AI SDLC Services

We focus on measurable delivery gains

Build faster, code cleaner, and improve product quality with AI integrated across your product development lifecycle—spec, design, engineering, QA, and release.

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We’ll explore your technology goals and challenges
You’ll get expert insights on the best path forward
We’ll outline next steps to bring your solution to life
FASTER CODE, but no change in feature velocity?

The whole software development life cycle rebuilt, on your own live backlog

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Built on a true baseline

Through Hypr (our enterprise-class development harness), we trace real features from idea to production, time every stage and handoff, and score your organization's development process on a six-dimension maturity model before touching anything, the same model whether it's run once or across a portfolio.

A group of business professionals collaborating in a lush urban park with towering skyscrapers in the background, symbolizing innovation and efficiency. The scene represents machine learning models optimizing business operations, as technology and sustainability converge to enhance productivity and decision-making in a modern corporate environment.

Built on your own live work

We rebuild life cycle stages on your actual backlog, shipping AI-generated code to production the whole time. Not a sandbox, not a pilot repo, and not a simulated environment—this rollout proves the most effective AI implementation strategy is spec-driven development with controlled adoption of specific use cases from the live backlog, focused on specific tasks rather than a broad, unbounded tool rollout.

A group of business professionals collaborating in a lush urban park with towering skyscrapers in the background, symbolizing innovation and efficiency. The scene represents machine learning models optimizing business operations, as technology and sustainability converge to enhance productivity and decision-making in a modern corporate environment.

In-flow training

Engineers, PMs, and designers learn the new system by running it on their own projects, seeing how AI enhances specific tasks in day-to-day delivery work, with certification built into the work. Teams leave with a clear mental model of where AI needs human input. The muscle memory gets built live, on your product.

A group of business professionals collaborating in a lush urban park with towering skyscrapers in the background, symbolizing innovation and efficiency. The scene represents machine learning models optimizing business operations, as technology and sustainability converge to enhance productivity and decision-making in a modern corporate environment.

Built for in-house ownership

FullStack leads the first builds, then co-leads, then coaches from the side and steps back, always with human oversight and human validation before anything ships. You exit with the playbooks, agent configs, translated code, and summarized documentation in your handoff materials, plus a champion network to support knowledge transfer and onboard the next team without us.

We'll trace three real features from idea to production and time every handoff.
You'll get a maturity score across six dimensions and a locked baseline, comparable across teams or across a portfolio.
We'll name the three lifecycle stages costing you the most.
Case Studies

Our client impact in action

Extra Duty Solutions—full transformation

A six-week cohort with Hypr deployed against their repos and CI/CD pipelines, training and certification in-flow, and a before-and-after outcome report at close. The closest structural match to the full AI SDLC engagement and the strongest documented proof.

Rapid modernization and quality code

Paciolan partnered with FullStack to roll out AI-native workflows across its entire engineering organization, freeing teams from repetitive migration work so they could focus on code quality and complex problem-solving. Modernization timelines improved more than 30%.

Bradford Airport Logistics—embedded applied AI engineer

An eight-to-ten week part-time embed that produced a written operating model and shipped AI coding agents, the best proof of the embedded motion and of the expansion pattern this offering is built on.

testimonials

What our clients are saying

FullStack’s deep understanding of BenjaminWest’s needs, coupled with consistent updates, made the collaboration seamless and the outcome outstanding.
Joe Eikelberner, COO
BenjaminWest
FullStack acted as true partners and advisors. The expertise around AI and the level of developers, engineers—whatever role it was that came to the table—was just phenomenal.
Marisa Kopec, CEO
Lux Research
Speed is only the byproduct; the real value is better software and better use of our people.
Raj Tatta, VP of Engineering
Paciolan
FullStack turned our vision for The Launchpad into reality. Their intuitive design approach delivered an app that provides IT buyers a seamless and hassle-free experience, effortlessly connecting them with the ideal tech vendors.
Tonya Turrell, Founder & CEO
Technology Match
FullStack completely transformed our company's app, breathing new life into how we service our customer base. Their innovative and collaborative team delivered an application experience that we're proud to have in the market!
Jay Williams, Software Manager
Green Mountain Power
FullStack’s deep understanding of BenjaminWest’s needs, coupled with consistent updates, made the collaboration seamless and the outcome outstanding.
Joe Eikelberner, COO
BenjaminWest
FullStack acted as true partners and advisors. The expertise around AI and the level of developers, engineers—whatever role it was that came to the table—was just phenomenal.
Marisa Kopec, CEO
Lux Research
Speed is only the byproduct; the real value is better software and better use of our people.
Raj Tatta, VP of Engineering
Paciolan
FullStack turned our vision for The Launchpad into reality. Their intuitive design approach delivered an app that provides IT buyers a seamless and hassle-free experience, effortlessly connecting them with the ideal tech vendors.
Tonya Turrell, Founder & CEO
Technology Match
FullStack completely transformed our company's app, breathing new life into how we service our customer base. Their innovative and collaborative team delivered an application experience that we're proud to have in the market!
Jay Williams, Software Manager
Green Mountain Power
ORGANIZATION-WIDE AI MATURITY

Audit your idea-to-production cycle time first

Every engagement opens with a one-to-two week fixed-fee Diagnose: three real features traced end to end, every stage and handoff timed, a six-dimension maturity score, and a locked baseline.
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Core arc in 8–10 weeks*

From diagnosis to a client-led team running the new lifecycle themselves, on a defined cohort of up to 20 participants.

A group of business professionals collaborating in a lush urban park with towering skyscrapers in the background, symbolizing innovation and efficiency. The scene represents machine learning models optimizing business operations, as technology and sustainability converge to enhance productivity and decision-making in a modern corporate environment.

Measured weekly against Week 0

Primary metric is idea-to-production cycle time on the targeted stages, anchored on DX Core 4. If cycle time didn't move, the engagement didn't work, and the weekly reporting says so.

A group of business professionals collaborating in a lush urban park with towering skyscrapers in the background, symbolizing innovation and efficiency. The scene represents machine learning models optimizing business operations, as technology and sustainability converge to enhance productivity and decision-making in a modern corporate environment.

Tool adoption alone won't do it

AI tools can improve coding speed and software quality, but they don't automatically improve the full delivery lifecycle. Code generation may get faster while idea-to-production time stays the same. The harder work is changing the operating model around the tools, from the first commit through maintenance.

A group of business professionals collaborating in a lush urban park with towering skyscrapers in the background, symbolizing innovation and efficiency. The scene represents machine learning models optimizing business operations, as technology and sustainability converge to enhance productivity and decision-making in a modern corporate environment.

Full-lifecycle scope needs both owners

Engineering-focused work can improve delivery, but a full lifecycle transformation needs support from both the CTO and CPO. If only one is involved, we focus the engagement on engineering and explain the tradeoffs.

*These are typical time estimates and actual times may differ based on project complexity and scope.
COMPREHENSIVE SOLUTIONS

Explore FullStack's AI SDLC transformation services

Gartner projects that by 2028, 75% of enterprise engineers will use AI assistants daily in software development, and that the advantage goes to companies that integrate AI structurally into the delivery operating model through AI SDLC services and broader transformation services. Tool adoption is done. Operating model change is the open market. AI-powered tools integrate safely when governed by centralized access controls and standardized security patterns, keeping system behavior predictable rather than handing control to fully autonomous systems.
  • Idea-to-production lifecycle tracing
  • Six-dimension agentic maturity scoring
  • DX Core 4 instrumentation and baselining
  • Lifecycle stage friction ranking
  • Weekly measurement against the Week 0 baseline
  • Hypr framework deployment for AI agents
  • Cross-functional in-flow training and certification for engineers, PMs, and QA teams
  • AI SDLC services documentation and playbooks

Partner with FullStack and ship working software faster

Mid-Market and PE Portfolio

Business-unit or product-org entry

Above $5B in revenue, we enter at the business unit or product org level, the same delivery motion, scoped to a real mandate rather than sold as a company-wide platform rebuild, usually expanding from controlled, specific use cases with development teams before broader adoption.
Enterprise Partnerships

One maturity model across a portfolio

For operating partners who want portfolio-wide productivity and dislike anecdotes, Diagnose runs as a screening product: the same model across 5–10 companies, comparable scores, a roadmap each. Mid-market product teams run the identical model on their own backlog.
our blog

Featured articles

Conceptual image of dense legacy code transforming into streamlined digital pathways, illustrating AI-assisted code refactoring and migration.

How AI is Transforming Legacy Modernization

Discover how AI-powered legacy application modernization cuts costs, reduces risk, and brings outdated systems into modern, scalable environments.
Read post
Cross-functional business and technology team reviewing a legacy modernization plan, including application costs, timelines, and AI-assisted delivery options.

2026 Legacy Modernization Price Guide

See what companies can expect to pay for legacy modernization in 2026, and discover how AI-assisted modernization is changing the game.
Read post
A glowing interface shows the futuristic nature of AI-powered product design for enhanced UX

AI in Product Design: How to Build and Scale AI-Enabled Design Teams

AI is reshaping design teams. From integrating AI tools to structuring cross-functional teams, see how companies adapt design for an AI-driven future.
Read post
Business analyst reviews performance dashboards on dual monitors, illustrating human oversight and risk-based approval of AI agent actions.

Gate Fatigue: When Human Approval Stops Meaning Anything

Gate fatigue can weaken human oversight in AI workflows. Learn how to design approval gates that hold up as agent activity scales.
Read post
Speaker presents on AI governance, highlighting operational controls, monitoring, and responsible AI agent deployment.

How AI Agent Governance Is Moving Into Practice

AI governance is changing fast. Learn how businesses can manage AI agents, access controls, monitoring, and incident response.
Read post
Business team meets in a conference room as a presenter discusses a data chart, illustrating cross-functional planning for enterprise AI adoption and governance.

AI Adoption Is Now an Everyone Problem: What Walmart, The New York Times, and Honeywell’s Filings Reveal

AI adoption is reshaping retail and manufacturing. See what Walmart, The New York Times, and Honeywell filings reveal.
Read post

Frequently Asked Questions

Does AI-assisted development replace human review and QA?
No, and that's by design. AI-assisted development speeds up the drafting stage, but every feature still goes through human review and quality assurance before it ships. The six-dimension model actually tracks where review happens in the lifecycle, so you can see whether it's a real gate or a rubber stamp.
What's the difference between AI-driven development and full AI-autonomous development?
AI-driven development is a human-in-the-loop model: agents write and refactor code with a person setting direction and reviewing output. Full AI-autonomous development, where agents run without a person in the loop and try to generate entire applications on their own, isn't what FullStack builds toward. Both approaches can use the same models; the difference is who owns the decision. The lifecycle rebuild is designed around AI-driven development with human developers owning every handoff, not autonomy for its own sake.
How does this engagement change software delivery and the way user feedback gets incorporated?
Software delivery gets measured stage by stage, so a slowdown between spec and QA shows up instead of hiding inside a single "time to ship" number. That same tracing surfaces where user feedback actually reaches the team building the next iteration, and where it currently doesn't.
Does the AI SDLC rebuild touch version control and core software engineering practice, or just the AI layer?
It touches both. Version control workflows, branching, and review conventions get rebuilt alongside the AI tooling, because software engineering fundamentals are what make AI-generated code safe to ship fast rather than just fast.
What do "AI SDLC aims" actually mean, and does generative AI expand our AI capabilities or just automate what we already do?
The AI SDLC aims are concrete: trace the lifecycle, score it on six dimensions, lock a baseline, then close the gap stage by stage. Generative AI is one part of that, mainly in code generation and drafting. The bigger shift is in AI capabilities around the tooling: review, orchestration, and handoffs, which is where most of the stalled gains actually live.
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