Beyond the dashboard
See why it happened, what happens next, and how to stop it.
Dashboards tell you how many. SkyworkBI builds AI data agents that tell you the rest, on Microsoft Fabric, Databricks, Snowflake, Google Cloud or AWS. Ask in plain English and get the cause, a forecast and the actions most likely to fix it, drawn from your tables and your customer text, with the evidence attached.
One question, one data source, 1–2 weeks, at no cost.
CHAT WITH A SKYWORKBI DATA AGENT
CSAT in the North fell from 81 to 74. Most of the drop comes from customers who had a technician visit. Their comments point to four themes:
Rows behind “Missed appointments”, North region
survey #88214“Waited in all day, nobody came and nobody called.”survey #88302“Second time the engineer didn’t turn up. Booked a day off for this.”ticket #51190Customer asked for compensation after a missed install slot.job #T-2231Technician note: “Previous visit cancelled, parts not on van.”SQLJoined survey comments to job records on customer ID, filtered to region = North, Q3.
If missed visits stay at today’s rate, North CSAT is likely to fall to 70 by December (range 67–73), and about 1,140 customers move into high churn risk.
Three actions most likely to stop the decline, ranked by expected effect:
1Call customers 24 hours before a visit. The South already does this and its missed-visit complaints are 60% lower. est. +3 CSAT2Stock the two most-used parts on every van. Job notes show “parts not on van” behind 1 in 4 repeat visits. est. −25% repeat visits3Credit customers after a second missed visit. They are four times more likely to cancel. est. −18% churn in this group
Illustrative example with sample data.
Built inside the platforms you already run
Microsoft Fabric, Power BI, Databricks, Snowflake, BigQuery, Looker, AWS, Amazon Quick, Azure, Microsoft Teams, Slack, Excel
The full answer
Four questions. One agent.
Most reporting stops at the first question. A data agent carries on to the cause, the forecast and the fix, using the same data and definitions.
Describe
“How did CSAT move by region last quarter?”
Governed numbers from your semantic model.
Diagnose
“Why did it drop in the North?”
Drivers from the numbers, reasons from comments, calls and notes.
Predict
“Where will it be by December?”
Forecasts and risk scores with a range, not a single guess.
Prescribe
“What should we change to stop it?”
Ranked actions with expected effect, and the workflow to act on them.
Why teams call us
Plenty of data. Too few answers.
Most companies already pay for a modern data platform. The questions still queue up for the same three analysts.
Today
- Business questions wait days in the analyst queue.
- Forty dashboards, three definitions of “active customer”.
- Survey comments, call transcripts and job notes go unread.
- An AI pilot gave a confident wrong answer, so nobody trusts it.
- Licences for BI and AI tools that few people open.
With SkyworkBI
- People ask in Teams, Slack or Power BI and get an answer in minutes.
- One set of metric definitions, shared by the agent and the dashboards.
- Every comment, call and note is read, grouped into themes and linked to the numbers.
- Each answer shows its query and its source rows, and is tested before rollout.
- It all runs on the platform you already pay for, and your team owns it.
What we build
From the first question to the action it leads to.
We design, build and hand over the whole chain: the data and context an agent needs, the agent and its forecasts, and the dashboards, apps and workflows around it.
Data agents
Plain-English answers from your warehouse, with the query and rows shown.
Data agentsText analytics
Themes, sentiment and root causes from comments, calls, tickets and notes.
Read every commentForecasting & prediction
Demand, churn and risk forecasts, with early warnings and recommended actions.
Predictive analyticsContext & semantic layer
Shared definitions and business rules, so agents and dashboards agree.
Context layerGovernance & evals
Test sets, permissions and monitoring, so you know when an answer is wrong.
Trust and testingPower BI & dashboards
Reports people open, data apps and written briefings for leadership.
BI and data appsPower Apps & automation
Apps and workflows that act on the answer: alerts, approvals and follow-ups.
Power PlatformCloud & BI migration
From Tableau, Qlik, SSRS or on-premises SQL to Azure, AWS or a modern lakehouse.
Plan a migrationWhere people already work
Answers in Teams, Slack and Power BI.
Nobody needs to learn a new tool. The agent answers in the chat your team already uses, inside Power BI or Excel, or in a small web app we build for you.
- Mention the agent in a channel and the whole thread sees the answer.
- Follow-up questions keep the context of the first one.
- Anything worth keeping becomes a chart, a scheduled briefing or a dashboard page.
@SkyworkBI what are new detractors saying since the March price change?
1,204 detractor comments since 1 March. 38% mention the new bill format, mostly customers on legacy plans. Wait times are steady, so the change looks billing-driven, not service-driven.
View rows in Power BIThat matches what the call centre is hearing. Can you split it by plan?
Every industry writes things down. We read it.
Hear every call, not a sample.
QA teams score a handful of calls per agent each month. A data agent reads every transcript, so coaching, complaints and call reasons come from all of them.
- Call transcripts and wrap codes
- QA scorecards and complaints
- Repeat-contact and handle-time data
- Delivery-date changes: 31%
- Refund status: 24%
- Password resets: 12%
Know why customers churn before they call to leave.
Join complaints, network tickets and survey comments with billing and usage, so retention teams see the reason behind each churn risk.
- NPS and survey comments
- Network and fault tickets
- Billing changes and plan history
- Bill increases after promo ends
- Repeated outages in one postcode area
- Slow fault resolution
Find the story inside claims and complaints.
Claim notes, adjuster comments and complaint letters explain what scores and cycle times can’t. The agent reads them under your existing access controls.
- Claim notes and adjuster comments
- Complaints and regulator-reportable cases
- Customer emails and chat logs
- Delays waiting for repair approval
- Unclear excess deductions
- Hard-to-reach handlers
Turn reviews and returns into merchandising decisions.
Product reviews, return reasons and service chats say what sales data can’t: why a product disappoints and who it disappoints.
- Product reviews and ratings
- Return reasons and free-text notes
- Chat and email transcripts
- Runs small in the shoulders
- Colour differs from photos
- Zip quality on two styles
Hear what patients say between the scores.
Patient feedback, complaints and contact-centre notes show where the experience breaks down. We can work on de-identified data inside your environment.
- Patient experience surveys
- Complaints and PALS-style cases
- Booking and contact-centre notes
- Appointment letters arriving late
- Parking and access at two sites
- Waiting-room updates
Put your best technicians’ know-how in everyone’s hands.
Job notes record how problems actually get fixed. The agent reads them across thousands of jobs and shows what first-time fixes have in common.
- Technician and engineer job notes
- Work orders, parts and repeat visits
- Customer feedback after visits
- Check fault history before the visit
- Carry the two common spare parts
- Replace seals during any service
Spot safety and adherence signals in free text.
Adverse-event reports, medical information enquiries and patient-support notes are mostly written text. The agent reads them alongside prescription and sales trends, under your access controls.
- Adverse-event and complaint narratives
- Medical information and patient-support notes
- Prescription and sales trends by region
- Dizziness, mostly over-65s on the higher dose
- Packaging hard to open
- Missed doses after a supply delay
Built to be trusted
An answer you can check is an answer people use.
Teams stop using AI after the first confident wrong answer. We design against that from day one.
Stays in your tenant
The agent runs in your own cloud account, under the security and permissions you already have.
Shows its working
Every answer links to the query, the definitions and the rows it used.
Tested before rollout
We score the agent against a set of your real questions before anyone relies on it, and again after every change.
Yours to keep
We build on your platform’s own agent tools and hand over the code, the docs and the tests.
We built a data agent for a CMO that answers their questions directly from millions of rows of their own data, replacing the back-and-forth over reports nobody used.SkyworkBI project. Client name kept confidential.
The free pilot
See the answers before you decide anything.
Pick one question
Choose a question your dashboards can’t answer and the data source behind it.
Connect a sample
We work on a sample in your environment, anonymised if you prefer.
Build and test
We set up the context and the agent, then test it on your real questions.
Your team asks
Your people put their own questions to it and decide whether it’s worth going further.
Questions we hear
Straight answers.
We already have Power BI. Why do we need an agent?
Power BI is a good foundation, and we build on it. Dashboards answer the questions someone planned for. The agent answers the next question, and the why behind the numbers, using the same model and permissions.
Our data is sensitive.
The agent runs in your own Fabric, Databricks, Snowflake, Google Cloud or AWS account, under your existing security. For the pilot we can work on an anonymised sample.
We tried a chatbot and it made things up.
Chatbots answer from documents and guess when they don’t know. A data agent queries your data through agreed definitions, shows the rows it used, and is tested against your real questions before rollout.
Which platform should we use?
The one you already run. Microsoft, Databricks, Snowflake, Google and AWS all have native agent tools now. We pick the setup that fits your data and licences.
What happens after the pilot?
If the answers are useful, we scope the full build together: which sources, which teams and which questions. We quote once that scope is clear.
Which question would you ask first?
Send us one question your dashboards can’t answer, and the data source behind it. We’ll reply with how a pilot would work for you.