AI for engineering
Make your engineers AI-native — and ship faster.
Your developers are already coding with AI. Reframe turns that into an advantage: the process, methods, and tooling to roll out AI-assisted development across your teams — raising velocity while quality, security, and review keep pace.
The velocity gap
AI is in your IDEs already. Few orgs have made it a system.
A few power users, not a team
Some engineers are 2× faster with agents; most haven’t changed how they work.
AI code with no guardrails
Generated code is merging without the evals, review, and security gates it needs.
No line to outcomes
Leadership can’t see whether AI is actually moving throughput, quality, or cost.
The process
From scattered usage to an engineering platform.
Baseline
Measure how teams build today — velocity, quality, where time goes.
Enable
Roll out coding agents with playbooks, guardrails, and training.
Platform
Golden paths, shared tools, and evals so good practice is the default.
Scale
Spread across teams with metrics leadership can actually read.
The methods
How AI-native engineering is built — safely.
AI pair-programming playbooks
Concrete patterns for using agents on real work — not demos.
Secure code-gen guardrails
Policy on what AI can touch, and controls that keep secrets and IP safe.
Eval & review gates
Automated checks and human review that keep up with AI-speed code.
Golden paths
Paved roads so the safe, fast way is the easy way.
The tooling
The platform that makes velocity repeatable.
Coding-agent rollout
Standardized setup, prompts, and scopes for agentic development.
Pipeline guardrails
Evals, security scans, and policy checks gating AI-generated changes.
Evaluation harness
Regression and quality evals so speed never costs correctness.
Internal tool & MCP catalog
Reusable tools and context that make every agent more capable.
Velocity dashboards
Throughput, quality, and adoption — the numbers leadership asks for.