Proven in production.

Developed an AI-powered Knowledge Management System delivered as a SaaS platform, designed to unlock enterprise knowledge at scale and accelerate decision-making. Built with a cloud-native, multi-tenant SaaS architecture, the platform seamlessly integrates with leading enterprise systems such as Jira, Confluence, SharePoint, LeanIX, and other knowledge sources, creating a unified and intelligent knowledge layer across the organization.

Deployed on the Microsoft Azure platform, the solution leverages Azure Functions for scalable, event-driven processing and Azure OpenAI to enable advanced semantic search, contextual retrieval, and natural language interactions. By transforming fragmented documents and data into actionable insights, the platform empowers teams to find the right information faster, improve productivity, and drive consistent, informed outcomes across the enterprise.

AI-powered, multi-agent RFQ extraction solution purpose-built for energy companies to accelerate and standardize the quotation lifecycle. The platform intelligently ingests RFQs from images, scanned PDFs, native PDFs, and Excel files, automatically extracting, validating, and structuring commercial and technical requirements with high accuracy.

Leveraging a multi-agent AI architecture, specialized agents collaborate to interpret specifications, identify line items, normalize units, and flag inconsistencies, significantly reducing manual effort and turnaround time. Seamlessly integrated with the enterprise sales portal, the solution enables sales teams to convert RFQs into structured opportunities, quotations, and proposals faster and with greater confidence.

AI-powered operational workflow automation platform designed to transform IT and business operations from reactive to self-healing. The solution intelligently orchestrates end-to-end workflows with auto-healing capabilities, automatically detecting issues, triggering remediation actions, and minimizing service disruption.

Leveraging historical knowledge and past incident data, the platform automatically enriches tickets with contextual work notes, enabling faster diagnosis and resolution while reducing dependency on manual intervention. Machine learning–driven auto-approval models streamline routine operational decisions, accelerating turnaround times without compromising governance or compliance.

A built-in multi-environment log analyzer continuously monitors logs across development, test, and production environments, proactively identifying critical errors and early warning signals. The platform provides intelligent recommendations and actionable insights, helping operations teams prevent incidents, improve system reliability, and achieve measurable reductions in mean time to resolution (MTTR).