Custom apps took ten months to build. Now, autonomous AI agents handle UI, coding, and QA to deliver finished tools in 15 minutes.
A technology company providing enterprise AI platforms that localize content for over 20 Indian languages and integrate with more than 200 services.
Developing custom enterprise applications traditionally required up to ten months of developer time, creating a bottleneck for innovation. Existing...
“What we've done is we have created a sequence of AI agents. One handles UI/UX, another handles business requirement documentation and so on for system architecture document generation, backend and frontend coding, QA testing, and deployment. That means you can now actually go and have a chat with the AI, it will understand what you're trying to do, and create the entire application for you.”
Open commerce infrastructure provider for the ONDC network.
Cloud computing services, AI infrastructure, and data analytics platforms for enterprises.
Adya's Automated software development is part of this use case:
Related implementations across industries and use cases
Manual review of sensitive files took two days. AI agents now finish the work in one hour.
Voice integration demanded 400 lines of code. A pre-built framework cuts that to 40, enabling rapid agent deployment.
Steep learning curves caused high user churn. Now, a native AI agent lets builders fluidly mix text prompts and point-and-click tools.
Steep learning curves caused high user churn. Now, a native AI agent lets builders fluidly mix text prompts and point-and-click tools.
Debugging scattered across individual notebooks meant week-long fixes. One shared platform now catches errors before customers do.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
A mistranslated word could derail global R&D projects. Now, researchers instantly refine technical papers & communicate seamlessly across languages.
Custom apps took ten months to build. Now, autonomous AI agents handle UI, coding, and QA to deliver finished tools in 15 minutes.
A technology company providing enterprise AI platforms that localize content for over 20 Indian languages and integrate with more than 200 services.
Developing custom enterprise applications traditionally required up to ten months of developer time, creating a bottleneck for innovation. Existing...
“What we've done is we have created a sequence of AI agents. One handles UI/UX, another handles business requirement documentation and so on for system architecture document generation, backend and frontend coding, QA testing, and deployment. That means you can now actually go and have a chat with the AI, it will understand what you're trying to do, and create the entire application for you.”
Open commerce infrastructure provider for the ONDC network.
Cloud computing services, AI infrastructure, and data analytics platforms for enterprises.
Adya's Automated software development is part of this use case:
Related implementations across industries and use cases
Manual review of sensitive files took two days. AI agents now finish the work in one hour.
Voice integration demanded 400 lines of code. A pre-built framework cuts that to 40, enabling rapid agent deployment.
Steep learning curves caused high user churn. Now, a native AI agent lets builders fluidly mix text prompts and point-and-click tools.
Steep learning curves caused high user churn. Now, a native AI agent lets builders fluidly mix text prompts and point-and-click tools.
Debugging scattered across individual notebooks meant week-long fixes. One shared platform now catches errors before customers do.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
A mistranslated word could derail global R&D projects. Now, researchers instantly refine technical papers & communicate seamlessly across languages.