AI-Accelerated SDLC, Proven with 250 Developers, Ready for Your Team
We convert engineering teams to full agentic development. Autonomous agents produce the majority of code, while humans operate at the system and architectural level. We design and deploy multi-agent delivery architectures and drive measurable, multi-x throughput gains across the SDLC.
AI Reshaped the Industry, and This is No Longer Hype
Big tech companies are accelerating releases, team structures are changing, and traditional roles in engineering and delivery are being redefined. Organizations that adopt AI systematically in their development processes are gaining a long-term competitive advantage.
We have gone through this transformation ourselves:
- -AI-first tooling — Since early 2025, we have rolled out Cursor and Claude Code across all roles: developers, QA engineers, analysts, and managers.
- -Agent-driven code — Two thirds of our production code is created with AI agents, based on Claude Code and Antigravity, excluding autocomplete.
Now we bring this experience to your team.
Why AI Tools Do Not Work “Out of the Box” and Require Consulting
Giving developers access to tools like Cursor is not enough. Without methodology, training, and changes to development processes, most teams continue working the same way as before. Effective AI adoption requires a structured, end-to-end approach.
In practice, AI adoption follows the innovation diffusion curve:
- 2.5% —innovators who will explore AI independently
- 13.5% —early adopters
- 70% —the majority who need structured guidance
- 15% —require focused, hands-on support
Most engineering organizations fall into the middle 70%.
Without a systematic approach, AI adoption typically results in:
- Fragmented and inconsistent use of AI tools
- No measurable impact on delivery speed or quality
- Growing resistance inside teams
- License costs without return on investment
This is why successful AI adoption is an organizational transformation, not a tooling rollout.
How Leading Teams Are Integrating AI into Development
A one-month AI rollout designed to deliver measurable gains across speed, quality, and delivery. Covers all key areas of development transformation, from architecture to execution.
Ready to accelerate delivery 2x–4x with AI?
Let’s tailor our AI methodology to help your team deliver faster.
How to Use AI Without Compromising Security or Compliance
Security is a top priority for large organizations. Here is how we enable AI adoption without compromising your security requirements.
Option 1. Using Cloud-Based Models
Within Globalbit, we work with Cursor and models from Anthropic, OpenAI, and Gemini. Many organizations allow this approach under defined conditions:
- -Across all development, if security policies permit
- -For a limited set of projects, for example frontend only
- -For non-critical tasks such as prototyping or internal tools
Option 2. Deployment in a Closed Environment
When cloud models are not acceptable, we deploy AI tools inside your infrastructure:
- -AI code review fully on-premise
- -Test case automation where AI receives only requirements and UI access, source code is never shared
- -No external data transfer outside your environment
We'll help you get Agentic Development approved by your CISO
We help your teams align AI adoption with internal security requirements:
- -Explain how AI tools work in practice, including data flow and controls
- -Prepare clear technical and security justification for internal approval
- -Propose compromise scenarios that balance speed and compliance
What You Can Expect: Proven Results and a Projection for Your Team
Real results achieved in our own processes, and what they translate to for a team of your scale.
Team Distribution:
Median developer
- 80% to 90% of merged code is written by agents.
- 5 to 15 agent-generated commits per developer per day.
- Human role focuses on task framing, acceptance criteria, and PR review.
Top 15% agentic leaders
- 95% to 98% of merged code is agent-written.
- 20 to 50 commits per day coordinated across multiple parallel agents.
- Humans act as system architects, reviewers, and final decision makers.
Bottom 15% requiring intervention
- 50% to 65% of code is agent-generated.
- Bottlenecks are usually prompt quality, unclear task decomposition, or review latency.
- Targeted fixes include workflow restructuring, agent orchestration templates, and review heuristics.
Projected Impact for a 200-Person Team
What This Means in Financial Terms
For a team of 200 developers with an average payroll of $120,000 per year, 4x throughput is equivalent to the output of 600 additional developers. This represents approximately $72M per year in value. ROI of consulting services: 500x-1000x in the first year.
Ready to accelerate delivery 2x–4x with AI?
Let’s tailor our AI methodology to help your team deliver faster.
[ Process ]How AI Adoption Works: From Kickoff to Measurable Results
From the first meeting to measurable results in 4 weeks, with up to 12 months of ongoing support. AI adoption in software development is delivered through a structured, multi-stage process:
How AI Adoption Works: From Kickoff to Measurable Results
From the first meeting to measurable results in 4 weeks, with up to 12 months of ongoing support. AI adoption in software development is delivered through a structured, multi-stage process: