A method and a platform
We create AI engineers for the future.
Not people who know about AI, but engineers who can build it, to standard, from day one. Two things make that possible: a method built for this era, and a platform that delivers it at scale.
How we use AI to teach AI
- 1Every submission reviewed against a written engineering standard
- 2Struggles flagged the same week, not at the final assessment
- 3Mentors spend their time on judgment, not routine checking
Drag the handle to follow an engineer through the programme.
A method
Coordinated training taught on one real-world project and assessed on delivered work rather than a test. That work ends up in the engineer's own repositories, not ours.
See the method 02A platform
One place holding the curriculum, the labs, the AI review and the assessment. It's what lets the method survive contact with a hundred engineers instead of ten.
See the platformNeither works alone. A method without a platform runs once, for one cohort, for as long as the best mentor stays. A platform without a method is a video library.
Why it's needed
Knowing isn't the gap. Delivering is.
People arrive knowing a great deal, from a degree or a decade in another stack, and still take months to become productive. The gap isn't knowledge. It's application, standards and delivery, and it's engineerable.
What most training gives
- Theory, algorithms, fundamentals
- Correctness in isolation
- A stable, slow-moving syllabus
What industry needs
- Applied engineering and standards
- Architecture and the modern stack
- Delivery craft, in a team
The work has changed
Agents, RAG, evaluation and AI-assisted delivery are core engineering now, not a specialism. The job was redefined in the last two years, whoever is walking into it.
Our answerEvery track shares a core where engineers learn to build with AI, and every module is assessed on applying it to a real use case, never on recall.
Curricula can't keep pace
Courses validate slowly; the stack changes monthly. Every intake arrives further from ready, through no fault of the course.
Our answerOur content is versioned and revised as the practice moves, the way engineering is, so the fast-moving layer stays current instead of waiting on a revalidation cycle.
Employers can't wait
Employers need engineers productive from day one, not after months of expensive ramp-up they increasingly refuse to absorb.
Our answerEngineers rehearse delivery before it counts: a capstone delivered to a proxy client (a mentor playing the client) under changing requirements, and certified on that work, not attendance.
What makes it different
Why this, not the usual way.
Most AI training is one of three things. Here is what changes.
See it for yourself. Two sections of the Applied AI track, free: the videos, the reading and the decision questions.
Try the free previewThe method
Teach engineering the way industry practises it.
An applied layer on what someone already knows, not a replacement for it. Capable in, industry-ready AI engineer out.
The method · the four dimensions
Judgment first, then depth.
Core before depth. Every track shares the same core, so every engineer learns to build with AI before specialising. Governance runs through every dimension rather than sitting in one module. Together the four dimensions form the catalogue; each nine-week cohort follows a path composed from it.
Judgment and client craft: what makes a forward deployed engineer
6 modules · application-based assessment on real use cases
See the six core modules
- The Forward Deployed Engineer (FDE) operating model
- Problem Discovery & Scoping
- Client-Facing Skills
- Delivery Method & Pace
- Judgment, Autonomy & Escalation
- Feedback & Knowledge Loop
What they must show
A scoping brief and a live client session: judgment shown, not asserted.
What you build with
6 modules · application-based assessment on real use cases
See the six technical modules
- Product & Platform Fundamentals
- Systems Design
- LLM Foundations
- Integration & Environment Craft
- AI-Driven SDLC
- Governance & Regulated Environments
What they must show
A working build under real client constraint, walked end to end.
What you build within
4 modules · application-based assessment on real use cases
See the four functional modules
- Product Thinking & PDLC
- Commercial & Engagement Awareness
- Engagement Operations
- Client Organisational Literacy
What they must show
An engagement plan and commercial narrative, defended to a panel.
Depth: one track per engineer, five to choose from
Five tracks, all running · Applied AI: 9 modules with a hands-on lab in every section · other tracks: applied assessment on real use cases
Whatever the track, every engineer takes the same core, including LLM Foundations, the AI-Driven SDLC and Governance & Regulated Environments. A data, platform, integration or web engineer leaves able to build AI into their own discipline. Applied AI goes deepest.
See the five tracks
- 01 · Applied AI
- 02 · Data Engineering
- 03 · Platforms & Infrastructure
- 04 · Microservices & Integration
- 05 · Mobile & Web
What they must show
The assessed, real-world capstone. This is the certification gate.
Part of the method
One curriculum, a plan for each engineer.
Nobody starts in the same place. Each engineer's plan is built from their profile and a baseline assessment, then tailored in depth, emphasis and pace. It stretches the strong, supports the struggling, and moves with them as evidence arrives.
- ProfileWhere they come from and what they already build with.
- BaselineWhat they can actually do on day one, assessed rather than self-reported.
- PlanDepth, emphasis and pace set per engineer, within the same curriculum and gates.
- AdjustRevised as evidence arrives, so the plan tracks the engineer rather than the calendar.
The method · how it is delivered
Learn alone. Get unblocked live. Solve the hard parts together.
Self-paced, office hours and classroom, coordinated by the platform so no mode ever starts cold. Pick a mode to see what it does and what it leaves behind.
Every mode feeds the platform, so no session starts from scratch.
The cohort delivery model: 9 weeks, by mode
A nine-week cohort on the Applied AI path: about 15 hours a week, with self-paced learning and office hours every week and three in-person days. Select a week.
See the nine weeks, week by week
Weeks 1–6 build capability · weeks 7–8 are the capstone sprint · week 9 is assessment and certification.
The method · the Applied AI track
Every lab carries industry craft.
Every module is assessed on a real use case. Applied AI goes further: a hands-on lab in every section, reviewed until it meets the standard. What follows is drawn from that track.
hands-on labs in the Applied AI track
One in every section of its nine modules, each on a real use case. A cohort's path draws on them, and every lab an engineer completes feeds the capstone and lands in their Git profile.
See what every lab carries
Design standards
- Grounding & citations
- Testing: evals as tests
- Error handling
- Code-review standards
Architecture
- The agent loop
- RAG pipeline design
- MCP contracts
- Separation of concerns
Concepts
- The AI SDLC
- Non-determinism
- Tool use & autonomy
- Quality gates
How every topic is taught
Every topic is taught on one real-world project, so engineers see why things fail and what it takes to get from demo to production. Each is delivered three ways.
GlassCast
Our glassboard method: the concept built up on an illuminated board and captured on video, so the reasoning is visible rather than narrated over slides.
Concept reading
The same concept in written form, to read, annotate and return to at the engineer's own pace.
Real-world case study
The concept at work inside a real engagement, including the part where it went wrong.
Application-based quizzes close each section: they test whether an engineer can apply the idea (understanding, not recall) before moving on.
The method · the culmination
Proven on a real-world capstone.
The programme is judged on delivery, not a test. Everyone ships real work to a proxy client under real conditions, and that is the credential.
A pod takes a real brief
Up to four pods per cohort, each a delivery team of five to six engineers given a distinct, real-world project.
Everyone owns a feature
One feature each, built and delivered end to end, so everyone builds and everyone is assessed.
Delivered to a proxy client
A mentor plays the client for a two-week sprint: setting the brief, changing requirements, wanting a demo on Thursday.
Assessed as the final gate
The delivered capstone is the certification gate. Pass, and the credential is earned.
The platform
Our platform runs the method at scale.
A method that depends on a great mentor works once. The platform lets it run for a hundred engineers across several cohorts without diluting.
The platform · the AI layer
Every submission reviewed. Every struggle caught early.
The AI review feeds everything else here: its evidence drives office hours and adjusts each engineer's plan, and because routine review is automated, mentor time goes where judgment is needed.
Every submission is checked the way a senior engineer would check it (grounding, tests, error handling, structure) against a written engineering standard called SKILL.md. Try it.
Nothing worse ships: the gate stays closed until the standard is met. Alongside it sits a grounded AI tutor that answers from the course's own material and coaches to the answer rather than handing it over.
Every week, proof of progress: captured in the platform, reviewed by AI, and coached.
Record
Engineers record the weekly demo directly in the platform.
AI reviews
The AI reviews the video: both the working software and the pitch.
To the mentor
Feedback is routed to the mentor automatically.
Office hours
Picked up and coached in the next session, while it still matters.
The recorded demo is that week's proof of work, and it lands in the engineer's portfolio as visible proof they can build and present.
Ordinary office hours begin with a guess. Ours begin with evidence. Switch the signals to see the agenda assemble.
Proactive office hours · Thursday
Assembled by the platform for the mentor
No signals selected. Without them, office hours begin with a guess, which is how cohorts lose people quietly.
The signals an engineer's own work generates become the agenda for their coaching.
The platform · the personas
Four roles, one source of truth.
Each role has one clear job. Select a role.
Content, delivery, AI coaching, people and proof all plug into one platform: the single source of truth.
The proof
Capability you can check.
Four gates to the certificate
Every assessment on this page is one of these four. Pass all four, and Dakshify awards the certificate.
Certified on evidence, not attendance.
Who's behind it
Built by people who have delivered applied AI in regulated environments such as banking, financial services and healthcare, where evaluation and audit decide what ships. The method is what that work produced.
Who awards the certificate
Dakshify awards it, on assessed delivery. It's an industry credential rather than an academic award. Its weight comes from the builds, reviews and capstone an employer can read for themselves.
Engineers leave ready. Leads leave with proof.
Not a certificate and a set of slides. The point of the programme is that someone can be put on a client engagement the week after it ends.
If you are the engineer
- Work they own. Every lab and the capstone in their own repositories, as evidence anyone can inspect.
- A method you keep. The AI SDLC, SKILL.md and the quality gate are how you'll work afterwards, not an exercise you leave behind.
- Rehearsed delivery. You've scoped a problem, run a client session, handled changing requirements and presented at Demo Day, all before it counted.
- Judgment about failure. You've seen the system break the way real ones break, and you know what to do next.
If you run the cohort
- A live picture of capability. Progress against objectives across engineers, cohorts and pods, updated by the work itself.
- Intervention before the drop-off. Configure the alerts and the platform flags who needs attention: oversight without the effort.
- A path you compose. Choose from the catalogue, build each cohort's nine-week path, set the schedule, without waiting on us.
Who it's for
The same method, three different reasons to buy it.
The method and the platform don't change. What changes is what you get back. Select the one that fits.
Employability you can influence
Your graduates leave industry-ready, with public repositories of delivered work an employer can inspect, which is exactly what employers say is missing.
A curriculum that keeps pace
The fast-moving layer sits here, above a field that changes faster than any syllabus can be revalidated.
A credential to trust
Backed by an assessed, real-world capstone. Proof, not just completion.
Differentiation, and it's complementary
A distinctive applied programme that sits on the degree rather than replacing it. It also reports itself, through gate completion and portfolio evidence rather than a survey.
Where it fits
Delivered alongside the degree rather than inside it, so it can change every term without touching accreditation. Nine weeks, three in-person days, one certificate at the end.
Talk to us about a university cohort More for universities →Turn bench time into billable capability
Engineers between engagements are cost with no return. Nine weeks at about 15 hours a week turns that time into the AI capability your clients are asking for, without taking anyone off a billing engagement.
Off the bench, billable in AI work
They come off the programme having already shipped to a proxy client under real conditions, chargeable sooner and at a rate their evidence supports.
Bid for work you currently pass on
You have the client relationships. What you lack is a bench that can deliver agentic and AI engineering. This builds it from the people you already employ.
Capability you own, not capacity you rent
Subcontracting specialists delivers the project and leaves you where you started. Enablement leaves the capability, along with the method, standard and quality gate that come with it, on your margin.
How this usually starts
A cohort drawn from your bench, pointed at a live opportunity you are already chasing. Four pods of five to six, so up to 24 engineers come back ready for the same kind of work at once. If the capability doesn't stick, the model hasn't worked, and that's a fair test to agree upfront.
Talk to us about a bench cohort More for consulting firms →Pilots that reach production
Most AI work stalls between demo and release because nobody agreed what "good enough to ship" means. The gate makes that explicit.
One standard across teams
Everyone reviewed against the same written standard, so AI work stops being a collection of individual habits.
Reskilling your own engineers
Faster than hiring scarce AI engineers, and your people already understand the domain, which is the harder half. 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.
A note on honesty
There's no return figure on this page. Any number invented here would be worth less than one built with you from your own ramp time, bench cost and win rate. That's the first hour of any conversation.
Talk to us about your team More for engineering teams →We create AI engineers for the future.
Tell us who you want to train. We'll walk you through a cohort end to end: the tracks, the nine weeks, the capstone and the evidence an engineer leaves with.
I'm here for
Or write to us directly: info@dakshify.ai