You leave able to do the job, not holding a piece of paper that says you can.
Practitioner-led, practice-first programmes curated to the team's exact stack - fresher induction and lateral upskilling for engineers, and functional AI enablement for every business function. Scored against real work, not attendance.
Built to Deploy
We don't train people for the role. We simulate the team they're about to join.
AI Enablement for Every Function in Your Organization
Everyone's already using AI. Almost no one's using it well.
- 40+
- competencies tracked per programme
- 18,000+
- training hours delivered
- Hands-on
- practice-first, with minimal lecture time
Technical Training
Getting engineers deploy-ready on your exact stack - what makes it different, the enablement itself, and the programme tracks we run.
Training that changes what you do at work, not just what's on your resume
Curated to your exact stack
Programmes are built against the team's real codebase, ERP landscape, or CRM - not a generic syllabus. Fresher induction and lateral upskilling run on the tools people will use on Monday.
Capstones inside Agile pods
Learning finishes inside a working pod, on real tickets. The standard we hold to is a Friday bootcamp exercise becoming a Monday live production ticket - not a slide deck and a quiz.
Scored, not attended
Progress is measured on coding assessments, assignments, and quizzes against 40+ tracked competencies. If someone is struggling in week 2, we know in week 2 - not at a post-programme survey.
Deploy-ready engineers on your exact stack
KLS trains engineering teams to work with modern delivery workflows and the enterprise platforms they already run - measured by what teams ship, not seat time. Induction gets freshers deploy-ready; lateral programmes move experienced engineers onto a new stack fast.
Modern engineering workflows
Teams adopt AI inside real delivery: planning, code, review, testing, and release - not isolated tool demos.
Enterprise platform fluency
Role-specific enablement on the platforms your teams operate daily - Software Engineering, SAP, and Salesforce, each with GenAI inside the workflow.
Assessed against competencies
Every track maps to tracked competencies and is scored on assignments and assessments, so progress is visible mid-programme.
Practitioner-led, capstone-based delivery
Training is built around practical execution: workflow simulation, real tool usage, and capstones inside Agile pods on representative codebases and tickets - not theory-only sessions.
Software Engineering with GenAI
Teams build and ship using AI-assisted engineering workflows - prompt-driven development, AI-assisted debugging, test and documentation generation across the frontend, backend, and deployment lifecycle.
SAP with GenAI
SAP teams use AI to accelerate documentation, support workflows, process understanding, reporting, and requirement interpretation - turning tribal knowledge into retrievable answers.
Salesforce with AI
Salesforce teams apply AI across CRM workflows, support operations, and delivery - from case summarization to automation ideation and reporting.
Functional AI Training
Making every business function AI-fluent - the method we teach and how it lands per function.
Design Thinking → Context Engineering → Prompt Mastery
Tool training alone produces people who can operate a model but cannot decide what to point it at. The method runs in sequence: frame the real problem, engineer the context the model needs to answer it well, then master the prompting craft that gets a dependable answer out - on secure enterprise Copilot and ChatGPT, per function.
Design Thinking
Start from the workflow, not the tool. Teams learn to identify which parts of their own work are worth handing to AI, what a good outcome looks like, and where a human has to stay in the loop.
Context Engineering
The discipline most tool training skips: assembling the documents, data, system state, and constraints a model needs before it can be useful on enterprise work - and structuring them so answers stay grounded.
Prompt Mastery
Repeatable prompting craft against real tickets and real codebases - decomposition, iteration, verification, and knowing when the output cannot be trusted without a check.
AI productivity, per business function
Function-specific productivity programmes for business teams - focused on the documents, decisions, and operational work each department runs every day. Sales teams alone spend around 14 hours a week on admin the method is built to give back.
The method behind every card: Design Thinking → Context Engineering → Prompt Mastery: find the real problem, feed AI the right context, get output that's usable.
Sales
~14 hrs/week lost to admin and proposals - trained to feed AI their actual pipeline context, not generic prompts, for faster deal closure.
Marketing
Manual content and audience research - trained on role-specific prompting for content and targeting, for higher engagement and better ROI.
Recruitment
Resume overload forcing superficial screening - trained on structured AI-assisted shortlisting matched to their hiring bar, for better hires and stronger retention.
Operations
Manual process and inventory decisions - trained on AI-assisted process and inventory analysis, for lower cost and faster operations.
Finance
Manual variance analysis and reconciliation - trained to feed AI real transaction and ledger context, for faster close cycles and fewer errors.
Customer Support
Generic ticket responses and slow resolution - trained on AI-assisted case triage and structured response drafting grounded in your actual knowledge base, for faster resolution and higher CSAT.
What the training installs
The requests that come up most often
Technical Training
- 01Fresher induction into a specific tech stack before they touch a live project
- 02Lateral upskilling when the team adopts a new framework or tool mid-project
- 03A pre-project bootcamp so a new delivery pod is productive from day one
- 04Training structured around Agile sprint rhythms, not a classroom calendar
Functional AI Training
- 01Getting a team past generic prompting into AI use grounded in their actual work context
- 02Rolling out enterprise Copilot/ChatGPT access with real training attached, not just licenses
- 03A short workshop to make one function (sales, recruitment, ops, marketing) AI-fluent before a wider rollout
- 04Turning "everyone's already using AI a little" into consistent, structured use across a team