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Hacktivate for business

Build practical AI capability across your organisation.

Build the understanding and practical skills your workforce needs to use AI effectively, evaluate its output, and make responsible decisions. Every learner demonstrates capability through hands-on challenges, not passive course completion.

Discuss a pilot Explore the training

From £25 per seat, per year.

135marked AI challenges built around practical decisions
LLMs to agentsPrompting, multimodal systems, RAG, tools, and governance
Evidence built inReadiness trends, exports, and training records
From £25per seat, per year

AI capability in context

Build skills that hold up in real work.

Start with practical AI decision-making, connect it to the security risks surrounding modern tools, and add Python depth for teams working with automation, data, or generated code.

Core AI training

Understand how AI works – then learn to use it well.

Ten modules move from models, prompting, and generated media through evaluation, data, bias, security, RAG, tools, and agents. No technical background is required, and every challenge turns understanding into practical capability.

10 modules135 challengesMarked, not self-reported

Prompting with purpose

Set clear goals, use context, examples, constraints, and model settings effectively, then define how the result will be evaluated.

Evaluating AI output

Identify hallucinations, unsupported confidence, hidden assumptions, and contradictions, then verify important claims before acting.

Generated media and code

Create and evaluate AI-generated images, speech, video, code, and multimodal content, checking quality, authenticity, and suitability before use.

Data, bias, and evaluation

Understand how training data and representation shape results, then assess accuracy, bias, and failure patterns using meaningful measures.

Secure and responsible AI use

Protect confidential data, identify prompt-injection attacks and privacy risks, respect copyright, and understand when human oversight and accountability are required.

Advanced AI workflows

Design and evaluate workflows using retrieval, embeddings, chunking, tool calling, agents, and orchestration – while knowing when a simpler approach is better.

Verify before actingIdentify hallucinations, unsupported claims, and hidden assumptions before they influence a decision.
Know when RAG earns its costCompare retrieval, chunking, and embeddings with a simpler prompt, adding complexity only when it improves the result.
Protect confidential informationKnow what should stay out of AI systems, and identify prompt-injection, privacy, copyright, and accountability risks.

Cybersecurity awareness

Build safer decisions into everyday work.

Teams investigate how weak passwords, exposed data, websites, files, and networks create risk, developing the awareness to spot suspicious activity and make safer choices.

314 challenges5 difficulty levelsSafe browser labs

Identity and data security

Investigate passwords, hashes, hidden data, metadata, archives, and file evidence to understand how information is protected or exposed.

Web and network security

Examine browsers, headers, cookies, DNS, protocols, ports, and network traffic to understand how systems communicate and where risks appear.

Confident technical investigation

Use command-line tools, inspect process and network activity, research unfamiliar systems, and solve problems methodically.

Understand how attacks work

Explore how weak assumptions are exploited inside controlled challenges, without targeting real systems or exposing real data.

Optional technical depth

Practical Python for automation, data, and AI-generated code.

Eleven structured modules build confidence with code, automation, data processing, APIs, and the critical evaluation of AI-generated Python.

583 challenges11 modulesReal Python

Clear, reliable Python

Write readable programs using sound logic, reusable functions, and structured data.

Automate routine work

Build reliable, reusable tools that process data, work with files, and connect to APIs.

Tackle complex problems

Develop dependable solutions using object-oriented design, algorithms, testing, and systematic debugging.

Reviewing AI-written code

Read, run, test, question, and improve AI-generated Python instead of trusting it on sight.

Evidence leaders can act on

See strengths, progress, and support needs across your workforce.

Hacktivate brings challenge progress, readiness trends, and training records into one clear view, helping leaders target support and plan what comes next.

Track AI readiness over time

Follow a transparent score combining participation, module coverage, capability breadth, and independent problem-solving, with dated snapshots that show progress.

Understand individual progress

Review each learner's completed challenges, hint use, module progress, and recent activity to identify strengths and support needs.

Export the detail you need

Download summary or detailed CSV reports for internal analysis, targeted follow-up, and record keeping.

Document AI literacy

Export activity by learner, module, date, and AI literacy theme, mapped to EU AI Act Article 4. Records support governance and oversight without claiming legal compliance.

Maintain a clear audit trail

Review important account and organisation actions from one central administrative view.

Flexible rollout

Start focused, then scale with evidence.

Choose the teams and capabilities that matter, configure the right training mix, and use participation and challenge progress to guide expansion.

Fast learner onboardingBulk-import company email addresses, then let each learner securely configure their own account.
Flexible access controlsChoose available courses, organise learners into groups, and manage hints and progress visibility.
One view across every courseMonitor learner progress across AI, Cybersecurity, and Coding from a single administrator dashboard.
Evidence to guide next stepsUse readiness trends, individual progress, exports, and training records to decide where to focus next.

Plan a focused pilot

Start with a clear capability goal.

Choose the teams, skills, and outcomes that matter most, then align the initial training mix and reporting around them.

  1. 1

    Choose the starting cohort

    Select the group, define the business context, and set clear capability priorities.

  2. 2

    Select the training mix

    Configure access and choose AI, Cybersecurity, Coding, or a focused combination.

  3. 3

    Review progress and evidence

    Use readiness trends, learner activity, exports, and team feedback to decide what comes next.

Build capability through practice

Build an AI-capable, security-aware workforce.

Start with the teams, priorities, and training mix that matter most to your organisation.

Discuss a pilot View pricing