AI Adoption & Team Enablement
Most AI adoption stalls not because the tools are weak, but because the rollout is ad hoc. Individual engineers experimenting in isolation, no governance, no measurement, no shared playbook. We move teams from experimentation to operational practice in 4-8 weeks.
AI-assisted first-pass review routinely drops engineer review time from 2.5 days to under 4 hours.
Standard adoption windows. Longer if governance is stricter, shorter if tooling is already in place.
Governance is wired in from day one, so enterprise audits don't surface AI-specific findings.
Assessment
- Engineering workflow and delivery pattern review
- AI-tool inventory and shadow-adoption mapping
- Skill and confidence survey across the team
- Risk and compliance posture check
- Written readiness assessment with prioritised recommendations
Rollout plan
- Phased adoption roadmap (quick-wins to complex workflows)
- Tooling selection (Cursor, Claude Code, Copilot, Codex, Cline, etc.)
- Corporate-plan migration from personal AI accounts
- Pilot scope and success criteria
- Outcome-measurement framework
Enablement
- Hands-on training embedded in real work (not abstract workshops)
- Pair sessions during sprints to build muscle memory
- Internal champions network and reusable patterns
- Prompt libraries and review heuristics for your codebase
Operationalisation
- Access controls, audit trails, and escalation paths
- AI PR-review configuration with human escalation
- Measurement dashboard for usage, quality, and outcomes
- Internal documentation and onboarding for new joiners
SDLC transformation is sequential. Each phase builds on the previous. The order matters: agentic development without process and test foundations accumulates technical debt instead of creating leverage. Each phase adds a layer of safety that makes the next level of automation viable rather than reckless.
- 01
Phase 01 · Process Foundations
~2-3 monthsEstablish the baseline that all later automation depends on. Branch protection, review/merge PR cycle, checklist validation, and calibration of dev-machine AI tools. Formatters (dotnet format, linters), code analysers (ESLint, dotnet CLI), database and storage verification, security tooling. All integrated into the CI/CD pipeline as automatic quality gates.
- Branch protection and PR-cycle policy
- Formatter, linter, and analyser integration in CI
- AI-assisted code generation (first measurable quick-win, typically 4-6 weeks in)
- Automated AI code review in the CI pipeline
Enables → Phase 2. Tests can be run automatically and trusted.
- 02
Phase 02 · Test Automation
~2-3 monthsUnit and integration test revision. Security, backward-compatibility, and performance test integration. Team calibration on generating tests faster with AI tools. Without test coverage, engineers cannot run agents with confidence: generated code can silently modify shared modules and introduce bugs that only surface in production.
- Unit and integration test coverage uplift
- Security, compatibility, and performance tests in CI
- AI-assisted test generation patterns for the codebase
- Baseline quality metrics for agent rollout
Enables → Phase 3. Agents can be trusted to modify code with guardrails in place.
- 03
Phase 03 · Local Agentic Development
~2-3 monthsFull development-cycle automation on a single developer machine: agents that write code, run tests, and iterate. Requires Phases 1-2. Agents need branch policies, linters, analysers, and tests as guardrails to produce reliable output rather than break shared code.
- IDE-integrated coding agents configured per repository
- Agent-driven test and refactor workflows
- Human-in-the-loop escalation patterns
- Per-engineer productivity baseline and measurement
Enables → Phase 4. Organisation-wide autonomous workflows with the proven patterns.
- 04
Phase 04 · Autonomous Agents & Process Integration
~2-3 monthsAutonomous agents running on virtual development environments. Integration of BA, QA, and UI generative tools into the process via API, MCP, or other patterns. Optional additional targets: tools to support client-specific processes, RAG systems, product-level generative AI features.
- Autonomous agents on ephemeral / virtual dev environments
- BA, QA, and UI tool integration (API / MCP)
- Cross-role workflow automation (spec → code → test → review)
- Portfolio-level quality and cost monitoring
Diagnostic
Pressure-test your current AI adoption approach. Identify the two or three gaps most worth closing first. No slides, no salespeople.
Hands-on Demo
Walk through an agentic SDLC setup in a real repo, showing what each phase looks like in practice, from branch protection through autonomous agents. No commitment, no slideware.
Sprint
Hands-on engagement. Readiness assessment, rollout plan, embedded enablement, and governance wired in before the pilot goes live.
Embedded Retainer
A dedicated senior consultant works alongside 1-2 of your engineers under your engineering leadership, not as an external advisor. Monthly time-and-materials, typical baseline ~35 hrs/week. 12-month shapes common; shorter by mutual agreement.
Best fit
- Software companies where AI usage is currently ad hoc or shadow
- Engineering teams of 10-200 moving from experimentation to practice
- Organisations with enterprise or regulated customers asking about AI
- Teams that want governance baked in from day one
Not a fit
- Teams looking for strategy decks without implementation support
- Organisations who want external staff to do AI work for their engineers
- Research-only projects with no production use case
Engineering AI Adoption
B2B SaaS · Series B · 15-person engineering team · APAC
A software company wanted to adopt AI across engineering in a practical way, but needed the right workflows, training, governance, and rollout model to make it useful and compliant.
Read case studyAI product architectureHoverBot
AI-native chatbot platform
The client needed a production-grade AI platform that could support configurable chatbots, knowledge-grounded responses, and safe enterprise-friendly workflows.
Read case studyDomain-specific conversational AILabCaddy
Scientific product platform
The client needed a more intelligent way for users to discover science-related products and interact with product information through conversation, not just keyword filtering.
Read case study01How is this different from an AI training course?
Training courses teach general tool usage. We embed with your team in their real codebase, using your real workflows, with your real constraints. Engineers leave with muscle memory on your code, not a certificate on a generic tool.
02What tools do you recommend?
We are tool-agnostic and pick based on your constraints. Common stacks: Cursor or Claude Code for IDE-native development, Copilot Enterprise for governed organisations, Codex or Cline for autonomous workflows, and governed LLM proxies (OpenAI Enterprise, Anthropic, Bedrock) for data-sensitive contexts. We help you compare and decide.
03Can you help move engineers off personal AI accounts?
Yes. This is one of the most common early moves. Personal accounts give you zero visibility, no data protections, and no audit trail. We help migrate teams to corporate plans with centralised logging, data-processing agreements, and admin controls, then retire the personal accounts without disrupting day-to-day work.
04How do you measure success?
We set the measurement framework during the assessment. Typical metrics: PR cycle time, defect rate, engineer-reported confidence and friction, tool usage depth, and compliance readiness. We track against a baseline taken before the rollout begins, and review at the end of each pilot.