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AI-Assisted SDLC Consulting

Your team has AI tools. They are not producing the productivity gains you were sold.

We identify where AI tooling genuinely accelerates your software development lifecycle — and where it is adding noise, false confidence, or technical debt. A practical assessment and integration approach grounded in your actual codebase and team workflow.

The situation

Everyone has adopted AI tools. Almost no one has evaluated whether they are working.

Developers have GitHub Copilot, Cursor, or equivalent. Managers have reported productivity gains. The actual delivery metrics have not changed. The gap between the tool's potential and the team's outcome is in the integration — not the technology.

You are dealing with

AI tools adopted individually, without a coherent integration into the development workflow. Productivity claims that cannot be verified against delivery data. Uncertainty about where AI genuinely accelerates work and where it creates confident-looking code that requires expensive review.

What you need

A clear picture of where AI tooling produces real gains in your specific workflow — code generation, review, testing, documentation — and where the overhead of managing its output exceeds the benefit.

What you cannot afford

Shipping AI-generated code that the team cannot maintain. Replacing human review with AI confidence that misses the subtle context that experienced engineers catch. Building a delivery system that depends on tools the team does not understand well enough to override.

What AI-SDLC consulting delivers

A structured integration of AI tooling into your development workflow — specific use cases, team-level practices, code review protocols, and the guardrails that prevent AI assistance from becoming AI dependency.

Consulting approach

Assess where AI fits. Integrate where it genuinely helps. Govern what it produces.

We start with your actual workflow and codebase — not with a preferred tool. The integration is designed around where AI produces measurable gains, not where it looks impressive in a demo.

Step 1
Step 2
Step 3

Phase 01

AI readiness assessment

Review of current AI tool adoption, codebase suitability, team capability, and delivery workflow. We identify the specific points in your SDLC where AI assistance produces measurable gain — and the points where it is producing overhead or risk that is not being tracked.

3–5 business days · Fixed scope

Phase 02

Integration design

A structured AI integration protocol for your team — specific tooling recommendations, use-case guidelines, code review standards for AI-generated code, and the training approach that gets the team using the tools effectively rather than inconsistently.

2–4 weeks · Collaborative

Phase 03

Rollout and measurement

Structured rollout with delivery data tracked. Baseline established before integration; outcome measured after two sprint cycles. Adjustment where the data shows the integration is not producing the expected gain. Handover with documented practices and governance protocols.

4–8 weeks · Decreasing involvement

What you receive

AI integration that shows up in delivery data, not just developer satisfaction.

  1. 01 / 04

    AI readiness report

    An honest assessment of where your codebase, team capability, and delivery workflow are ready for AI integration — and where gaps need to be closed first. Use-case prioritisation by expected gain and implementation complexity.

  2. 02 / 04

    AI integration protocol

    Team-level practices for AI tool use — which tools for which tasks, prompt engineering standards for your codebase context, code review standards for AI-generated output, and the escalation path when AI assistance produces uncertain results.

  3. 03 / 04

    Governance framework

    The policies that prevent AI assistance from becoming unmanaged dependency — code attribution standards, AI-generated code review requirements, security review protocol for AI-suggested dependencies, and the ownership model for maintaining AI-generated components.

  4. 04 / 04

    Outcome measurement

    Before-and-after delivery metrics — cycle time, review overhead, defect rate, and test coverage — tracked through the integration period to produce evidence of actual productivity gain, not claimed productivity gain.

What if AI tooling is not right for our team or codebase at this stage?

We will say so. Some codebases — high security sensitivity, unusual language combinations, domain-specific logic — are not well-served by current AI coding tools. Some teams need foundational development practices in place before AI assistance amplifies output rather than amplifying inconsistency. The assessment is honest about both.

Case studies

Case 1

Identified up to $220k/month in delivery-related revenue leakage

A software client had active development, ongoing releases, and a long-term product relationship, but delivery problems were starting to threaten the commercial value of the account.

Critical bugs, piecemeal releases, and work not tied to business objectives were creating a growing cost of poor quality. We built a business case around what poor delivery was costing and what fixing it could unlock.

Impact:

  • Estimated delivery-related revenue leakage: $50k-$220k/month
  • Broader potential monthly impact modeled around $315k
Read the full case study
Case 2

Turned a small efficiency request into a $4.19M self-funding roadmap

A modest time-saving ask uncovered a far larger structural constraint sitting underneath the day-to-day work.

We mapped the real opportunity and built a phased roadmap that pays for itself as it is delivered, rather than a single large bet.

Impact:

  • Modeled value of the phased roadmap: $4.19M
  • Each phase funds the next
Read the full case study
Case 3

Turned a routine review into a €1.05M resilience business case

A German funding intermediary with a well-built stack and a lean 24-person team had one systemic constraint: no tested recovery layer across eight interconnected systems — one WordPress database as the single source of truth for all of them.

A modest environment review uncovered €508K–€1.05M in modelled annual risk, unpriced and invisible until the numbers landed on paper. Reliability had quietly become the product.

Impact:

  • Modelled expected annual loss, quantified and phased out: €508K–€1.05M
  • Designed recovery time, from no tested restore: < 4 hrs
Read the full case study

Common questions

What executives ask before engaging.

What is AI-assisted SDLC?+

AI-assisted SDLC is the practice of integrating AI tools into the software development lifecycle — from requirements analysis through code generation, testing, code review, and deployment. The goal is measurable productivity gains in delivery speed and quality. Without deliberate integration into existing workflows, AI tool adoption rarely produces its claimed benefits.

How do you integrate AI into the software development lifecycle?+

Effective AI integration follows a workflow-first approach: identify where the team spends the most time and which steps have the highest error rate, then fit AI assistance to the actual task structure. Tool selection follows diagnosis. Common integration points are requirements drafting, code generation review, test case generation, and incident post-mortems.

What are the benefits of using AI in the SDLC?+

When properly integrated, AI in the SDLC reduces time spent on routine code review, accelerates test coverage generation, speeds documentation, and shortens the requirements-to-prototype cycle. The most reliable benefit is not raw speed — it is the reduction of context-switching for engineers who would otherwise shift between tasks.

Which AI tools are used in software development?+

Common categories: code generation assistants (GitHub Copilot, Cursor, Codeium), AI-enhanced code review (CodeRabbit, SonarQube with AI), test generation (Diffblue, CodiumAI), requirements analysis (LLM integrations), and AI-powered observability. Tool selection matters less than how tools are integrated into the delivery workflow — adoption without integration rarely improves delivery metrics.

Which AI tools do you support?+

We work with the major coding AI platforms — GitHub Copilot, Cursor, Cline, Claude Code, and others — as well as AI-assisted testing, documentation, and code review tools. The assessment is tool-agnostic; we recommend specific tooling based on your workflow and codebase, not vendor preference.

What is the risk of AI tooling introducing security vulnerabilities?+

Real, and not consistently managed by most teams adopting AI coding tools today. AI models suggest code patterns and dependencies without security context. The governance framework we produce includes security review standards for AI-generated code, specifically covering dependency risks, injection patterns, and the authentication and authorisation code that is most dangerous to get wrong.

Fixed-scope first step

AI Readiness Assessment

Three to five business days. A written assessment of where AI tooling fits your workflow and codebase — use-case prioritisation, tooling recommendation, and integration approach.

The assessment covers

  • Current AI tool adoption — what is in use, how consistently, and what the team reports
  • Codebase suitability — language, complexity, domain sensitivity, and test coverage
  • Workflow integration points — where AI genuinely accelerates vs where it adds review overhead
  • Security and IP risk — data handling policies, code attribution, dependency risk
  • Written use-case prioritisation and integration protocol recommendation

3–5 business days · Remote · Fixed fee

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