Applied AI Engineering for Established Software

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 Cloud Vertex AI, so proprietary code never leaves your infrastructure. Access is controlled at the organization, repository, team, and user level, and every engagement carries $3M combined liability (cyber, data and professional) coverage, active from day one.

Through the Layer — 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 cost and return per shipped feature, sprint by sprint. Clients report 30–40% lower engineering costs. Delivery speed is measured per client against their own baseline, tracked sprint by sprint in the Layer, rather than quoted as a multiplier.

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.

Every engagement starts with the Benchmark: a fixed fee for one month, with the AI Intelligence Layer deployed inside your VPC and your baseline delivered at the end of it. The fee is credited in full toward your first month of delivery. If you stop there, you keep the baseline.