
Claude in Production
Get Claude shipping real features inside your codebase, safely, in the right order, so nothing risky touches core code first. We forward-deploy senior Claude Certified Architects who modernize, maintain, and build your product at AI speed. You get:
- Claude Certified Architect: senior, production-proven, top 4% vetting pass rate
- Agentic SDLC: one disciplined, Claude-driven process from day one
- Secure AI Development Environment: your code in your own cloud, zero IP exposure
- AI Development Intelligence Layer: board-ready proof of what actually ship.
The board wants AI. Your codebase, your team, and your proof are not ready for it.

The team is the real constraint
Two failure modes at once. Some engineers resist AI and keep working the old way. Others accept whatever Claude generates without review. Add turnover and hoarded context, and there is no shared standard for how the team ships with AI.

The codebase is the bottleneck
Your platform is business-critical and years deep. Brittle, thinly tested, full of code no one fully remembers. Point Claude at it cold and it makes wrong assumptions. It cannot tell dead code from load-bearing code, so one wrong change can break production.

No proof, and no proof the spend is working
The board committed an AI budget and wants to see velocity. You also have to show the money is working: what the AI usage is actually producing. Without hard, sprint-by-sprint signals, both are guesswork.

IP & Compliance Exposure
Shadow AI and fragmented toolchains expose proprietary codebase data. Deploying without an isolated environment breaks enterprise compliance.
Safe AI adoption, in the right order
One Architect, three contexts, always in this order. First we make the platform safe to change, then we put Claude into the daily work, then we ship AI into the product. The order is the safety: skip the first steps and acceleration outruns stability.
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Modernize — make it safe to change. Claude maps and documents the system and builds the tests it never had. Nothing risky touches core code first.
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Maintain — put Claude in the daily work. Claude writes, tests, and reviews under engineer oversight. Bug load and tech debt fall.
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Build — ship AI into the product. The growth the board wants, reliable only on a stable platform. Skipping the first two is why AI initiatives stall.
Everything it takes to put Claude into production. Safely.
Established software companies putting Claude into production
Operational and Security Boundaries
How It Works
Everything you need to ship at 10x+ velocity: engineers, workflow, observability, and security
— deployed as one governed system in 4 steps.
Contact Us
Submit your information to schedule a technical briefing. We will walk you through the 4x Applied AI Engineering model and our under 4-week embedding process.
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FAQ
Agentic SDLC (Agentic Software Development Lifecycle) is the development methodology GoGloby deploys across every engineering team. Instead of every engineer using AI tools differently, it establishes one consistent process: specs before code, AI output reviewed before it ships, autonomous agents integrated into CI/CD pipelines. The result is predictable output, no ungoverned AI usage, and a team that works the same way every sprint.
A forward-deployed engineer works inside your team, your sprints, your tools, and your codebase, not at arm’s length. Staff augmentation gives you headcount to manage and a seat to fill. An agency delivers a project outside your team as a black box. A forward-deployed Claude Certified Architect is different on both counts: they bring a governed method, a secure environment, and board-ready proof, and they transfer the capability to your own engineers, so you end up owning the result instead of depending on a vendor.
Yes, and the rewrite is exactly what you avoid. The work runs in one order. First make the platform safe to change: use Claude to map the system, document it, and build the automated tests and clean build it never had. Nothing risky touches core code until that safety net is in place. Only then does Claude go into daily work and, later, into new product features. You accelerate on the platform you already have, rather than betting the business on rebuilding it.
Your code stays in your own environment. Your team works in Claude Enterprise, which is governed and never used to train models. Your codebase runs on Claude on your own cloud, through AWS, Amazon Bedrock, or Google Vertex, so proprietary code never leaves your infrastructure. Access is controlled at the organization, repository, team, and user level, and every engagement carries $3M cyber and data liability coverage, active from day one.
Through the Performance Center — telemetry that tracks hard signals every sprint: AI Contribution Ratio, velocity acceleration, and Agentic AI commit rate. No code access required, metadata only. You get board-ready proof of velocity gains sprint by sprint. GoGloby clients report 4x engineering velocity and 30–40% lower engineering costs within the first two sprints.
Through a standardized Agentic SDLC that the Architect installs across the team from day one. It sets one process everyone follows: a spec before code, Claude generating under that spec, and human review before every merge. The rules are enforced consistently across repositories, teams, and users, which brings both the engineers who resist AI and the ones who over-trust it to the same standard. The result is predictable output instead of silent drift.
Look for production-proven engineers rather than AI hobbyists, and ask whether they make the platform safe to change before they accelerate on it. Confirm the code stays in your environment, that there is contractual liability behind that claim, and that a performance guarantee is in writing. Ask how they prove results to a board, and whether the engagement transfers capability to your own team or keeps you dependent. A trustworthy partner leads with safety and proof, not speed alone.













