Custom AI Applications & Agents
AI built for one business problem, wired into the systems that hold the answer. Six to twelve weeks from scope to something running against real data.
The demo works. The deployment is the hard part.
Building something impressive on a sample of data takes a fortnight. Getting it to run every day, against live systems, on data that changes underneath it, is a different job.
- It has to read from the ERP, the order system and the product catalog — each with its own idea of what a product is.
- It has to be right often enough that people stop double-checking it, and honest enough to say when it is not sure.
- It has to be watchable, so somebody can tell six months later whether it is still working.
- It has to cost a knowable amount per use, or finance will switch it off.
We build for that from the first week, because we also run the platforms and the operations these applications have to live inside.
How we build
Six to twelve weeks, scoped against one decision or one process. Not a platform programme. One problem, one owner, one measurable before-and-after.
Integrated, not adjacent. ERP, order management and product systems are part of the build, not a later phase. This is the single biggest reason AI projects slip.
Built on a data foundation we already run. Our data quality and governance engine sits underneath, so the application is working from data that has been checked rather than hoped about.
Agentic where it earns it. Multi-step agents where the task genuinely needs judgment and tool use. A simpler pattern where it does not, because simpler things break less.
Document and invoice automation
Demand forecasting and planning
Price matching and competitive intelligence
Recruiter and applicant screening
Competitive and trend scanning
Product Selector
Sales Role-Play
Trend Scouter
Two questions set the class
Pipeline or agent
Rails that climb with the class
Nothing leaves unredacted
Three gates, no exceptions
Your rules become the test
A step up means a new class
Private Enterprise AI
Where the data cannot leave, we deploy the application inside your own perimeter. Enterprise sign-on, role-based access, an AI gateway, your choice of commercial or open-weight models, and retrieval grounded in your own documents. AWS-native, live in six to eight weeks, and no business data leaves your environment.
Where these run
An Indian FMCG group
Private Enterprise AI — a company-wide internal assistant, so every employee works with governed AI inside the perimeter rather than reaching for public tools.
A leading US home-furnishings retailer
Multiple production AI use cases running on product data.
A global home-improvement retailer
Price-matching and competitive intelligence in production.
A global industrial manufacturer
The Product Selector agent live in production, with conversation analytics behind it.
Frequently asked questions
What can you realistically build in six to twelve weeks?
One application against one process, integrated and running on real data. Not a platform, and not five use cases at once. The timebox is what keeps it honest.
Do you build agents, or applications?
Both, and the distinction matters less than whether the task needs multi-step judgment. Where it does, an agent. Where it does not, a simpler application that will break less often.
Which models do you use?
Whichever suits the task and your constraints — commercial or open-weight. Under Private Enterprise AI the choice sits behind a gateway you control, so it can change without rebuilding the application.
Will it work with our ERP?
That is the part we treat as the main event rather than a final phase. Integration with the ERP, order management and product systems is scoped in week one.
Can our data stay inside our own environment?
Yes. Private Enterprise AI runs the whole thing inside your perimeter, with no business data leaving it.
What happens once it is live?
It moves into managed AI operations — monitoring for drift and quality, incident response, cost attribution and audit evidence. That is a separate page, and it is where most of the long-term value is protected.
Can you work on something another vendor built?
Often, yes. It starts with establishing what the existing system actually does and what it is connected to, which is usually less than the documentation claims.
How do you decide how an application is built?
We ask two questions. How much does the model decide? What is the most sensitive data it touches? The answers set the architecture, the rails, the tooling and the model.
Bring us one process
One that is slow, manual, or full of judgment calls nobody has time for. We will tell you whether AI is the right answer, what it would need to reach, and what it would take to find out.
Connect with our experts
DJ Basumatari
Chief Executive Officer
Abhishek Jain
Director - Solutions & Innovation
