A clear breakdown of what Brainforge fixes, how fast, and what it actually delivers.
Assistant rollout
Deploy AI coding assistants across your team with setup, onboarding, and repo-aware context.
AI coding assistants your whole team ships with
AI coding assistants are only as good as their setup. Brainforge deploys and governs coding assistants — from IDE tools to terminal agents — with the context, guardrails, and review loops that turn them into daily leverage.
In plain terms
What AI Coding Assistant means in practice
Where teams get stuck
Deploy and govern AI coding assistants with the setup, guardrails, and review loops that make them productive.
Coding assistants are installed but only a few developers use them.
We can't tell what's AI-generated or whether it's safe to merge.
No shared setup, so every engineer reinvents the tool.
Agents skip tests, review, and security patterns.
What we deliver
Core AI Coding Assistant
Guardrails and governance
Permissions, review integration, and policies so assistant output is safe to merge.
Team enablement
Playbooks and usage patterns your engineers extend after rollout.
How deep it goes
Capabilities behind the work
The same delivery primitives (context, controls, and review) show up across every engagement.
Assistant deployment
Rollout of IDE and terminal coding assistants with repo-aware setup.
IDE assistantsTerminal agentsRepo-aware setup
Context and standards
Repo conventions and operating rules wired into assistant behavior.
Context wiringCoding standardsSkill setup
Guardrails and review
Permissions, sandboxing, and review integration so output is safe.
Scoped permissionsReview gatesSecurity patterns
Enablement
Playbooks and usage patterns the team extends after rollout.
PlaybooksOnboardingUsage patterns
Evaluation
Quality checks on assistant output so gains are measured.
Code evalsQuality reviewRegression checks
What changes
Outcomes you can point to
- Coding assistants deployed with shared context and standards.
- Guardrails and review gates on assistant output.
- Playbooks the whole team uses.
- Measurable adoption and code quality.
Proof in production
See where this work shows up.
Related ways to engage
A clear front door into this work
Common questions
AI Coding Assistant, straight answers
What does AI coding assistant deployment cost?
Engagements start with a scoped rollout sprint, so you pay for a bounded piece of work rather than an open-ended retainer. Most teams begin with one pilot team, then expand once guardrails are proven.
Which coding assistants do you deploy?
IDE assistants and terminal agents — including Cursor, Claude Code, Codex, and OpenCode — standardized to your team's stack and security posture.
How do you keep AI-generated code safe?
Guardrails are the core: scoped permissions, review integration, and evals so assistant output goes through the same quality gates as human code.
Do you train our engineers?
Yes. Rollout includes onboarding, playbooks, and usage patterns, and we hand over the operating model so the team extends it.
How long does deployment take?
A first deployment with context, guardrails, and enablement typically ships in 2–4 weeks of sprint work.
How we work
A path from pressure to a working system
01
Audit current AI coding usage
We see which tools and patterns work, which stall, and where agents create risk.
02
Roll out with guardrails
We deploy the assistants, context, permissions, review gates, and playbooks.
03
Enable and measure
We onboard the team, wire in evals, and iterate from real usage.
Our Trusted Partners
We only bring the best of the best
READY TO PUT
AI Coding Assistant to work?
AI Coding Assistant to work?
In one working session we'll name what's broken, what's possible, and the first system worth building.
