-privilege access, and remove it at the end of the work. Data stays in your environment wherever possible.
- Code and account ownershipCode, reports, models and cloud resources are created in your tenant or repositories and belong to you.
- Documentation and trainingWritten documentation and training sessions so your team can run and change what we build.
- Post-launch supportA support period after go-live, with optional ongoing support and optimization.
- Clear, separate costsOur fees are separate from software licenses, cloud consumption and advertising spend, which you pay directly to the provider.
Related services
Frequently asked questions
How accurate are the answers?
Accuracy depends on the data model and definitions. We test against a set of real questions with known answers and agree an acceptable level before launch. Every answer shows its query so it can be checked.
Can it see data people aren’t allowed to see?
No. The agent queries with each person’s own permissions, including row-level security.
Can it change data?
No. Data agents use read-only access.
Do we need a data platform first?
A governed model makes a big difference. If your data isn’t ready, we usually start with a data engineering or Power BI modeling step.
How is this different from a knowledge assistant?
A data agent answers from structured business data such as sales and finance figures. A knowledge assistant answers from documents such as policies and procedures.