For consulting firms
Turn bench time into billable AI capability.
Your clients are asking for agentic and AI engineering. Your bench already has engineers who know your clients' domains. Nine weeks turns them into consultants who have already shipped AI work to a client, under real conditions.
The problem
You have the relationships. You need the bench.
Engineers between engagements are cost with no return. Meanwhile AI work goes to firms that can staff it, or gets subcontracted to specialists who deliver the project and leave you exactly where you started.
Hiring AI engineers is slow and expensive, and new hires don't know your clients. Your own people already understand the domain, which is the harder half. What they need is applied AI engineering, to a standard you can put in front of a client.
What you get back
What your firm gets.
Off the bench, billable in AI work
They finish having shipped to a proxy client under changing requirements, so they're chargeable sooner, at a rate their evidence supports.
Bid for work you currently pass on
A bench that can deliver agentic and AI engineering opens the engagements you'd otherwise decline or subcontract.
Capability you own, not capacity you rent
Enablement leaves the capability with you, along with the method, the written standard and the quality gate, on your margin.
Built around billing
About 15 hours a week, so nobody has to come off a billing engagement to take part.
Client craft is part of the core
Trained to work in front of a client, not just at a keyboard.
Every track starts with the Forward Deployed Engineer operating model: problem discovery and scoping, client-facing skills, delivery pace, judgment and escalation. Engineers have to show it in a scoping brief and a live client session.
The functional dimension adds product thinking, commercial and engagement awareness, engagement operations and client organisational literacy, assessed by defending an engagement plan to a panel. By the end, each consultant has:
- Scoped a problem and run a live client session
- Delivered a feature to a proxy client who changed the requirements mid-sprint
- Presented and defended their work at Demo Day
- Every lab and the capstone in repositories you can review
- A working method: 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.
How this usually starts
A cohort drawn from your bench, pointed at the kind of opportunity you're 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.
Questions
What people usually ask.
Does it take people off client work?
No. It's about 15 hours a week, mostly self-paced with weekly office hours, plus three in-person days for kickoff, mid-point and finale.
Which engineers can take part?
Data, platform, integration, web and mobile engineers as well as those heading for applied AI. Every track shares the same AI core, so each engineer leaves able to build AI into their own discipline.
What return should we expect?
We don't publish a figure. Any number here would be worth less than one built with you from your own bench cost, ramp time and win rate, and that's the first hour of any conversation.
How do we know who's ready?
You get a live picture of progress across engineers and pods, updated by the work itself, plus configurable alerts that flag who needs attention before they fall behind.
What does the certificate mean to a client?
It's awarded on assessed delivery, not attendance. The evidence behind it, reviewed builds and a delivered capstone, is something you can show a client.
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.
Build the AI bench your clients are asking for.
We'll walk you through a cohort end to end: the tracks, the nine weeks, the capstone and the evidence each engineer leaves with.