Configure Claude Code for your codebase, standards, and delivery model, with guardrails that match your teams.
AI Engineering adoption
Build an AI-native engineering team
Stop guessing the ROI of your AI tools. We configure the right stack, train your team to use it effectively, and measure the direct impact on delivery so your investment translates into real speed.
Why invest in AI enablement
Make AI an advantage,
not a subscription
Throwing tools at a team rarely creates a step-function increase in output. True acceleration happens when AI is embedded into your codebase, workflows, and daily engineering habits.
Standardize the workflow.
Give everyone a baseline setup that matches your stack and conventions instead of ad hoc configurations.
Measure the real ROI.
Move past anecdotal feedback to concrete data on how AI impacts code quality, speed, and delivery.
Upskill the entire team.
Turn casual tool users into AI-native engineers who know how to prompt, review, and leverage agents effectively.
Make your AI investment work
Tooling Setup
Give team a production-ready AI setup
We set up the AI tooling around your codebase, standards, and delivery process. The result is a setup engineers can use in real work from day one, without everyone inventing their own way of working.
Use Cases
Claude Code Rollout
Cursor & Codex Configuration
Tune Cursor, Codex, and related IDE assistants against your stack so suggestions match your conventions and code patterns.
MCP Configuration
Connect agents to internal tools, data sources, and developer systems with the right scopes and access, so integration stays safe and practical.
Agent Skills Development
Build reusable Agent Skills that encode your team's conventions, playbooks, and repeatable engineering work.
Developer Environment Setup
Build a reproducible AI-native developer environment so every engineer starts from the same baseline.
Adoption Measurement
Track the real ROI of your AI investment
AI adoption needs measurement, not assumptions. We track speed, quality, workflow bottlenecks, so teams can see what is working, improve how they use AI, and adjust the setup when the investment is not paying off.
Use Cases
Adoption Dashboard
Track AI adoption, usage quality, and delivery impact across teams in one live view.
Engineering Benchmarks
Compare your teams against internal baselines and benchmarks drawn from other Callstack client environments.
Prompt & Pattern Diagnostics
Surface prompting patterns, workflow bottlenecks, and failure modes that quietly reduce output quality.
AI ROI Measurement
Connect AI usage signals to delivery outcomes so leadership can see where the investment is paying off.
Quality Signals
Monitor code quality, review outcomes, and defect rates on AI-assisted work so velocity gains do not hide regressions.
Team Training
Turn your developers into AI-native engineers
New tools do not help much if the team is still guessing how to use them. We train engineers to work with AI and agents in a way that improves judgment, code quality, and output.
Use Cases
Readiness Assessment
Identify workflow gaps, team maturity, and the most practical path to AI-native engineering.
Developer Training
Hands-on training that moves engineers from ad hoc experimentation to deliberate, effective AI use.
AI Champions Program
Grow internal champions who can spread effective AI-native practices across teams.
Continuous Tuning Retainer
Improve tooling, workflows, and team habits continuously based on real usage signals and measurable outcomes.
Assess → Implement → Improve
Make AI part of everyday engineering.
Assess
We audit tooling, workflows, adoption, and ROI. Leadership gets a clear rollout plan.
See the baseline before rollout starts
Get a plan with cost and timing
1-2 WEEKS
Implement
We set up tools, agents, and standards. Pilot first, then roll out wider.
Set up tools, agents, and training
Prove it in a pilot, then scale
2-6 WEEKS
Improve
We keep the setup current as adoption grows. Your champions lead day to day.
Tune the workflow as usage grows
Keep pace with tools and models
ALWAYS ON
Open Source
Want to build it on your own?
We open-source the tools behind our delivery model.
Use them, fork them, or let us run them for you.
Insights
Worth your time, by engineers.
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Announcing Codex Plugins for React Native Development
Skills work well in isolation, but most React Native work spans multiple concerns at once. This post covers how plugins bundle related skills into a single installable package, and walks through two we're releasing today: one for React Native development, one for testing with device automation.
Announcing: React Native Best Practices for AI Agents
We’re publishing react-native-best-practices: a structured set of skills derived from The Ultimate Guide to React Native Optimization, designed for AI coding agents working on React Native and Expo apps. The repo covers practical techniques across JavaScript, native iOS/Android, and bundling. All contribute to the metrics that matter: FPS and TTI.
