Prior-employment experience

Enterprise AI Platform Delivery

Making software understanding usable at enterprise scale.

Founder experience leading the development of a multi-agent platform for legacy code analysis and modernization, from concept through delivery.

The situation

In a prior employment role, Dimitar led work on an AI-enabled platform for understanding legacy software. The challenge connected software analysis, enterprise context and a usable architecture for real delivery.

A proof of concept built independently

The first idea was a chat interface over an existing internal tool. Dimitar built the first proof of concept independently, analyzing codebases directly. It surfaced hidden business flows and system capabilities, not just file summaries.

From chat wrapper to platform

The evidence from the proof of concept moved the initiative from a chat wrapper to a code-intelligence platform. After stakeholder buy-in, Dimitar designed the target architecture and led the work to production, with a team scaled to 16+ AI engineers.

The architecture contribution

The work brought together multi-agent analysis, retrieval and graph-based structures to investigate software. Architecture decisions had to address integration, evidence organization and the practical operation of a production platform.

Into enterprise conversations and pilots

The platform was taken into many enterprise conversations and commercial pilots. That gave a direct feedback loop between product design, sales conversations and real transformation use cases.

Discovery cut from weeks to days

Discovery for legacy transformation work went from weeks to days. Delivery teams started with a richer picture of unfamiliar systems, and the pilots supported the central idea: AI is most valuable when it helps architects understand the real system beneath the documentation.

Why this experience matters

It informs Sparkling's focus on connecting code-level evidence with system understanding and an accountable architectural interpretation. Tools support that work; the quality of the conclusions still depends on validation and context.

The architecture lesson

Software intelligence is useful when it improves a real architecture or modernization decision.

Work undertaken in prior employment, not a Sparkling client engagement. Employer-owned platforms and intellectual property remain separate from Sparkling's independently developed methods.

Make the next architecture decision with better evidence.

Tell us about the system, the decision and what is changing. We will establish the architecture contribution that fits.

Discuss your situation