Is your team slow, or does it just feel this way? Get the answer
At Softonix, we provide AI adoption consulting alongside a dev velocity audit. In 3 weeks, we score your codebase, delivery process, team structure, and AI setup, then hand you a ranked list of what’s costing you time. No coding work from your side.
Since you opened this page, a team of 5 engineers has cost you
$ 0.00
Do you know what you got for it?Do these challenges sound familiar?
- You asked the team why things are slow, but couldn’t evaluate the answer.
- Estimates are approximate, and you have no way to verify any of them.
- One engineer answers all the questions. When they’re off, work waits.
- Every release needs manual supervision, as if it were the first one.
- You bought the team AI tools, but nothing changed.
- When a deadline moves, you find out close to the date.
It’s not a team problem. You just manage a blind spot
End guessingYour blind spot has a location
Our Dev Velocity & AI adoption audit helps you find and fix it
Delivery has 4 points of failure. This software development audit checks them all:
Codebase health
Architecture, technical debt, test coverage, build, and CI times. Does the code slow down with every change?
Delivery process
Ticket to production. Where work sits, who unblocks it, and how long a pull request waits before anyone opens it. This goes deeper than standard code audit services: we tie code health to delivery speed, not just a list of issues
Product and engineering alignment
Requirements that move mid-sprint are considered slow shipping.
Your AI setup (0–100)
Spec-driven work and autonomous reviews on whole tasks, or prompting and hoping, one piece of routine at a time.
We evaluate the system, not the people in it
A slow pull request queue isn’t anyone’s fault until someone measures it.
Who is this audit for?
You have a live product with real users.
5 or more engineers, or an equivalent vendor contract.
You sign off on the engineering budget yourself.
You suspect something’s wrong and can’t prove it.
Real case: we were looking for bad code, but found a bottleneck instead
A founder told us his team was slow. The code was fine. The team lead was rewriting everyone’s work by hand every day.
90% of the team lead’s week, fixing other people’s first drafts
The team was stuck in a review loop no one had expected. The plan set up checks that catch most first-pass problems before a person sees them, so most of this work would stop reaching the lead at all, freeing his time for strategic tasks.
Nobody planned it that way. Everything was routed through one person because, at some point, that was the fastest path, and no one went back and changed it. The report named it, with the data to back it up.
How the audit works, week by week
Diagnose
Read-only access to your repositories, CI, issue tracker, and team channels. We pull cycle-time data across the full path: pull request opened, reviewed, merged, and deployed.
You get a short memo with the top 5 drags on delivery.
Depth and AI evaluation
A deeper read on architecture, hotspots, test coverage, and front‑end/back‑end friction + an AI adoption and audit score. We look at what the team delegates to agents, what they don’t do, and where the setup stops at the license level. You get an adoption score and a ranked list of what to change first.
We check direction with you mid‑week, before any conclusions harden.
The plan
You get your AI adoption plan: a step-by-step AI adoption strategy for closing the gap between where your team is and where it could be. It explains what to set up first, what should come off people’s plates, what the infrastructure should look like, and the order, ranked by effort vs. impact.
It’s built for your team to run without us. Start on step one the week you get it, and the first wins land before you reach the end of the list.
See where AI fits in your delivery. Get a ranked plan in 3 weeks.
Get your planTwo deliverables from one engagement
Dev velocity report
where your delivery stalls, ranked, with the data behind every finding. It highlights issues, including the ones only your team can fix.
AI adoption audit & plan
a prioritized roadmap: what to fix first, what to automate, and which repetitive tasks to remove from your workload. Every step is ranked by effort and impact.
Real case: good code, but weak AI setup. The plan we made to fix this
90%
codebase health
<50 out of 100
AI setup
Their AI tools were licensed and used only sparingly. The plan set up checks that catch most first-pass problems automatically, before anyone opens a review, so the routine that ate their days stopped needing a person at all.
That is what we actually needed, but we never had a chance to work on it because of the tight deadlines and continuous work on the product features. There is just no room for improvement or self-development. This AI Adoption plan looks amazing, can’t wait to start integration.
Engineering Team Lead
One page from your AI adoption plan
This is just a sample. Yours will be built around your team.
Everything above comes from a real AI adoption plan, including the structure, workflows, and level of detail. We’ll create yours around your team, processes, and tools.
Scope and pricing
2 deliverables in 3 weeks
Dev velocity report
Where delivery stalls, ranked, with the data behind every finding.
AI adoption audit and plan
A ranked sequence: what to set up, what to automate, what comes off your team’s workload.
Want help running the plan?
We can implement it with you, as a separate engagement, after you’ve seen the findings. The price is fixed before we start and doesn’t move based on what we find.
Book a free intro call
We’re happy to walk you through the scope, timeline, report, or fit for your team. We’ll give you clear answers with no pressure to commit.
Frequently asked questions
Cycle-time analysis across your pull request history.
We look at how long work sits before merge, where it stalls, and whether the same reviewer is the bottleneck.
Architecture and hotspot review.
Which files change and break most often, and whether they’re the same ones.
Test coverage and CI timing.
Not just the percentage, but how long a developer waits to learn something is broken, and how often they skip the wait.
Structured interviews with your team.
The same questions for everyone, so the answers can be compared.
An AI adoption survey and score against a 100-point benchmark.
What gets delegated, what doesn’t, and what the setup does on its own.
No. Read-only access to the repository is enough. We don’t commit, open pull requests, or touch your release schedule. The heaviest ask is a few short conversations with your engineers, plus a kickoff call at the start. If your team is mid-release when we begin, we work around it.
They can see symptoms, and they’re probably right about most of them. What they don’t have is the data underneath: how long work sits between opening and merging, which files break most often, and how much time goes to work nobody planned. The harder part is that they already know the people involved. This makes them better managers and worse assessors. We arrive without such a context.
Running the system is their job. Evaluating it can’t be, because the answer would be a verdict on their own work. This isn’t about honesty. In one audit, the team lead turned out to be the bottleneck and also the hardest-working person on the team. From the inside, that can look like doing the job properly.
Seats don’t mean adoption. Most of the teams we look at already have the licenses, and usage levels off after the first couple of weeks. The gains come from configuration: what the agent can do on its own, what runs before a human looks at the code, and what routine work stops being anyone’s job.
The NDA is signed before anything starts. Access is read-only, and if you’d rather not grant access at all, we can work from a mirror you control and revoke access at any point. We don’t commit, open pull requests, or leave anything in your history. Interviews with your engineers happen with your knowledge, and you decide who we talk to. When the 3 weeks end, access is revoked, and your codebase disappears from anywhere, in any form.
No. We evaluate the system, not the people in it; the Team Lead in our audit was the bottleneck and also the hardest-working person. When the cause is one person carrying work the system should carry, we report it as a workload problem, not a case against them. That’s also where this audit stops: we don’t build cases against anyone. If that’s what’s needed, this is the wrong instrument for it.
A written audit with a ranked action list, an AI setup score out of 100, and cycle-time data on your delivery, with the method behind each number.
The AI adoption plan is the main deliverable. The scores show where you stand; the plan says what to change, in what order, and what each change should move. Think of it as an AI readiness assessment for engineering teams: it measures cycle time, codebase health, and what your engineers delegate to AI agents, not company-wide data governance. It’s written for your team to run without us: which parts of the routine people can handle first, what the setup needs to do on its own before that works, and what to check after each step.