For universities
Graduates who finish industry-ready in AI engineering.
An applied AI engineering programme that runs alongside the degree, not inside it. Your students learn to build with AI to a written engineering standard, and leave with delivered work an employer can inspect.
The problem
Your graduates know a lot. Employers want them to deliver.
Students reach their first job knowing a great deal: algorithms, theory and fundamentals. What employers say is missing is applied engineering: working to standards, building with the modern AI stack and delivering as part of a team. That gap isn't the degree's fault. It's a different layer, and it can be taught.
The AI layer also moves faster than any syllabus can be revalidated. Agents, retrieval, evaluation and AI-assisted delivery became core engineering in the last two years. A course that waits on a validation cycle will always be a step behind, and every intake arrives a little further from ready.
What you get back
What your university gets.
Employability you can influence
Graduates leave with public repositories of delivered work, every lab and the capstone, which is exactly the evidence employers say is missing.
A curriculum that keeps pace
Our content is versioned and revised as the practice moves, so the fast-moving layer stays current without touching your accredited modules.
A credential with evidence behind it
The certificate is earned on an assessed, real-world capstone, not attendance. Its weight comes from work an employer can read for themselves.
Outcomes that report themselves
Gate completion, review results and portfolio evidence show what each cohort can actually do, rather than a satisfaction survey.
Where it fits
Alongside the degree, not inside it.
The programme sits on top of what students already know rather than replacing any module, so it can change every term without touching accreditation.
Every student takes the same core first, including LLM Foundations, the AI-Driven SDLC and Governance, then goes deeper in one of five tracks: Applied AI, Data Engineering, Platforms and Infrastructure, Microservices and Integration, or Mobile and Web. What each student leaves with:
- Every lab and the capstone in their own repositories
- A feature delivered end to end to a proxy client, under changing requirements
- Weekly recorded demos that become part of their portfolio
- Feedback against a written standard the same week, not at the final assessment
- A method they keep: the AI SDLC, SKILL.md and the quality gate
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.
Struggles caught the same week
AI review evidence, quiz results and weekly demos feed the platform, so office hours start from what each student is actually stuck on, and nobody drops off quietly before the final assessment.
Questions
What people usually ask.
Is it an academic award?
No. Dakshify awards an industry credential on assessed delivery. It complements the degree rather than adding credits to it, and its weight comes from the builds, reviews and capstone an employer can inspect.
Which students is it for?
It's an applied layer on what someone already knows, so it suits students who can already program and want to become AI engineers. Each student's plan is set from a baseline assessment, so the strong are stretched and the struggling are supported.
Does it replace any of our modules?
No. It's delivered alongside the degree, so your curriculum and accreditation stay exactly as they are.
How is it assessed?
On delivered work, never on recall. Labs pass a quality gate against a written engineering standard, and the final gate is a capstone feature delivered end to end to a proxy client.
How large is a cohort?
Up to 24 students, in four pods of five to six. Each pod takes a distinct real-world brief and every student owns one feature.
Can we see it before we talk?
Yes. Two sections of the Applied AI track are free to try: the videos, the reading and the decision questions.
Give your graduates work an employer can inspect.
We'll walk you through a cohort end to end: the tracks, the nine weeks, the capstone and the evidence each engineer leaves with.