AI and Machine Learning Applied to Real Business Data
We build AI and machine learning into the systems your business runs on: generative AI for documents and content, predictions from your historical data and automation for decisions that follow clear patterns. Each one is scoped to a measurable business problem.
Core stackPython · Azure OpenAI · Azure AI Document Intelligence · scikit-learn · pandas · .NET / C# · SQL Server · Microsoft Azure
When AI and ML Can Make a Difference
Machine learning is useful when you have enough relevant data and a decision or task that repeats.
No data, no model.
If the data isn’t there yet, we’ll say so and help you start collecting the right data first.
How we decideDocuments are processed by hand
Invoices, forms, contracts or emails are read and re-typed into systems one at a time.
Planning relies on guesswork
Demand, stock or resource planning is based on instinct rather than the patterns in your own data.
Problems are spotted too late
Unusual transactions, quality issues or churn signals are noticed only after they cost money.
Content work is slow and repetitive
Product descriptions, summaries and reports take hours to write, review and keep consistent.
AI and ML Capabilities We Build
Pick one to see what it usually involves and how we typically build it.
Generative AI features
LLM-powered features inside your applications: summarizing, drafting, rewriting and answering questions from your own content.
- Document and email summaries
- Drafts generated for human review
- Q&A over your own content
Document processing
Extracting structured data from invoices, forms and documents and passing it into your systems, with review for low-confidence cases.
- Invoice and form data extraction
- Validation against business rules
- Human review for exceptions
Predictive analytics and forecasting
Models trained on your historical data to forecast demand, sales or resource needs, delivered where planners already work.
- Demand and sales forecasting
- Resource and capacity planning
- Forecasts inside dashboards
Classification and anomaly detection
Models that categorize records or flag unusual activity, so issues are caught before they become expensive.
- Record and ticket classification
- Unusual transaction detection
- Churn and risk signals
Recommendations and personalization
Recommending relevant products, content or next steps based on behaviour and history.
- Product recommendations
- Personalized content
- Next-best-action suggestions
AI in existing software
Adding AI capabilities to existing applications through APIs, with accuracy and cost monitoring once live.
- AI features in existing apps
- API-based model integration
- Accuracy and cost monitoring
Data First. Models Second.
Every engagement follows the same problem-first Systems Engineering Method we use across AppPotter.
Define the business problem
We agree which decision or task the model improves and how success will be measured.
Assess the data
We check whether enough relevant, reliable data exists and what preparation it needs.
Choose the approach
An existing AI service, a trained model or plain rules: whichever is simplest and reliable enough.
Build and validate
Models and features are tested against real data, with accuracy measured before launch.
Integrate into the workflow
Results are delivered inside the systems people already use, with review where it’s needed.
Monitor and retrain
Accuracy, drift and cost are monitored and models are updated as the data changes.
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: Documents re-typed into systems by hand
After: Data extracted automatically, with review for exceptions
Before: Planning based on instinct
After: Forecasts built from your own historical data
Before: Issues discovered after the damage is done
After: Unusual patterns flagged early
Before: Repetitive writing done from scratch
After: Drafts generated for people to review and approve
Before: AI experiments nobody measures
After: Accuracy and cost tracked after launch
Chosen for Your Systems, Not Our Preferences
We use managed AI services where they’re good enough and train custom models only when the problem needs it. Our implementation depth is in Python and .NET, on Azure or AWS.
Where we have the deepest production experience
- PythonML & AI services
- Azure OpenAIGenerative AI
- Azure AI Document IntelligenceDocument processing
- scikit-learnMachine learning
- pandasData preparation
- .NET / C#Application layer
- SQL ServerData
- Microsoft AzureCloud
Also used when the project calls for it
- Other models
- Data
- 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 AI & Automation
Applying AI and automation to real workflows, so routine work doesn't depend on someone remembering to do it. Machine learning belongs where the data already exists and a decision repeats often enough to matter.
It depends on the problem. Some tasks work with existing AI services and little of your own data; forecasting and custom models need a meaningful history of relevant, reliable data. We assess this before recommending a build.
Start with existing services where they’re good enough, because they’re faster and cheaper. Custom models make sense when your data or problem is specific enough that general services fall short.
We agree how accuracy will be measured before the build and test against real data. Where mistakes are costly, results go to a person for review rather than being acted on automatically.
Yes. Most AI features can be added to existing applications through APIs, without rebuilding the system.
We decide up front what data is used, where it’s processed and who can access it and design around your privacy and security requirements.
Data changes over time, so we monitor accuracy, drift and running costs and update or retrain models when needed.
Tell Us What You Want Your Data to Do
We’ll look at the problem and the data behind it and tell you honestly whether AI, machine learning or a simpler approach is the right fit.
Book a GTM Systems Assessment30-minute call · No obligation · No sales pitch