Majlis Academy

How · From capability gap to a team that runs it

How we work.

What we offer, to whom, and how we build it. The programmes and formats, the frameworks we teach, the eight-stage method, the teams and technology behind it, and the path to hand-over.

Choose a first programme See the method
Fig. 01 · Release gateFive gates · five decisions Agents prepare · people decideThe package reaches learners

01 · Audiences

Audiences

Three audiences, each with its own way in.

Each audience gets a different format. All of them learn from the same reviewed knowledge, so leaders and teams use one vocabulary.

  • #1 Leaders

    CXO, ExCo and board members

    Decide on AI with a shared vocabulary. Briefings use only human-reviewed knowledge, frame the decision, and show the regulatory context.

    FormatsExecutive briefings · steering-committee teach-ins

  • #2 Practitioners

    Architects, engineers and delivery teams

    Build and run AI, cloud and data platforms under regulation. Learners prove skills in graded labs, capstones and hackathons on synthetic sandbox data.

    FormatsBootcamps · labs · capstones · hackathons · accelerators

  • #3 Capability owners

    L&D, CoE leads and knowledge stewards

    Run the factory yourselves. We transfer the Brain, the skills and the gates, and we train your people in the roles that operate them.

    RolesKnowledge Steward · Learning Architect · Facilitator

Courses · Available soon

GovKit Core, then a sector track for government, banking or telco.

See the courses

02 · Formats

Formats

Seven formats, from a short briefing to an accelerator.

Each format ends with evidence of learning. We choose the format from the objective, not from habit.

FormatForShapeEvidence of learning
Executive briefingCXO, ExCo, board45–90 minutes · Wardley map and ArchiMate overview · regulatory contextOne decision exercise
Technical bootcampArchitects, engineersMicro-lessons of 6–12 minutes, cases and labs, classroom or hybridGraded labs and TILT assessments
Hands-on labEngineersLab environment as infrastructure as code · synthetic dataDeterministic attester grades the result
CapstoneBootcamp graduatesChallenge brief tied to objectives · milestone gates · mentorsRubric review and demo day
HackathonCross-functional teamsOne to three days · sandbox data · judging rubricWorking solution and demo
Accelerator trackA cohort with a real use caseCohort over several weeks · mentor plan · milestone gatesGate reviews toward a pilot
Online micro-learningAny audience6–12 minute units on your LMS or LXPRetrieval quizzes and item analysis

03 · Content

Content

We teach with each industry's own frameworks.

Each sector has its own reference frameworks: BIAN for banking, TM Forum for telco, TOGAF and ArchiMate across government and the enterprise. We use them as the language of the course.

  • #1 Banking

    BIAN service domains

    Map AI and data use cases to the BIAN service landscape and to the controls a banking regulator expects.

    • BIAN
    • Model risk

  • #2 Telco

    TM Forum ODA and Frameworx

    Work with ODA components and their widely used predecessors: eTOM processes, the SID information model and the TAM application map.

    • ODA
    • eTOM
    • SID
    • TAM

  • #3 Enterprise architecture

    TOGAF, Well-Architected and ArchiMate 3.2

    Describe target states formally. Every architecture exercise uses ArchiMate notation and Well-Architected review questions.

    • TOGAF
    • WAF
    • ArchiMate

  • #4 Strategy

    Wardley mapping and systems thinking

    See the landscape before you choose a move. Map value chains, evolution and feedback loops in complex systems.

    • Wardley maps
    • Systems thinking

  • #5 Engineering

    Cloud, data and AI operations

    DevOps, DataOps, MLOps, LLMOps, AIOps and ScienceOps as standards. Labs in Python, TypeScript, Go and Rust. Documentation as code.

    • MLOps
    • LLMOps
    • Docs as code

  • #6 Governance

    AI regulation and standards

    Apply GCC regulator guidance with the EU AI Act, NIST AI RMF and ISO/IEC 42001 as reference frames for controls.

    • ISO/IEC 42001
    • NIST AI RMF

04 · Method

Method

One value stream, eight stages, five human decisions.

Every programme moves through the same stages, from a change in the world to a renewed package. Agents do the production work. People make the decisions. Open a stage to see what happens.

Build the package

Stages 1 to 4

  • 01Sense · Watch for change

    Signals arrive: a new regulation, a technology release, a client request or a learner-analytics finding.

    SkillsFreshness Sentinel · Learning analytics advisor

    Exit criterionThe need and its trigger are logged.

  • 02Curate · Sources to concepts

    Sources become Domain Concepts with provenance and a trust tier. A Knowledge Steward verifies each one.

    SkillsKnowledge harvester

    Gates and decisions

    • D1

    Exit criterionEvery concept the package needs exists, has sources and is stable.

  • 03Design · Backward design

    Objectives at a stated Bloom level, then the evidence, then the activities. Pacing follows cognitive-load limits.

    SkillsBackward-design architect · Cognitive-load optimiser · Transparent-assessment generator

    Gates and decisions

    • G2
    • D2

    Exit criterionThe alignment check passes and the client sponsor accepts the plan.

  • 04Produce · Assets per objective

    Agents draft scripts, storyboards, cases, labs as code, briefings, rubrics and capstone briefs. Production engineers edit them.

    SkillsScript and storyboard writer · Active-learning scenario builder · Lab exercise engineer · Capstone and hackathon designer · Executive briefing composer · Rubric and peer-review designer

    Gates and decisions

    • D4

    Exit criterionEvery objective has its assets.

Run, measure and renew

Stages 5 to 8

  • 05Assure · Five gates

    Scripts validate the bundle, check alignment, lint the language and test each lab grader. The Learning Architect sees one results table.

    SkillsSTE100 checker · Bundle validator · Grader test

    Gates and decisions

    • G1
    • G2
    • G3
    • G4
    • G5
    • D3

    Exit criterionGates G1 to G5 pass and the package is released.

  • 06Deliver · Run the cohort

    Facilitators get run sheets, logistics and contingency plans. Online cohorts get norms, onboarding and weekly prompts.

    SkillsFacilitator kit builder · Community facilitator

    Exit criterionThe cohort runs the package.

  • 07Measure · Evidence back in

    Item, objective and lab results are analysed. Findings go back into the Brain against the concepts they concern.

    SkillsLearning analytics advisor

    Exit criterionFindings are logged against concepts.

  • 08Renew · Rebuild or retire

    Stale concepts are verified again. Packages that depend on them are rebuilt, or retired when demand ends.

    SkillsFreshness Sentinel · Knowledge harvester

    Gates and decisions

    • G5
    • D5

    Exit criterionStale concepts are re-verified and packages rebuilt or retired.

1Factory value streamBusiness process view · eight stages · stage 8 feeds stage 1, so the loop never closes · source: AI Training Factory blueprint. Solid tags are gates, dashed tags are human decisions.

Quality gates before release

Scripts run each gate. A package with a failed gate does not reach a learner.

GateChecksRun by
G1The knowledge bundle is valid against OKF v0.2 and the training profilevalidate_bundle.py
G2Each objective has its evidence and its activitiesalignment_check.py
G3Learner text follows ASD-STE100 and uses exact framework termsste_lint.py + review
G4Each lab grader passes right answers and fails wrong onesgrader_test.py
G5No concept in the package is past its review datestale_report.py

Decisions that stay with people

The factory prepares the inputs and stops. It does not decide for you.

IDDecisionDecided by
D1Accept a concept as stableKnowledge Steward
D2Approve the learning planLearning Architect with client sponsor
D3Release the packageLearning Architect
D4Choose the lab data sourceClient data owner · default synthetic
D5Renew or retire a packageKnowledge Steward and Learning Architect

05 · Operating model

Operating model

Four team types, each with one job.

We organise the factory with Team Topologies. Stream-aligned teams own the programmes for one domain. Three other teams make that work fast and safe.

Enabling · facilitating

  • Learning MethodCoaches teams in backward design, cognitive load and assessment

Stream-aligned teams

  • Banking
  • Telco
  • Government
  • Cloud and AI engineering

Complicated subsystem · collaboration

  • Lab and Assessment EngineeringBuilds labs as code, graders and attesters

Platform · X-as-a-service

  • Factory PlatformBrain, skill runtime, CI gates and lab sandbox, offered as a self-service platform
2Team Topologies viewTeam types and interaction modes · after Skelton and Pais.

Roles and what each one decides

People make every release decision. Agents prepare the inputs. The decisions D1 to D5 are set out in the method.

RoleOwnsDecides
Knowledge StewardThe Brain: sources, trust tiers, review datesD1 · D5
Learning ArchitectDesign and release of each packageD2 · D3 · D5
Production EngineerRuns the skills and edits the assets—
FacilitatorDelivery in class, online and at hackathons—
Client sponsorThe capability need and the budgetD2
Client data ownerData classification and lab data rulesD4

How the teams interact

Three interaction modes keep the cognitive load of each team low.

ModeBetweenWhat happens
X-as-a-servicePlatform → stream teamsTeams use the Brain, skills and gates without a ticket.
FacilitatingLearning Method → stream teamsCoaches join a team for a design, then step out.
CollaborationLab Engineering ↔ stream teamsThey build one lab and its grader together, for a set time.

06 · Technology

Technology

An AI Training Factory built on knowledge as code.

The Brain is one Open Knowledge Format (OKF) v0.2 bundle in Git. Seventeen agent skills turn its concepts into learning packages. CI runs the gates on every pull request.

In scopeClient contextArtefactFocal component Notation: ArchiMate 3.2, monochrome
3Layered viewArchiMate 3.2 · simplified for the web · source: ai-training-factory.archimate.xml

  • #1 Knowledge as code

    Every fact has a source and a review date.

    Concepts are Markdown files with front matter. Each one records its sources, trust tier and the date after which it is stale.

  • #2 Freshness

    Change finds its own impact.

    The Sentinel compares review dates with today and follows links to every package that uses a stale concept.

  • #3 Attested labs

    Graders that do not guess.

    Lab graders are deterministic code, not an LLM. They are tested against right and wrong answers before release.

  • #4 Agent skills

    Seventeen skills, one per job.

    Skills harvest knowledge, design syllabi, write scripts, build labs and check language. A router skill stops at each human decision.

An OKF concept · Illustrative

---
id: bian-service-domain
type: domain-concept
status: stable
sources:
  - BIAN Service Landscape
trust_tier: primary
stale_after: 2027-04-30
verified_by: human:steward-01
---
# BIAN service domain
A service domain is one discrete
business capability. It has one
control record and one owner.

Field names are simplified for this page. The OKF v0.2 training profile defines the exact schema.

07 · Adoption

Adoption

A pilot proves the factory before it scales.

Each client moves through four phases. A gate review closes each phase, so you decide to continue with evidence in hand.

  1. Phase

    0

    Foundation

    • Brain repository and OKF training profile
    • Core skill set
    • CI gates G1 to G5

  2. Phase

    1

    Pilot

    • One learning stream
    • One bootcamp
    • One hackathon

  3. Phase

    2

    Scale

    • More learning streams
    • Freshness Sentinel runs on a schedule
    • Integration with your LMS or LXP

  4. Phase

    3

    Transfer

    • Your teams own the Brain
    • Factory runs as an internal service
    • We coach only
4Adoption roadmapImplementation and migration view · phase lengths set per client.

08 · Measures

Measures

We measure the gap closing, not hours trained.

Each measure comes from data the factory already records. Findings go back into the Brain against the concept they concern.

MeasureWhat it tells usSource
Lead time from change to trained staffHow fast a new rule or release reaches people who must apply itBrain log and cohort dates
Objective attainmentThe share of learners who show each objective in the evidenceAssessments, lab receipts, rubrics
Gate pass rate on first runThe quality of production before reviewCI results for G1 to G5
Stale concepts in live packagesContent risk. The target is zero.Freshness Sentinel report
Work owned by client teamsProgress toward hand-overGit history and release approvals

Work with us

Choose a first programme.

Tell us the audience and the capability gap. We propose the format, the first package and the path to run it in-house.

Choose a first programme See the courses Visit majlisofai.com

Email

contact@majlisofai.com