Darner
Darner Technology builds Garth, an engineering intelligence platform that helps engineering organizations measure, anticipate, govern, and secure AI-assisted development work. The platform is built around GKS, a unified knowledge graph that connects repos, tickets, pipelines, and spend to provide consistent answers about AI investment ROI, delivery risk, and code safety. Garth is designed specifically for the AI-assisted Development Lifecycle rather than retrofitting tools built before AI wrote code.
- Artificial Intelligence
- AI Agents
- Data & Analytics
- Developer Tools
- Enterprise Software
- Software Only
Funding
Founders
Product
Problem
AI now writes a large share of the code that ships, yet engineering organizations lack reliable ways to measure what their AI investment returned, anticipate delivery slips before they happen, and govern the code AI produces before it reaches customers. Traditional software development metrics and tools were designed before AI-assisted coding, leaving teams without visibility into the new risks and returns introduced by AI-generated code.
Solution
Darner Technology provides Garth, an engineering intelligence platform purpose-built for the AI-assisted Development Lifecycle. Garth answers three core questions: what the AI investment returned, whether promised work will land on schedule, and whether AI-written code is safe to merge. Every Garth product reads from GKS, the Garth Knowledge System, a unified graph of repositories, tickets, pipelines, and spend that ensures the three answers never contradict each other. The platform offers three product doors that customers can adopt individually and expand as the numbers justify, all backed by a single knowledge graph for consistent, reliable intelligence.
Target Audience
Engineering organizations ranging from startups to Fortune 500 companies that have adopted AI-assisted development and need to measure ROI, manage delivery risk, and govern AI-generated code.
Features
- GKS knowledge graph unifying repos, tickets, pipelines, and spend into one consistent data model
- ROI measurement tools that quantify the return on AI-assisted engineering investments
- Delivery risk anticipation features that flag slips before committed dates move
- Code governance capabilities that assess whether AI-generated code is safe to merge
- Modular product architecture allowing teams to start with one product and expand as value is demonstrated
- Built specifically for the AI-assisted Development Lifecycle rather than retrofitting pre-AI tools