AI runtime for a global energy access platform

How a multi-country solar and device-financing platform used Kognita to turn code, databases, Jira, and infrastructure into one queryable semantic layer for every team.

Multi-country

operating footprint

High-volume

payments, SMS, and device events

Cross-cloud

production infrastructure

The challenge

A small engineering team was responsible for a large production estate with many tenant-specific flows and configuration paths.

Support, product, QA, BI, DevOps, and leadership all needed system answers, but most questions still escalated to senior engineers.

Generic AI coding tools behaved like search over files, missing the relationships between services, tables, queues, Jira context, and runtime behavior.

What Kognita ran

The pipeline split code and database structure into logical chunks, enriched them with plain-English system meaning, embedded them, and exposed the result through MCP.

A managed agent runtime could query code, schema, Jira, read-only databases, and infrastructure context from the same conversation.

Role-specific instructions and tools gave each team a focused assistant while keeping the underlying semantic layer shared.

Business outcome

Product teams could refine requests and identify release risk before engineering picked up the work.

Support could get early root-cause analysis and suggested workarounds without waiting for a developer.

Engineers could connect Cursor, Claude Code, and Codex to grounded context instead of asking the model to crawl the whole codebase.

Evidence basis

Built from a real enterprise deployment, anonymized for confidentiality.

The source deck frames the core implementation as a four-layer system: backend ingestion, MCP bridge, containerized agent runtime, and UI or automation triggers. Exact client names, market names, private system identifiers, internal ticket IDs, and sensitive implementation details have been removed.

Source reference: Slides 1-8 from the internal case-study deck.