AI-Assisted Engineering

Your engineering team adopted AI. Is it actually working?

The AI-Assisted Engineering Diagnostic is a four-week, research-backed assessment where our team benchmarks your engineering organization against DORA's panel of 5,000 peers and delivers a clear, prioritized action plan, including:

Objective AI Readiness Score

Scored across the seven capabilities that predict AI ROI: AI Policy, Data Ecosystems, Data Access, Version Control, Small Batch Sizes, User-Centric Focus, and Internal Platforms.

Team-by-Team Archetype Mapping

A clear picture of where every engineering team actually stands, from Harmonious High-Achiever to Foundational Challenges, not just an org-wide average.

Prioritized 30/60/90-Day & 6-Month Roadmap

A focused, sequenced plan for what to fix first, next, and over time – prioritized against benchmarks from nearly 5,000 peers, so you're acting on data, not a guess.

Tailored Audience Briefings

Purpose-built findings for your executive team, IT leadership, and individual teams, so every stakeholder gets what speaks to them.

Request Your AI-Assisted Engineering Diagnostic

Ready to get started? A brief qualification call helps us confirm fit and scope your four-week engagement – and if you've got questions about the process, we're happy to talk through those too. No commitment required.

SOUND FAMILAR?

The challenges we hear most

Unproven AI ROI

Licenses are purchased and tools are rolled out, but no one can point to a measurable change in speed, quality, or cost.

Inconsistent AI Usage

Without shared training or standards, every developer uses AI differently – creating sprawl instead of scale.

Code Speed, Not Delivery Speed

AI is helping engineering teams write code faster, but that code isn't necessarily shipping software any faster, tested and secured.

No Visibility Into Bottlenecks

Leaders sense something is off, but without objective, team-level data, they're left guessing whether it's tooling, process, or people.

How it works

Benchmarking your AI readiness in four weeks

Gather the Data

We collect data straight from the people doing the work and the people managing it – 20-minute surveys, 45-minute team interviews, and a 90-minute value stream mapping workshop.

Benchmark the Findings

We score your results against DORA's AI Capabilities Model and their panel of nearly 5,000 technology professionals – so you know exactly how urgent each gap really is.

Deliver the Roadmap

You get team-by-team scorecards, a value stream map, and a prioritized 30/60/90-day and 6-month roadmap – plus tailored briefings for executives, IT leadership, and individual engineering teams.

WHO THIS IS FOR

This is perfect for you if...

You lead engineering, technology, or IT for a mid-market to enterprise organization with an internal team building custom software solutions.
Your engineering teams are already using AI tools, but you can't say with confidence whether it's actually improving software delivery – or just adding noise.
Your path to production is slow or cumbersome – software isn’t getting into the hands of users quick enough.
You're facing pressure from your board or C-suite to show AI ROI, and you need objective, benchmarked data to back up your next move.
You want a clear, prioritized roadmap for where to invest next – not another opinion, another tool pitch, or another slide deck full of hype.

FAQ

Questions You're Probably Asking

communication bubble with three question marks for our FAQ section of the home page

How does it work?

Over four weeks, we gather real data from your engineering teams through short surveys, interviews, and a value stream mapping workshop – then benchmark the results against DORA's published research and a broader peer panel. You get team-by-team scorecards, a value stream map, and a prioritized roadmap for what to fix first, next, and over time.

What do we get, as far as the deliverable?

A full findings report, team-by-team scorecards mapped to DORA's performance archetypes, a value stream map of your current-state software delivery pipeline, and a prioritized 30/60/90-day and 6-month action plan – plus tailored briefings built for your executive team, IT leadership, and individual teams.

Who from our company needs to participate, and what is the time commitment?

Team members participate in 20-30 minute surveys, 45-60 minute team interviews, and we run a 90-minute value stream mapping workshop for Product, Testing, Software Engineering, and DevOps. The engagement is designed for minimal operational disruption.

How much does it cost?

The AI-Assisted Engineering Diagnostic is a $30,000, four-week engagement – so you know the full cost and timeline going in, with no surprises and no open-ended hours.

What is DORA?

DORA (DevOps Research and Assessment) is the research team, now part of Google Cloud, behind some of the most widely respected, peer-reviewed research in software delivery. Their 2025 AI Capabilities Model – built from research involving nearly 5,000 technology professionals – is the foundation our diagnostic is benchmarked against, so findings are grounded in independent data, not a framework we invented ourselves.

Why would I use this over any other maturity models or assessments?

Ours is anchored in DORA's published, peer-reviewed research, delivered by practitioners who've actually built and run engineering organizations, and produces a specific, prioritized roadmap – not just a maturity score you have to figure out how to act on. Most assessments are either generic (not specific to AI-assisted engineering) or proprietary (built by a firm to sell their own methodology).

Can this help us identify strong performers or potential internal champions?

Yes and no. Because the diagnostic evaluates team-by-team, it naturally surfaces which teams are already doing things well – visibility you can use to identify internal champions and scale what's working to the rest of the organization, however it cannot isolate and evaluate individual performance.

Can we redo this diagnostic later to measure progress?

Yes. Re-benchmarking six or twelve months after your first diagnostic shows whether the changes you made actually moved the needle, using the same objective framework.

AI is live. Results aren't. We find out why.

AI-Assisted Engineering Diagnostic