AI in Software Engineering | Riverty Tech Nights

“As foundation models become increasingly commoditized, competitive advantage will come from how well an organization engineers context.”
— Jared Bowns, Forbes Technology Council
He’s right.

### The Challenge

In a typical enterprise, the tech landscape looks like a digital maze:

• Hundreds (sometimes thousands) of legacy systems in outdated languages
• A jungle of microservices spread across countless repositories
• Documentation scattered across SaaS tools
• Complex identity setups and cross-cutting business processes

Meanwhile, everyone’s already “using AI” — but in silos:
🧑‍💻 Developers rely on Copilot, Cursor, or Claude.
📋 Product teams live in Atlassian.
💬 Everyone chats with ChatGPT.
Yet the toughest questions remain slow — or nearly impossible — to answer:

• How does the payment flow really work across seven repositories?
• Who produces and consumes what in our event-driven architecture?
• Where is business logic duplicated across systems?

### The Riverty Approach

At Riverty, we’re changing that.
We’ve parsed ~300 of our 1,500 repositories into a knowledge graph that gives LLMs the context they need to actually understand our systems.
On top of it, we’re building smart agents that use this graph to:

• Trace dependencies across services
• Answer architecture questions in seconds
• Accelerate safe, reliable change

The result? LLMs that are faster, cheaper, and smarter — because they finally know the context.