For engineering teams
One standard for AI work, and pilots that reach production.
Reskill the engineers you already have to build with AI, reviewed against one written engineering standard. Faster than hiring, and your people already know the domain.
The problem
Most AI work stalls between demo and release.
Usually it's because nobody agreed what “good enough to ship” means. Each engineer brings their own habits, each pilot its own definition of done, and the risk function has nothing consistent to review.
Hiring scarce AI engineers is slow, and they arrive without your domain. Generic courses prove someone finished, not that they can deliver. What's missing is a shared standard, and practice meeting it.
What you get back
What your team gets.
Pilots that reach production
The quality gate makes “good enough to ship” explicit, so work moves from demo to release against criteria everyone agreed.
One standard across teams
Everyone is reviewed against the same written standard, so AI work stops being a collection of individual habits.
Reskilling your own engineers
Faster than hiring, and your people already understand the domain. About 15 hours a week, so it plans around delivery rather than stopping it.
Evidence for the risk function
Evaluation, quality gates and portfolio evidence are built into how engineers work, which is what regulated environments ask for anyway.
The standard
Reviewed the way a senior engineer would review it.
Every submission is checked for grounding, tests, error handling and structure against a written engineering standard called SKILL.md. Work doesn't pass until it meets the standard.
Engineers keep the method afterwards. The AI SDLC, SKILL.md and the quality gate become how your team works, not an exercise they leave behind. As the lead, you get:
- A live picture of capability across engineers, cohorts and pods
- Alerts that flag who needs attention before they drop off
- A nine-week path you compose from the catalogue, on your schedule
- Office hours built from evidence: review results, quizzes and weekly demos
- Every lab and capstone in repositories you can inspect
How it works
Nine weeks, assessed on delivered work.
Baseline and plan
Each engineer is assessed on what they can actually do on day one. Their plan sets depth, emphasis and pace within the same curriculum.
Learn three ways
Self-paced study, weekly office hours built from evidence, and three in-person days: kickoff, mid-point and finale.
Labs to a standard
Every submission is reviewed against a written engineering standard, SKILL.md. Work doesn't pass the quality gate until it meets it.
Capstone delivery
Pods of five to six each take a real-world brief. Everyone owns one feature, delivered to a proxy client under changing requirements.
Certificate
The delivered capstone is the final gate. Pass, and Dakshify awards the certificate, backed by work anyone can inspect.
Built for regulated environments
The method comes from delivering applied AI in banking, financial services and healthcare, where evaluation and audit decide what ships. Governance runs through every dimension, and Governance and Regulated Environments is part of the shared core.
Questions
What people usually ask.
How much time does it take?
About 15 hours a week for nine weeks: mostly self-paced, with weekly office hours and three in-person days. It plans around delivery rather than stopping it.
Which engineers is it for?
Engineers who already build in another stack. There are five tracks: Applied AI, Data Engineering, Platforms and Infrastructure, Microservices and Integration, and Mobile and Web, all sharing the same AI core.
Can we shape the programme?
Yes. You choose from the catalogue, compose each cohort's nine-week path and set the schedule, without waiting on us.
What evidence do we get at the end?
Reviewed labs, recorded weekly demos, a capstone feature delivered end to end to a proxy client, and a certificate awarded on that assessed delivery.
How many engineers per cohort?
Up to 24, in four pods of five to six. Each pod takes a real-world brief and every engineer owns one feature.
Can we see it first?
Yes. Two sections of the Applied AI track are free to try: the videos, the reading and the decision questions.
Give your team one standard, and ship what you pilot.
We'll walk you through a cohort end to end: the tracks, the nine weeks, the capstone and the evidence each engineer leaves with.