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BACKGROUND
A leading New York-based renewable energy provider partnered with Robosoft to modernize how business leaders access operational data and performance KPIs.
The goal was to replace manual reporting cycles with an AI-powered conversational platform that allows users to interact with data through natural voice and text queries, securely and at enterprise scale.
THE CHALLENGES
Addressing the
critical gaps
Thousands of unstructured documents and siloed systems limited centralized visibility.
Generating reports required 1-2 hours of analyst effort, delaying decision-making.
The platform needed low latency, high accuracy, and seamless scalability.
External AI training data and public models were not permitted due to compliance requirements.
Without feedback loops, improving AI accuracy over time was difficult.
THE CHALLENGES
Addressing the
critical gaps
Thousands of unstructured documents and siloed systems limited centralized visibility.
Generating reports required 1-2 hours of analyst effort, delaying decision-making.
The platform needed low latency, high accuracy, and seamless scalability.
External AI training data and public models were not permitted due to compliance requirements.
Without feedback loops, improving AI accuracy over time was difficult.
THE ROBOSOFT SOLUTION
Built using proprietary data only, with no reliance on public datasets or third-party training sources.
Enabled intuitive voice and text interaction for KPI queries, document search, and report generation.
Architected on Microsoft Azure with Docker and Kubernetes (AKS) to ensure resilience, performance, and enterprise-grade availability
Implemented Reinforcement Learning with Human Feedback (RLHF) to refine responses and improve contextual accuracy over time.
THE ROBOSOFT SOLUTION
Built using proprietary data only, with no reliance on public datasets or third-party training sources.
Enabled intuitive voice and text interaction for KPI queries, document search, and report generation.
Architected on Microsoft Azure with Docker and Kubernetes (AKS) to ensure resilience, performance, and enterprise-grade availability
Implemented Reinforcement Learning with Human Feedback (RLHF) to refine responses and improve contextual accuracy over time.
Clear outcomes
that drive growth
Fine-tuned models continuously improve through reinforcement learning, delivering reliable, enterprise-grade responses without relying on any third-party data sources.
What once took analysts 1–2 hours of manual effort can now be completed in under 30 seconds, thus freeing teams to focus on decisions, not data wrangling.
Analysts and managers spend significantly less time on recurring reports, with 80% of PPT and Excel output generated automatically.
The entire document library is now searchable and retrievable through natural voice and text queries, saving manual labor.
The platform operates entirely on proprietary data with no external dependencies, meeting strict regulatory and data security requirements from day one.
Build smarter, faster, and sustainable engineering solutions.
