Building an AI-Native Engineering Platform
How we integrated Claude Code, local LLMs, and retrieval-augmented generation into daily engineering workflows at Edgecast Cloud (Sovren Technologies) — and what actually worked in production.
When I joined Edgecast as CTO, one of my first priorities was transforming how engineering worked with AI. Not adding an AI feature to the product — rethinking how the engineering organization itself operates.
The Problem With Most AI Tooling Rollouts
Most enterprise AI adoption looks like this: someone buys a Copilot license, rolls it out, declares victory, and moves on. Engineers use it for autocomplete, maybe some basic generation, and that's the end of it. The ROI is real but modest.
We wanted something different: AI embedded at every stage of the engineering lifecycle. Architecture. Implementation. Review. Documentation. Testing. Incident response. Not as a nice-to-have, but as the default way we work.
What We Actually Built
The platform has three main layers:
1. Context Infrastructure The most underrated part of any AI deployment is context. An LLM is only as useful as what it knows about your system. We built a retrieval-augmented engineering knowledge system that indexes:
- Source code across all repositories
- Architecture decision records
- Runbooks and operational procedures
- RFCs and design documents
- Incident history
When an engineer asks a question or starts a task, the system surfaces relevant context automatically. Claude doesn't have to know our systems — it has access to our systems.
2. Standardized AI Development Environments Every engineer works inside the same AI-assisted environment. Claude Code is the default interface, with MCP servers connecting to our internal tools, documentation, and code analysis systems. Local LLMs (via Ollama) handle tasks that should stay on-prem.
This standardization matters. When everyone is using the same tools the same way, you can actually improve those tools. You build institutional knowledge about what works and what doesn't.
3. Agentic Workflows The highest-leverage work has been building actual agents — systems that take a task and drive it to completion across multiple steps:
- Architecture analysis agents that review proposed designs against existing systems and past decisions
- Implementation agents that generate code with full context about the surrounding codebase
- Code review agents that check for consistency, security, and adherence to our engineering standards
- Documentation agents that generate and maintain technical documentation automatically
- Incident response agents that correlate metrics, logs, and runbooks to accelerate investigation
What Works and What Doesn't
Works: Anything where the quality of output can be evaluated automatically or by someone with domain expertise. Code generation, documentation, test generation, architecture review — all solid.
Works: Retrieval-augmented generation for internal knowledge. Engineers stopped asking each other questions they could ask the system.
Doesn't work (yet): Fully autonomous agents for high-stakes decisions. We use agents heavily, but always with a human in the loop for consequential actions.
Doesn't work: Skipping the governance layer. We built explicit AI governance policies — what data can be sent where, how outputs should be reviewed, what decisions require human sign-off. Without that, you get shadow AI usage and security problems.
The Real Lesson
The biggest unlock wasn't any specific tool or model. It was standardization. When everyone is using the same environment, the same tools, the same workflows — you can actually improve the system. You can measure what's working. You can share techniques. You can build institutional knowledge.
AI tooling adoption that happens organically, without standardization, produces islands of expertise and a lot of wasted effort. Standardize first, then optimize.