3–5 documented projects
Each with a short write-up: the problem, your approach, the trade-offs, the result, and how it was evaluated.
A portfolio that translates into a job
You don't build throwaway practice projects. You contribute to real, in-progress work — so what you graduate with is evidence you can do the job on day one.
What “real” means
Real project work, reviewed like production
Every build in a CapabilityNext programme is a genuine project: real requirements, real data, real constraints, and a real review. It's assessed the way a senior engineer reviews a pull request — for correctness, clarity, trade-offs and cost — not marked like a worksheet.
- Briefs come from actual work — internal tools, data problems, model deployments — not textbook exercises.
- You handle the messy parts: missing data, ambiguous specs, latency and budget limits.
- Every submission gets written mentor feedback plus a live review.
- “It runs” isn't the bar. “You can explain and defend it” is.
What you leave with
The portfolio, unpacked
A presented capstone
A complete project on a real brief, demoed at a showcase — the piece you walk an interviewer through.
A mentor reference
A practitioner who reviewed your work and can speak to how you think, not just that you attended.
It's structured to map onto what technical interviews actually ask about — architecture decisions, evaluation, cost, and what you'd change.
From portfolio to payroll
Each programme is built around a live role
Before a programme runs, we work backwards from a real hiring need: the responsibilities, the tools, and what those interviews screen for. The curriculum, the projects and the capstone are shaped to that profile — so on completion you're not “job-ready” in the abstract, you're ready for a specific kind of role and can start applying immediately.
- Interview preparation tuned to AI roles — technical rounds, take-homes, and how to present your portfolio.
- CV and portfolio review with a mentor.
- Introductions where we have them.
- An honest caveat: we don't guarantee any individual offer — that still comes down to your interviews.
Student work
A sample of what graduates ship
The kind of work that comes out of a cohort. (Illustrative examples — replace with real cohort projects, with each student's consent.)
GenAI & LLM
Policy assistant with source citations
RAG assistant over a 400-page handbook; 92% answer accuracy on a 50-question test set, under a fixed cost per query.
Data & MLOps
Churn model, deployed and monitored
End-to-end pipeline: feature store, containerised serving, drift alerts and an automated retraining trigger.
Computer Vision
Document layout extractor
Detection + OCR pipeline turning scanned invoices into structured JSON, evaluated on a held-out set.
Student stories
In their words
"I came in writing scripts and left having deployed a monitored model on the cloud. The weekly reviews are what made it stick."
"The LLM track was exactly the practical grounding I needed. My capstone became a demo I now show in interviews."
"As a final-year student this bridged the gap between coursework and what teams actually expect. Worth every evening."
Build the portfolio that gets you hired
Tell us the role you're aiming for and we'll map it to a track.
Talk to an advisor