Atomic AoL replaces weeks of manual compilation with a guided web wizard and an agent pipeline that executes the whole AACSB Standard 5 workflow — under human control at every decision that matters.
Your institution gets an isolated workspace. Every row of data is stamped with your tenant ID server-side — agents cannot read or write across institutions.
An agent ingests the AACSB standard text and indexes it semantically, so every later judgement can cite the standard it serves.
Agents read your public programme pages and module descriptions, extract qualifications and learning outcomes, and propose the mapping from competency goals to modules.
Grades and indirect measures (alumni surveys, exit surveys, employer feedback) arrive as simple CSV files — templates included, validation messages in plain language.
Proposed competency goals wait in an approval queue. Nothing proceeds until a named human approves — and the approval is recorded with name and timestamp.
One click. Layer 2 computes cohort aggregates, multi-year trends and benchmark breaches; Layer 3 writes the narrative and signs the bundle. Minutes, not weeks.
Scraper, AACSB seeder, competency-goal generator, modules reader, outcome mappers, coherence gate. They build the curriculum graph the analysis stands on.
Grade collector, cohort aggregator, trend computer, benchmark comparator, narrative-arc balancer and colleagues. Deterministic maths where maths belongs; AI judgement only where judgement is needed.
Executive summariser, evidence narrator, gap flagger, report stitcher, accreditation packager. Output: a typeset PDF plus the evidence trail that backs every sentence.
| What you provide | What the pipeline produces |
|---|---|
| Programme / module URLs (public pages) | Qualification & learning-outcome graph |
| Grades CSV (per cohort, per year) | Cohort aggregates + multi-year trends per competency goal |
| Indirect measures CSV (surveys) | Indirect vs direct alignment analysis |
| Benchmark policy (or let the AI propose one) | Breach analysis with severity grading |
| ~30 minutes of your attention | Signed submission bundle: REPORT.PDF + EVIDENCE.JSONL + SHA-256 MANIFEST |
Atomic AoL is built around approval gates, not around autonomy. Competency goals — the intellectual anchor of the whole report — are blocked until someone with a name approves them. The same pattern guards key analytic judgements. This is what makes the output defensible in front of a peer-review team: every claim has a human who said yes, and a timestamp that proves when.
We demo the full pipeline on "Hogwarts Business School" — six years of synthetic data, so you see real mechanics with zero privacy concerns.
Book a pilot → Book a call →