AI in custom applications
Add useful AI features to web, mobile and business applications where they improve a real task.
Transform operations
Make AI useful inside the software your team uses.
Panlab develops and integrates AI and machine-learning capabilities into custom applications. We begin with a specific business task, then connect the right models, data, workflow and human review to create something people can use with confidence.
Practical intelligence, connected to real work.
Applied AI & machine learning
Capabilities chosen for the work, not for the label.
An AI feature should help someone complete a task, understand information or make a decision. The right approach depends on the data available, the risk of an error and the system it must fit into.
Document understanding
Extract and organize information from suitable documents, with review for uncertain or important fields.
Knowledge assistants
Help users find and work with information from approved sources inside their applications.
Forecasting & prediction
Explore machine-learning models for demand, capacity or other planning questions when the data supports them.
Recommendations & insights
Surface relevant suggestions or patterns to help people make better-informed decisions.
Classification & detection
Identify categories, exceptions or unusual activity in data for review and follow-up.
Workflow automation
Connect AI outputs to business rules, approvals and existing systems without losing clear ownership.
Model development & evaluation
Develop or integrate suitable models, test them against the use case and monitor how they perform over time.
AI in focus
Bring intelligence into everyday application workflows.
Useful information.
People in control.
Document understanding
Turn incoming documents into reviewable information.
Extract fields, flag uncertainty and let the right person confirm information before it moves into a business system.
Planning & prediction
Use data to support the next decision.
Where suitable history exists, machine learning can help surface forecasts and recommendations for people to assess and act on.
Our approach
Start with the use case. Test before you scale.
We consider the data, desired outcome and consequences of a wrong answer early. Prototyping and evaluation help determine whether AI is appropriate and how the feature should work inside the application.
- 01
Define
Choose a specific task, its users and the result the system should support.
- 02
Assess data
Review the available information, its quality, access and privacy requirements.
- 03
Prototype & evaluate
Test a suitable approach against real examples and agree where human review is needed.
- 04
Integrate & improve
Build the feature into the application, observe its use and refine it as conditions change.
2017
Established in India.
Working across borders.
Clients across
- India
- Africa
- Middle East
- Singapore
- US
- Europe
AI & machine learning FAQs
Questions about applying AI to your software?
Tell us the task, data and application you have in mind. We’ll help assess a practical way forward.
Talk with our teamCan Panlab add AI to an existing web or mobile application?
Yes. We can assess the application, available data and integration points, then develop or connect a suitable AI capability within the existing user workflow.
Do you develop machine-learning models or integrate existing AI services?
Both approaches are possible. We choose based on the use case, data, evaluation needs, maintenance effort and available services rather than assuming a custom model is always necessary.
What kinds of tasks can AI help with?
Suitable tasks may include document extraction, knowledge search, classification, recommendations, forecasting and workflow assistance. Each use case needs assessment against the quality of the data and the impact of errors.
Can people review AI-generated results before they are used?
Yes. We can design review, edit and approval steps so users can verify important outputs before they affect records, customers or business decisions.
What do you need to start an AI or machine-learning project?
A clear task and sample data are a good starting point. We then review data quality, access, privacy considerations, success criteria and the systems the feature must work with.
Let’s find the right use case
What task should your application help people do better?
Share the workflow, data and goal behind your idea. We’ll discuss whether AI or machine learning is suitable and how it could fit into your software.
Relevant data.
Useful automation.
Clear human oversight.