BEYOND AI ASSISTANTS

Engineering Velocity,
Re-Engineered.

Stop buying AI licenses and hoping for productivity. I provide onsite consultancy to transform your development team into an AI-native powerhouse—standardizing workflows, securing your IP, and automating the SDLC.

The 2026 Developer Bottleneck

Tool Fatigue

Teams are juggling fragmented AI tools without a unified strategy.

Review Latency

AI is generating code faster than your senior engineers can safely review it.

Context Blindness

Generic LLMs don't understand your unique architecture or legacy constraints.

Security Risk

Ad-hoc AI usage leads to proprietary logic leaking into public models.

Two Strategic Paths

Package A: The AI Jumpstart

Foundation & Security Alignment

Duration: Half-Day Onsite Intensive
Focus: Standardizing the team's baseline and eliminating security risks.

Key Modules

  • The Golden Toolstack: Selecting and configuring the right IDE extensions and LLMs (Cursor, Copilot, Local Models).
  • Proprietary Guardrails: Implementing zero-leakage protocols and organizational AI policies.
  • Contextual Prompting: Moving senior devs from "Basic Chat" to "Contextual Reasoning" and project-aware prompting.

Package B: The AI-Native SDLC

Workflow Re-Engineering

Duration: Full-Day Deep Dive
Focus: Automating the "Non-Coding" parts of the Software Development Life Cycle.

Key Modules

  • Agentic PR Reviews: Deploying AI agents to handle first-pass code reviews for logic, style, and security.
  • Automated Test Orchestration: Moving from manual unit tests to AI-generated edge-case and regression suites.
  • Living Documentation: Automating the synchronization of READMEs, API specs, and technical debt logs.
  • Custom RAG Pipelines: Building internal knowledge assistants that "know" your codebase.

Technical Syllabus

Context Injection

Strategies for feeding long-range codebase knowledge into LLMs without token bloat.

In-IDE Governance

Implementing .cursorrules and system prompts to enforce architectural standards automatically.

Model Orchestration

When to use Reasoning Models vs. Fast Models vs. Local LLMs (Llama 3/4) for cost and speed optimization.

The Outcome

Predictable High-Performance Engineering

By the end of the engagement, your team will have a unified roadmap, reduced cycle time, and future-proof governance.

A Unified Roadmap

A clear, documented strategy for AI integration over the next 12 months.

Reduced Cycle Time

Measurable reduction in "Time to First PR" and "Review Latency."

Future-Proof Governance

A framework for safely adopting new AI agents as they emerge.

Ready to scale your output without scaling your headcount?

Request Workshop SyllabusBook a 15-minute Strategy Call →