Prove GenAI capability across your enterprise workforce.
AI made knowing about GenAI easy. What counts now is talent who can build, ship and secure it on your stack. Tekstac runs your people through real work on the tools they actually deploy with and shows you, with evidence, exactly who’s ready.
GENAI ROLES (Sample Readout) n = 240 evaluated
Ready
Near-ready
Gap
0 25 50 75 100%
● Ready – deployable now
● Near-ready – targeted practice
● Gap – bench is thin
The market is full of GenAI content. Almost none of it proves capability.
Enterprises have spent two years buying video libraries, certificates and AI-literacy programmes. What they have to show for it is attendance – dashboards that count who watched, not who can build. When a GenAI project needs staffing, none of it tells you who to put on it.
WHAT YOU BOUGHT
- Content and video libraries
- Completion certificates
- AI-literacy programmes
- Attendance dashboards
WHAT YOU STILL CAN’T ANSWER
- Who can ship a RAG feature on our stack?
- Who can run agents safely in production?
- Who is ready for a GenAI project today?
- Where is our bench thin, by team and role?
What you get ?
A workforce you can deploy on GenAI work and the evidence to prove it.
01 - Complete
The whole GenAI workforce
Foundations learners, developers, operators and architects with responsible-AI expectations built into every one of them, not treated as a separate course.
02 - Proven
Auto-evaluated hands-on labs
Every track puts people on real work on the target stack, and evaluates what they produce. Capability is demonstrated, not assumed.
03 - Measured
A live capability map
Every result becomes skill evidence and rolls up into a map your organisation can act on – for hiring, placement, project staffing and upskilling.
How it works
From learning to evidence.
Tekstac is built the other way round from a content platform. Every track starts with real work and ends in proof. This is the path a single learner takes and where their result lands in your capability map.
01
Hands-on lab
A real task on the target stack — not a quiz.
02
Auto evaluation
Functional, quality, security and judgment checks on what they built.
03
Skill evidence
Observable proof of what the learner can actually do.
04
Role readiness
Ready, near-ready, or needs intervention.
05
Capability intelligence
A team- and enterprise-level map you can act on.
Not a course library. An instrument for workforce decisions.
Capability Intelligence MAP
Stop guessing who's ready.
Because every result is measured against the same role-readiness standard, individual evidence rolls up into a single live map – who can be deployed, who’s close, and where the bench is thin, by team, role and stack. Every training investment then lands where it changes deployability.
Ready
People who can be deployed on GenAI work now.
Near-ready
People who need targeted practice or mentoring to get there.
Gap areas
Team-level skills where the bench is thin, and hiring or upskilling is needed.
Ten tracks. Every GenAI role, from foundations to architect.
Each track is hands-on and auto-evaluated on your real stack. Architect tracks are assessed through scenario and case-study evaluation – design judgment, not coding labs.
01
GenAI Foundations
Everyone · the shared floor
Prompting · RAG · embeddings & vector stores · context engineering · RAGAS · MCP
Proof · Builds a working RAG pipeline on an unfamiliar corpus and judges its output quality.
02
AI-Native Developers
Every developer
GitHub Copilot · Claude Code · Cursor · Codex · MCP
Proof · Applies coding agents across the SDLC while verifying every AI-written change.
03
AI for Java Developers
Java / Spring teams
Spring AI · LangChain4j · Spring Boot · vector stores · MCP
Proof · Ships AI-enabled features on the Java / Spring stack to production standard.
04
AI for .NET Developers
.NET / Azure teams
Semantic Kernel · Microsoft Agent Framework · Azure AI Foundry
Proof · Ships secure agentic features on the .NET / Azure stack.
05
AI for JavaScript / Full-Stack
React / Angular / Node teams
Vercel AI SDK · LangChain.js · Mastra · React / Next streaming · MCP
Proof · Ships AI-powered full-stack features with typed, tested agent flows.
06
Agentic AI
Low-code to production
n8n · Langflow · Flowise → LangGraph · CrewAI · MCP · A2A · LangFuse
Proof · Designs and productionizes multi-agent systems with observability and control boundaries.
07
LLMOps / ModelOps
Those who run it in production
LiteLLM gateway · LangFuse / LangSmith · eval pipelines · cost & drift monitoring · Bedrock / SageMaker
Proof · Runs GenAI in production — gateway, observability, cost and regression control.
08
AI Quality & Validation
QA / eval engineers
DeepEval · golden datasets · LLM-as-judge · hallucination & grounding detection · CI/CD quality gates · RAGAS
Proof · Quality-gates any LLM feature before release and reports findings to stakeholders.
09
GenAI Solution Architect
Case study · designs the right solution
Use-case selection · RAG vs fine-tune vs agentic · build-vs-buy · cost / latency / quality trade-offs
Proof · Selects the right pattern per use case and defends cost, latency and quality trade-offs.
10
GenAI Platform Architect
Case study · designs the platform
Reference architecture · model gateways & routing · agent infra (MCP, A2A) · security · cost governance
Proof · Designs reusable platform architecture that standardizes how teams build and run GenAI.
Trust Layer — Security + Responsible AI
Embedded · secures every track, build to design
OWASP Top-10 for LLMs · prompt-injection defence · safe agent permissions · guardrails · red-teaming · bias, transparency and explainability – evaluated inside every track, not bolted on as a separate module.
Tracks combine into the roles you actually staff.
| Sample Role | Track Combination | Capability Evidence |
|---|---|---|
|
AI-Enabled Developer
|
Foundations + AI-Native Developers + Trust Layer
|
Uses AI coding assistants safely, drives multi-file changes, reviews generated code and verifies output before merge.
|
|
GenAI Application Developer
|
Foundations + Java / .NET / JavaScript + Trust Layer
|
Builds RAG or AI-enabled features on the enterprise stack with evaluated functionality, quality and safety.
|
|
Agentic Workflow Developer
|
Foundations + Agentic AI + Trust Layer
|
Designs multi-step agentic workflows, defines tool permissions, adds guardrails and validates workflow behaviour.
|
|
LLMOps Engineer
|
Foundations + LLMOps / ModelOps + Trust Layer
|
Operates gateways, eval pipelines, spend tracking, drift monitoring and production reliability controls.
|
|
GenAI Architect
|
Foundations + Architect case study + LLMOps + Trust Layer
|
Chooses the right pattern and defends the trade-offs, then designs reusable platform architecture with routing, observability and governance.
|
Sample mappings, not fixed job descriptions. Role-readiness criteria tune by business unit, project stack, cloud provider and maturity level.
Start here
Prove it in 30 days.
1
Pick one role
Java, .NET, full-stack, AI-native developer, LLMOps or an architect audience.
2
Map the stack
Align labs to your cloud, frameworks, tools and governance expectations.
3
Run hands-on proof
Learners complete auto-evaluated labs and case studies with repeatable scoring.
4
Review the map
Identify ready, near-ready and intervention-needed talent by team, role and skill.
One role. One real stack. One measurable baseline – and a clear view of who’s ready, and who’s not.
Map your GenAI workforce capability with Tekstac.
Book a capability walkthrough. We’ll take one role on your real stack and show you the capability intelligence map.
