Data Analyst AI Agent

Data Analyst AI Agent answers your managers’ questions in plain words, straight from your data warehouse and ERP, with the chart and how each number was calculated. Decisions are made on current figures instead of last month’s report, and each person sees only the data they are already allowed to see.

Ask for a proposal See the results

Data Analyst AI Agent dashboard: morning brief, key numbers, questions from the team, data health and unusual changes

What it means for your business

40 s

from question to answer

Before
A manager raised a ticket and waited about two days for the reporting team.
With Data Analyst
The answer arrives in the chat with the chart and how it was calculated.
78%

of questions answered without the data team

Before
Analysts spent their week on small variations of the same questions.
With Data Analyst
Everyday questions are answered directly, and analysts work on forecasting and deeper analysis.
−60%

ad-hoc requests to the data team

Before
Open requests piled up in the queue, so decisions were made on old numbers or gut feeling.
With Data Analyst
The requests that remain are the ones that need an analyst, and they arrive well described.

What your managers and data team get back

01

Answers today, not next week

Managers ask in plain words and get the number, a chart and a short explanation in seconds, without joining the reporting queue.

02

Changes explained before anyone asks

When a number moves, the agent finds which regions, products or customers caused it and alerts the people responsible the next morning.

03

One number everyone trusts

Every answer uses the same agreed definitions and shows which data it used, so teams stop arguing about whose figure is right.

04

Reports that build and send themselves

A report is set up once from a plain request, checked against the data each time and delivered on schedule to the people who need it.

What it looks like in practice

A sales operations manager asks how margin changed by region. The agent reads the figures from the data warehouse and the ERP, finds what drove the change and attaches the chart and the calculation. It then offers to send the same view every week.

Sales operations managerHow did margin by region change last quarter?

Data AnalystGross margin fell from 34% to 31% in the DACH region, mainly because freight costs rose 18%. Iberia improved by 2 points. The chart and the calculation are attached. Shall I send this to the regional leads every Monday?

Looked up

Data warehouse
ERP
Snowflake
Slack

How six companies use the Data Analyst AI Agent

Each one in short: the problem, the results, and how the agent handles the work.

Managers get answers in 40 seconds, not two days

An area manager who wanted to know why a region was behind plan raised a ticket and waited about two days.

Retail chain · Enterprise, on their own servers · Live in 6 weeks

Diagram: where Data Analyst sits. Inside the retail chain, area managers and merchandisers ask the agent. It reads the sales database, the stock system and the store plans, looks up history in Snowflake and saved charts in Tableau, shares answers with store managers in Microsoft Teams, passes harder questions to the data team and follow-ups to Task Agent.

1
Understands the question in plain words
Emma, an area manager, asks in Microsoft Teams why the North region is behind plan.
2
Uses the agreed definition
It takes "net sales" from the metrics catalogue, exactly as Finance certified it, so Emma's answer matches the finance report to the euro.
3
Joins the facts across internal systems
It reads 1.4 million till receipts in the sales database, the November plan from the store spreadsheets, stock levels from the stock system and the same weeks last year from the warehouse.
4
Answers with a chart and shows its working
Emma gets a short answer, a chart of the stores furthest behind and the steps the agent took: which data, which definition, which filters.

40 sfrom question to answer
78%of questions answered without the data team
−55%open requests in the data team's queue
Every answer built on a certified metric

Month-end movements explained in 2 hours, not three days

When unpaid bills jumped 9% in October, explaining it took a billing analyst about three days: exporting data from four systems into Excel, matching customers by hand and writing a commentary for the month-end pack.

Energy utility · Enterprise, on their own servers · Live in 7 weeks

Diagram: where Data Analyst sits. Inside the utility, billing analysts and the finance team ask the agent why a number moved. It reads the billing system, meter readings and customer accounts, checks Power BI and Excel figures, shares approved explanations in the finance channel and hands the month-end pack to Document Agent.

1
Takes the question as it is asked
Marta, a billing analyst, asks in Microsoft Teams: "Why did unpaid bills rise 9% in October?" The agent finds the certified definition of unpaid bills and the two month-end figures it needs to explain.
2
Breaks the change into drivers
It compares September and October customer by customer and groups the difference by cause: estimated bills after missed meter reads, payment plans that ended, the October price change and customers moving to direct debit.
3
Traces every driver to its source
Each step of the waterfall shows the internal system it came from, and the agent checks that the drivers add up to the total in the Power BI finance report.
4
Writes the explanation for review
Marta gets a short written explanation with the waterfall chart.

2hours to explain a month-end movement
4of 4 drivers traced to their source system
−60%time spent on month-end commentary
Every explanation reviewed before it is shared

Bed census ready by 06:30 every day, weekends included

Each bed manager spent about six hours a week copying figures from the bed management system, the patient administration system and the theatre schedule into spreadsheets.

Hospital operations · Enterprise, on their own servers · Live in 8 weeks

Diagram: where Data Analyst sits. Inside the hospital, bed managers and site operations staff ask the agent for reports. It reads patient administration, bed management and the theatre schedule, delivers reports to ward channels in Microsoft Teams and the operations inbox in Outlook, and gives the bed census to Meeting Agent for the morning bed meeting. The agent provider is outside the hospital and only sends updates in.

1
Takes the request in plain words
Grace, a bed manager, writes in Microsoft Teams: "Every morning at 6:30, send the bed census by ward to the bed managers' channel." The agent works out which figures that needs and where they live.
2
Builds the report and shows a preview
It joins beds free, occupied and closed from the bed management system with today's admissions from the patient administration system, and sends Grace a preview with the steps it took.
3
Schedules it once it is approved
When Grace approves the preview, the report joins the hospital's schedule.
4
Delivers counts, never names
Each report arrives in its Teams channel or Outlook inbox on time, with an Excel file where staff want one.

6reports built from plain requests
06:30 bed census, every dayThe census is ready before the morning bed meeting, weekends included.
−6 ha week per bed manager
0patient names in any report

85% of marketing questions answered directly in Slack

Twelve marketers shared two analysts.

Online store marketing · Cloud, run for them · Live in 2 weeks

Diagram: where Data Analyst sits. The agent runs in a private cloud for the online store. Marketers and campaign leads ask the agent. It reads the data warehouse and campaign budgets, looks up Shopify and Google Analytics, answers in the marketing channel in Slack, sends new requests to the data team and campaign results to Sales Agent.

1
Understands the question in plain words
Lena asks in Slack which campaigns brought the most first orders in October and what each one cost.
2
Joins the data from every source
It takes orders from Shopify, the campaign behind each first visit from Google Analytics and the spend from the budget sheet, and counts first orders the way the metrics catalogue defines them.
3
Replies with a chart and its working
In 38 seconds Lena has a short answer, a bar chart by campaign channel and a "how I got this" note listing every source and definition, so she can check it or forward it with confidence.
4
Turns good questions into regular reports
When Lena asks for the chart every Monday, the agent schedules it in #marketing-data.

85%of questions answered in Slack
−70%tickets for the data team
38 stypical time to an answer
+2 daysa week for each analyst

Production data problems caught 5 hours earlier, every night

Production data arrives overnight from the production database, the quality inspections system and shift spreadsheets kept on the file share.

Manufacturing · Enterprise, on their own servers · Live in 6 weeks

Diagram: where Data Analyst sits. Inside the manufacturer, quality engineers and plant managers work with the agent. Each night it checks the production database, quality inspections and shift spreadsheets, publishes the yield report in Power BI, tells production lines about held data and hands offline sensors to System Guardian. The agent provider is outside the company and only sends updates in.

1
Checks each night load as it arrives
Every line's data passes the five checks, one after another, between 02:00 and 03:00, long before the report is built.
2
Stops a load that fails a check
Line 3 stops at "sensor values in range": oven 3B read 0 °C for 47 minutes when it normally runs at 182-196 °C.
3
Tells the right person what is wrong
At 03:05 the quality engineer receives a message in Microsoft Teams with a small chart of the gap and two choices: exclude the 47 minutes or release the data as it is.
4
Publishes the report on time
The 06:30 Power BI report goes out with lines 1, 2 and 4.

0reports published on bad data
5 hearlier warning
−80%manual checking time
Every decision on held data recorded

Fuel cost per km down 4% with daily alerts

Fuel cost per km moves for many ordinary reasons: routes, weather, seasonal volume.

Logistics & fleet · Cloud, run for them · Live in 3 weeks

Diagram: where Data Analyst sits. The agent runs in a private cloud for the logistics company. Fleet managers and depot leads ask the agent. It reads vehicle tracking and fuel card exports, looks up Snowflake and Google Sheets, raises unusual fuel costs in the fleet ops channel in Slack and sends fuel card cases to Finance Agent.

1
Watches every depot each morning
At 06:30 the agent compares yesterday's fuel cost per km at each depot with its expected range.
2
Rules out a data problem first
Before raising anything, it checks that the fuel card export arrived on time, that all 42 vans reported their kilometres and that the price per litre did not change.
3
Finds what is driving it
It breaks the extra cost down: two vans idled 6.5 hours a night, one fuel card was used twice on the same day, and a road-works diversion added 11 km to one route.
4
Alerts the people who can act
The alert lands in #fleet-ops in Slack with the chart and the reasons.

1 dayto spot an unusual change
−4%fuel cost per km
86%of alerts explained the same day
2 daysa month back for finance

Illustrative scenarios with unnamed companies. The figures show the kind of improvement each setup is designed to deliver; actual results depend on volume, the systems connected and how the agent is configured.

From first conversation to live in two to eight weeks

We set up the Data Analyst AI Agent around how your data team and the managers it serves work today.

1

Week 1

Learn your process

We sit with the people who do the work, map the process step by step and find the systems that hold the information. You get a written plan before anything is built.
2

Weeks 2 to 3

Customize your agent

We build and configure the agent around your process: your rules, your approvals, your wording, and connections to your databases and the tools your teams use.
3

Weeks 3 to 4

Deploy and test with your team

We install it inside your infrastructure, or in our cloud if you prefer, and test it with your team on real requests until the results meet the standard you set.
4

From week 5

Onboard your team

We train your employees and administrators, hand over the controls and stay with you: monthly reviews, updates you approve, and the next process when you are ready.

What your team and managers see

Data Analyst AI Agent, dashboard screen — **Dashboard.** What changed in your numbers this morning, why, and what the agent did about it.

Data Analyst AI Agent, ask your data screen — **Ask your data.** Answers in plain words with the chart, what moved the number and every step of how it was worked out.

Data Analyst AI Agent, reports screen — **Reports.** Recurring reports the agent builds, checks and delivers on schedule to the right people.

Data Analyst AI Agent, data access by role screen — **Data access by role.** Which data each role may see, and the agent never sees more than the person asking.

Data Analyst AI Agent: questions from business leaders

How quickly will we see results?

Your managers ask real questions within the first weeks. Most companies see fewer requests reaching the data team, and faster answers, during the first month after going live.

What does it cost?

It depends on how many people use it, the data sources it connects to and where it runs. After a first conversation you get a written proposal with the price, so you can compare it with what reporting costs you today.

Will it replace our analysts?

It takes over the repeat questions and routine reports, so your analysts spend their time on forecasting, testing and deeper analysis. Definitions, data quality and the answers that matter most stay with your people.

How do we know the numbers are right?

Every answer shows which data it used and how it was calculated, so analysts can check it. It uses only the definitions your team has agreed, and it warns you when a source is late or incomplete instead of answering from bad data.

Can we control what it does?

Yes. You decide which sources it reads, which reports it may send alone and which need a person’s approval first. You can pause it at any time.

Is our data safe?

It can run on your own servers, with its AI models, so sales, finance and customer data stay inside your company and no outside AI service sees them. We have no access to it, and your data is never used to train AI models.

Who can see what?

Each person only sees the data they are already allowed to see. The agent has no access of its own: it uses the rights of the person asking, and checks that everyone in a shared channel may see the same detail.

Does it need a data warehouse, or new dashboards?

No. It works with your data warehouse, such as Snowflake or BigQuery, or directly with your ERP, databases and spreadsheets. It works alongside Tableau, Looker and Power BI and can point to the right dashboard.

What do you need from our team?

One person who knows your data and how reports are used today, access to test versions of the sources involved, and a few hours a week from the data team while we test.

Can we start small?

Yes. Most companies start with one team or one subject, such as sales or month-end figures, see the results, and then extend the agent to other teams.

See what the Data Analyst AI Agent can do for your data team.

Tell us which questions reach your data team most often, where the data lives and who needs which figures. You get a written proposal with the expected results, the setup, a timeline and a price.

Ask for a proposal info@ihtech.ba

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