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Make the whole system work.

The most expensive problems often sit between tools: a learner launches a course but no result arrives, or a manager sees a dashboard that cannot explain the record behind it. We work through those handoffs from requirement to operational test.

Areas of work

Six ways we can help.

These are service capabilities drawn from Mahipal’s implementation experience. The right scope depends on your systems, users and acceptance criteria.

01 /

Learning systems and SCORM

Connect course launch, learner identity, Storyline content, completion and the reports people actually use.

The problem

A course can open successfully while completion, scores or learner identity fail later in the journey.

How we approach it

Map the learner path from the LMS into the course and back. Define which system owns each identifier, status and result, then test the handoffs across repeat attempts and exceptions.

Typical working outputs

  • Launch and completion map
  • Storyline or SCORM integration plan
  • Administrator test scenarios
  • Reporting field specification
02 /

AI practice and coaching

Design guided practice that joins scenario, conversation, evaluation, feedback and trainer review.

The problem

A convincing AI conversation has little training value if an attempt cannot be identified, scored, reviewed and reported reliably.

How we approach it

Define the scenario and evaluation criteria, connect learner and attempt context to the AI service, and make the completion and feedback path testable end to end.

Typical working outputs

  • Scenario and evaluation design
  • Session and attempt flow
  • Feedback and review journey
  • Quality and failure-case checks
03 /

Assessment operations

Make question delivery, learner access, attempts, results and trainer review work as one process.

The problem

Distributed assessment creates operational questions about access, timing, retakes, result accuracy and report ownership.

How we approach it

Translate the assessment policy into a clear user journey, configure the platform and reporting flow, then test the experience for learners and administrators.

Typical working outputs

  • Assessment journey and rules
  • Question and result workflow
  • Trainer review view
  • Operational runbook
04 /

Automation and reporting

Bring APIs, webhooks, forms, spreadsheets and dashboards into a dependable information flow.

The problem

Teams spend time reconciling records when each tool has a different status, identifier or timing.

How we approach it

Identify the source of truth, normalize identifiers and statuses, add retries and diagnostics, then build a report that exposes the work still needing attention.

Typical working outputs

  • Data and API map
  • Status and retry rules
  • Automation workflow
  • Management reporting view
05 /

Digital learning content

Turn complex processes into clear courses, application walkthroughs, explainer films and demonstrations.

The problem

Training content fails when the script, screen actions and actual process disagree.

How we approach it

Study the workflow first. Write the narration and on-screen content together, storyboard the interaction, and review the finished media against the real process.

Typical working outputs

  • Process-led script
  • Storyboard and visual plan
  • Course or video production
  • Review and update notes
06 /

Technical delivery and governance

Bridge requirements, vendors, security questions, rollout testing and support after launch.

The problem

A prototype becomes difficult to operate when access, data handling, documentation and ownership are left until the end.

How we approach it

Define the decision makers, data flow, access rules and acceptance tests early. Keep troubleshooting evidence and operational handover part of delivery.

Typical working outputs

  • Requirements and architecture brief
  • Vendor and security questions
  • Acceptance checklist
  • Support and handover notes

Delivery method

From a messy process to a tested flow.

We keep the first scope narrow enough to inspect and verify, then expand only where the evidence supports it.

  1. 01

    Understand the real journey

    Talk to the people doing the work, inspect the existing tools and write the end-to-end path in plain language.

  2. 02

    Define ownership and data

    Agree which system owns identity, status, score, feedback and the final record. Map exceptions before building.

  3. 03

    Build a focused working slice

    Connect only the necessary interface, API, data and report pieces for the first useful result.

  4. 04

    Test the handoffs

    Check normal, repeat, delayed and failed paths with representative test data. Compare what the learner, trainer and manager see.

  5. 05

    Prepare for ownership

    Document operational checks, access, support responsibility and what to do when the connected service changes.

Start with one workflow

Show us the handoff causing work.

Share the systems involved and what a successful result would look like. We can then define a sensible first scope.

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