ChatGPT, GitHub Copilot, Cursor — tools promising to transform how software is built. Clients are asking. Competitors are experimenting.
No guardrails. No measurement. No clear process. The result: inconsistent quality, security concerns, and velocity gains that don't materialize.
We ran an internal hackathon across 10 real-world projects to find the ground realities of the AI landscape — not vendor claims, not theory. Structured experiments with rigorous before/after measurement across 6 project types.
| Project Type | Velocity Gain | Why the Gain |
|---|---|---|
| Greenfield (New Projects) | 4x – 5x | Clean slate — no legacy constraints, maximum AI leverage |
| Application Modernization | 3x | AI excels at systematic, pattern-based migration tasks |
| API Optimization | 2x – 3x | Strong gains on structured, repeatable work |
| Application Logging & Monitoring | 2x – 3x | Repetitive instrumentation code benefits greatly |
| Brownfield (Legacy Codebases) | 1.5x – 2x | Gains still significant despite codebase complexity |
| Frontend Heavy Modules | 1.5x | UI nuance requires more human oversight and iteration |
Anthropic's agentic coding agent — large context reasoning, architecture decisions, greenfield projects.
IDE-integrated AI — codebase-aware suggestions, refactoring, day-to-day development workflow.
Google's agent-first IDE — autonomous planning, execution and validation across editor, terminal & browser.
OpenAI's code-focused model — evaluated for code completion, generation and transformation tasks.
AI generates code without long-term structure or ownership in mind.
AI confidently produces plausible but incorrect logic or APIs.
Over-generation adds bloat, unused features, and hidden complexity.
AI writes tests that pass but don't validate real business behaviour.
Root cause — AI is strongest at execution with clear constraints, and weakest at ambiguity, accountability, and context ownership.
Humans define the problem, constraints, and success criteria with precision.
Humans own architecture decisions; AI accelerates research and options.
AI executes within defined constraints; humans review and approve output.
Humans validate business behaviour; AI assists with coverage and regression.
Humans set direction and priorities; AI handles repetitive evolution tasks.
Result — Velocity from the power of AI. Control from Human in the Loop. That's what drives the 1.5x–5x gain.
Defined boundaries on what AI can and cannot do — restricted zones, mandatory review gates, and scope limits per project type.
Standardised guidelines for prompting, AI-assisted code review, and output validation — applied consistently across every engineer and project.
Pre-built, version-controlled skill files (e.g. skill.md) that every engineer loads into their agentic environment — so the AI agent always generates code within Tkxel's defined standards automatically, not by individual judgment.
Why it matters — Any team can use AI tools. Tkxel has built the institutional infrastructure to make AI development safe, repeatable, and enterprise-ready.
More story points per sprint — no new hires, no budget increase.
More features shipped per dollar invested in the engagement.
Stay ahead as agentic development becomes the industry standard.
Client purchases and manages both subscriptions — full control remains with you.
New features & greenfield projects. Superior at large context, architecture decisions, and building from scratch.
Enhancements, bug fixes & support. Deeply integrated with existing codebases — optimized for day-to-day workflow.
Set up Cursor rules and project-specific context. Feed domain knowledge and codebase to AI environment. Define AI usage boundaries. Establish coding best practices within tooling.
Capture baseline velocity (story points/sprint), bug rate, and test coverage. Establishes a clean before/after baseline to demonstrate measurable value.
Tkxel has a complete, structured training path for every engineer — covering hands-on workshops, practical exercises using your actual codebase, and a formal evaluation to certify readiness before any engineer works on an agentic project. Covers prompting best practices, AI-assisted code review, and test generation workflows.
The team focuses on setting project context, calibrating agentic workflows, and establishing the AI environment. A deliberate investment that sets up every sprint that follows.
Minimum 1.5x velocity gain, with continued improvement as agentic workflows deepen and the team builds fluency.
Unit tests generated by default. Measurable bug reduction. Faster PR review cycles — from day one.