Clear Answers to Your Business Questions
Where revenue really comes from, which customers are at risk and where operations lose time. We analyze your own data to answer the questions behind your next decision, then explain the findings in plain language.
Core stackSQL Server · Azure Data Factory · Microsoft Fabric · Azure Data Lake · Python · Power BI · PostgreSQL · Microsoft Azure
When Analysis Pays Off
Analytics work usually starts with a question nobody has been able to answer.
Analysis answers questions. Dashboards track them.
Once an answer matters every week, we turn it into a dashboard so you can follow it over time.
Dashboards and data visualizationPlenty of data but few answers
Years of CRM, sales and operations data sit in your systems without anyone asking them a clear question.
Decisions run on gut feel
Pricing, hiring and spending choices are made on instinct because pulling the numbers takes too long.
Marketing spend can't be tied to revenue
You know what each campaign costs but not which ones brought in paying customers.
Data sits across too many tools
Customer, sales and operations data live in separate systems, so any analysis starts with days of exports.
Six Kinds of Analysis We Deliver
Pick one to see what it usually involves and how we typically build it.
Scoping the business question
Turning a broad concern into a specific question with a decision attached, so the analysis has a clear finish line.
- Question and decision workshop
- Measures of success
- Data needed to answer it
Data audit and quality checks
A check of what data you have, where it lives, how complete it is and what needs fixing before anyone draws conclusions.
- Source and field inventory
- Duplicate and gap checks
- Fix list by priority
Revenue and customer analysis
Where revenue comes from, which customers are most valuable and which are showing signs of leaving.
- Revenue by channel, product and segment
- Customer churn risk
- Campaign spend tied to revenue
Operational analysis
Where work stalls, which steps cost the most and where capacity runs short.
- Process and cycle time analysis
- Cost to serve
- Capacity and workload patterns
Forecasting
Sales, demand and cash forecasts built from your own history, with the assumptions written down so you can judge them. Machine learning models are handled with our AI and ML team.
- Sales and demand forecasts
- Cash flow projections
- Scenario comparisons
Turning analysis into dashboards
When an answer needs tracking over time, we turn the analysis into a Power BI dashboard that refreshes on an agreed schedule.
- Recurring KPI reports
- Agreed metric definitions
- Scheduled refresh
Question First. Data Second.
Every engagement follows the same problem-first Systems Engineering Method we use across AppPotter.
Agree the question
We pin down the decision the analysis should support and what a useful answer looks like.
Audit the data
We check what data exists, where it lives and whether it is good enough to answer the question.
Prepare and join
Data from different tools is cleaned and joined so customers, deals and invoices line up.
Analyze
We test explanations against the data and look for patterns that hold up, not just interesting charts.
Report in plain language
You get the findings, the evidence behind them and the limits of what the data can tell you.
Track what matters
Answers you need regularly become dashboards so you can follow them over time.
What Changes When the Process Runs on the Right System
We agree on what to measure during discovery, so success is defined before the build starts, not after launch.
Before: Decisions made on gut feel
After: Decisions backed by your own numbers
Before: Marketing spend with no link to revenue
After: Spend traced to the customers it brought in
Before: At-risk customers noticed after they leave
After: Early signs of churn while there is time to act
Before: Operations problems known only by anecdote
After: Clear evidence of where time and cost go
Before: One-off spreadsheets nobody can repeat
After: Analysis that can be rerun and tracked
Chosen for Your Systems, Not Our Preferences
We work in Python, SQL and Power BI, reading from the CRM, finance and operational systems you already use. Where your data lives elsewhere, we work with that instead.
Where we have the deepest production experience
- SQL ServerData
- Azure Data FactoryPipelines
- Microsoft FabricData platform
- Azure Data LakeStorage
- PythonData engineering
- Power BIReporting
- PostgreSQLData
- Microsoft AzureCloud
Also used when the project calls for it
- Data platforms
- Sources we migrate from
- Cloud
Senior Engineers, Directly Involved
- Founder-led delivery
Parth and Umesh are personally involved in every engagement.
From scoping the work to standing behind its outcome, backed by Parth's 14+ years of enterprise technology and delivery experience.
Meet our leadership - Current engagement · under NDA
A long-term software engagement with a US-based technology company.
Client identity and project details are undisclosed due to confidentiality obligations. We treat your work the same way.
See our approach
Ways to Work Together
- Advisory & Discovery
Short-term Strategy Engagements
For a focused, short-term engagement - an assessment, an architecture review, or technical due diligence - before any build begins.
- Comprehensive architecture reviews
- Due Diligence of your tech stack
- Roadmap and Estimation
- Fixed Price
Fixed Cost Model
For clearly-scoped integration or build work, where the outcome and boundaries are already well defined.
- Change Management process
- Milestone-based delivery
- Quality & SLA Commitments
- Dedicated Team
Dedicated Product Engineering Team
For long-term product or systems work - a sustained AppPotter engineering team working against your roadmap, not rotated project to project.
- Seamless onboarding and roadmap planning
- Fixed Monthly Retainer
- PO/Tech-lead Collaborations
Whichever structure fits, Parth and Umesh are personally involved, from scoping the engagement to standing behind its outcome.
Meet our leadershipPart of Business Systems Engineering
Connecting the operational systems, workflows and data a growing business runs on.
Data Analytics FAQs
Still unsure? A 30-minute conversation usually answers the rest.
Book a callAnalysis answers a specific question, such as why margins fell last quarter. Dashboards track the numbers you need to watch over time. Many projects start with analysis and end with a dashboard.
Usually what you already have in your CRM, accounting and operational tools. The data audit tells you early whether it is enough to answer the question.
Most business data needs some cleaning. We check quality first, fix what can be fixed and tell you plainly how far the data can be trusted.
Yes. Statistical forecasts built from your history cover most needs. Where machine learning adds real value, our AI and ML team can build and maintain the model.
Python and SQL for the analysis and Power BI for presenting results. Your team does not need to learn new tools to use the findings.
Most analyses are fixed price once the question and data sources are agreed. Ongoing analysis can run on a monthly arrangement.
Tell Us What You Want Your Data to Answer
We'll look at the question and the data you have, then tell you honestly whether it can be answered.
Book a GTM Systems Assessment30-minute call · No obligation · No sales pitch