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AI & Machine Learning / Case study

BriefRoom

Turn the documents a team already has into interactive lessons

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In short

BriefRoom converts existing PDFs and Word documents into interactive, narrated lessons with an AI-led classroom, without tying the product to one AI provider.

Status
Live
State
Private product
Input
PDF · DOCX
Output
Interactive lessons
Built with
Elixir / Phoenix / React

The brief

The problem

Most organisations already have their training content: policies, manuals, onboarding decks. It sits in documents that people skim once and forget. Turning it into real courses usually means an instructional designer, an authoring tool and weeks per course.

What we did

Our approach

We built a platform that does the conversion. A team uploads its documents, and BriefRoom produces a structured, interactive lesson from them. Learners work through it in an AI-led classroom with narration, and teams can export the result. The AI providers are interchangeable, so the product is not tied to one model vendor.

Features

What it does

  • PDF and Word upload

  • Automatic lesson structure

  • AI-led classroom

  • Narrated lessons

  • Exports

  • Interchangeable AI providers

Results

What happened

  • Documents in, interactive lessons out: no authoring tool required
  • AI-led classroom with narration for each lesson
  • Lessons exportable for use outside the platform
  • Not locked to a single AI provider

Services

What this work shows we do

  • AI automation

    Models wired into the tools your team already uses, with logs and limits.

    Service page

  • Custom software

    Software built around how the business works, and run after launch.

    Service page

Related work

Built with the same services

Other work of ours that shares this project's services.
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  • ButlerCRM, running
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    ButlerCRM

    ButlerCRM keeps contacts, deals, email, calendar and tasks in one workspace that updates live for everyone in it. We rebuilt it on Elixir/Phoenix so changes appear for the whole team as they happen.

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How we work

How a change reaches production

The same five gates for our own products and for yours. Nothing reaches your customers on the strength of a demo.

  1. 01 · Isolate

    Its own branch and workspace

    Every change starts in a separate copy of the code, so work in progress never touches what is live.

    git worktree add .worktrees/your-change
  2. 02 · Build

    Engineers with AI agents

    Agents write and test code beside us. They hold no passwords: every call they make goes through our gateway, which checks it and logs it.

    agent → gateway → policy · audit · call
  3. 03 · Prove

    Evidence, then review

    Tests, before-and-after screenshots of the running app, then independent reviewers. It does not move on until no serious finding is left.

    review: 0 high · 0 medium → pass
  4. 04 · Ship

    Deployed and checked live

    The pipeline builds and deploys. Then we check the running service itself, not just a green build.

    replicas 1/1 · image sha matches · health 200
  5. 05 · Watch

    Checked every night

    Each integration we run is tested against its provider every night, so a changed API is caught before your team notices.

    nightly: each integration vs its provider

Want something built like this?

Tell us what your team does by hand and the tools involved. AI automation is where most projects like this start.
Get in touch (954) 834-3426