# Compilationloom

*/Occupations/Engineering_and_Architecture_Teachers,_Postsecondary/Problems/ABET_Accreditation_Data_Collection/Startups/Compilationloom*

## Startup Overview

Engineering and architecture faculty face immense administrative friction mapping student coursework to continuous ABET accreditation outcomes. Department chairs and assessment coordinators routinely chase down instructors to collect, anonymize, and evaluate high, average, and low performance artifacts. Because accreditation demands granular proof of specific proficiencies rather than overall course grades, professors end up double-grading assignments and manually extracting question-level data from their learning management systems.

This software digests multimodal student submissions and translates them directly into compliant ABET proficiency matrices. The system ingests unstructured, domain-specific evidence—ranging from scanned thermodynamics exams to GitHub repositories and CAD files—and autonomously maps individual question components to mandated learning outcomes. It handles the anonymization and categorizes the artifacts to build complete compliance dossiers without requiring human data entry.

Incumbents like Watermark Taskstream, AEFIS, and Canvas LMS force departments into standardized rubrics, turning compliance into an exercise in manual extraction. By remaining fully format-agnostic across all coursework and driven by autonomous artifact parsing, this platform allows professors to evaluate course comprehension natively. Departments secure continuous, multi-year outcome tracking for accreditation visits while preserving total academic freedom for their faculty.

## Startup Founding Hypothesis

**Problem**: [ABET Accreditation Data Collection](/Occupations/Engineering_and_Architecture_Teachers,_Postsecondary/Problems/ABET_Accreditation_Data_Collection)
**Approach**: a software that autonomously parses standardized course rubrics via vision-language models
**Competitors**:
- [Watermark Taskstream](/Competitors/Watermark_Taskstream)
- [double-grading coursework](/Competitors/double-grading_coursework)
- [manual LMS extraction](/Competitors/manual_LMS_extraction)
**Differentiator2x2**: standardized-rubrics × autonomous-parsing — delivering instant compliance for structured engineering departments

## Startup Solution Coordinate

**Solution**: [Compilation Loom](/Software/Compilation_Loom)

## Startup Offer

**Proof**:
- Engineering Faculty eliminating double-grading by automating question-level proficiency extraction.
- Assessment Coordinators generating complete high/average/low artifact samples without emailing professors.
- Department Chairs replacing multi-month manual data compilation with continuous dashboard tracking.
- Architecture Programs autonomously mapping CAD portfolio submissions directly to accreditation standards.
**Tiers**:
- Name: Pilot Instructor · Price: ~$150–$300/mo · Inclusions: Designed for single engineering faculty members; parses up to 3 active courses per semester, handling scanned PDF exams, Jupyter notebooks, and automated PII redaction.
- Name: Core Program · Price: ~$8k–$14k/yr · Inclusions: Built for assessment coordinators; covers up to 15 courses per semester, continuous tracking of ABET learning outcomes, autonomous high/average/low sample generation, and intended basic LMS extraction.
- Name: Department Wide · Price: ~$18k–$28k/yr · Inclusions: Scaled for full engineering or architecture departments; unlimited active courses, multi-format parsing including proprietary CAD file structures, and automated compliance matrix compilation.
**Guarantee**: If the software cannot accurately map a course's unstructured coursework (including handwritten exams or code repositories) to your provided ABET rubrics, we will manually parse and map that semester's artifacts for you within 72 hours and refund the monthly software fee.
**Business Function**: ProvideService
**Objection Handlers**:
- Our faculty write complex equations and diagrams by hand. -> Compilationloom is designed with vision-language models specifically tuned to accurately transcribe and evaluate handwritten engineering notation.
- We refuse to force our professors into standardized grading rubrics. -> The software adapts to any existing grading scheme, extracting the ABET-relevant proficiencies without altering how instructors grade for comprehension.
- Student privacy regulations prohibit uploading unredacted coursework. -> The system autonomously identifies and redacts all student names, IDs, and PII from artifacts before mapping begins.
- We already pay a massive annual contract for Canvas and AEFIS. -> Compilationloom is designed to sit alongside these tools, doing the unstructured parsing they cannot and exporting the finalized mappings to them.
**Pricing Architecture**: Tiered
**Agent Checkout Support**:
- agentic-commerce-protocol

## Startup Brand

**Voice**: Scholarly and objective, defined by uncompromising technical precision.
**Tagline**: Automated ABET outcome mapping from unstructured student engineering artifacts.
**Icon Concept**: caliper
**Palette Intent**: institutional-cool
**Visual Identity**: Clean layouts grounded in academic slate and oxford blue employ monospace data tables and drafting grids to project rigorous evaluation.
**Archetype Reference**: the-sage

## Startup Proof Points

_Illustrative — target and order-of-magnitude estimate figures, not an achieved track record (this Thing is concept-stage)._

**Pilot Goals**:
- One-semester pilot in a core structural analysis course: Proves the vision-language models can ingest scanned handwritten exams, automatically redact student identifiers, and extract question-level ABET proficiencies without the instructor altering their workflow.
- Two-month retrospective data ingestion across three engineering departments: Demonstrates the platform can parse historical, unstructured CAD and code submissions from local drives to automatically populate a continuous improvement dashboard.
**Target Metrics**:
- target: 100% reduction in secondary grading cycles required to isolate specific ABET proficiencies
- target: 40+ hours of administrative email follow-up eliminated per department per semester
- target: 95% autonomous parsing success rate on unstructured handwritten math, CAD, and code artifacts
- target: 100% automated redaction rate of student PII prior to compliance mapping
**Target Case Studies**:
- Mechanical Engineering Department Chair at a Public Research University: Eliminates faculty double-grading by autonomously mapping handwritten thermodynamics exam PDFs directly to ABET criteria without altering the instructors' native rubrics.
- Assessment Coordinator at a School of Architecture: Replaces manual spreadsheet mapping by extracting specific learning outcomes from unstructured CAD file submissions and automatically curating the required high, average, and low performance samples.
- Computer Science Program Director at a Polytechnic Institute: Bypasses manual question-level LMS extraction by autonomously ingesting student GitHub repositories and Jupyter notebooks to map complex problem-solving proficiencies for accreditation reports.
**Testimonial Targets**:
- Mechanical Engineering Professor: Expresses relief that the software extracts granular learning outcomes from handwritten exams without forcing them to standardize their grading rubrics to match a database schema.
- Department Chair: Validates that continuous, multi-year outcome data populates automatically in the background, eliminating the frantic collection of artifact samples months before an ABET visit.
- Assessment Coordinator: Highlights the platform's ability to ingest messy, unstructured artifact formats and accurately categorize them into the mandated high, average, and low performance bands without manual data entry.

## Startup Solution Stack

- [Accreditation Mapping Service](/Services/Accreditation_Mapping_Service) — Service-as-Software
- [Artifact Parsing Agent](/Agents/Artifact_Parsing_Agent) — Agent
- [Anonymization Worker](/Agents/Anonymization_Worker) — Agent
- [Criterion Alignment Engine](/Software/Criterion_Alignment_Engine) — Software
- [LMS Extraction API](/Software/LMS_Extraction_API) — Software

## Startup Story Brand

**Hero**:
- **Need**: to be the strategic architect of program quality, not the department's compliance secretary
- **Want**: to secure compliant artifact evidence for ABET accreditation without chasing faculty via email
- **Identity**: the assessment coordinator at a university engineering or architecture department
**Plan**:
- Step: Identify targets · Detail: Select the core courses and specific ABET proficiencies you need to substantiate for the current cycle.
- Step: Confirm mapping · Detail: Review the autonomous evidence pulls from Canvas to verify the high, average, and low performance samples.
- Step: Generate dossier · Detail: Export a complete, anonymized outcome report ready for the department's ABET self-study submission.
**Guide**:
- **Empathy**: ABET multi-year outcome dossiers are won in the quiet weeks of the semester — but the reality of fragmented LMS data and non-standardized rubrics makes continuous collection impossible.
**Problem**:
- **Villain**: rubric rigidity
- **External**: Accreditation prep requires manually extracting question-level performance data from Canvas or Blackboard across CAD drawings, code repositories, and handwritten thermodynamics exams.
- **Internal**: You feel like you are policing your colleagues' academic freedom just to fill a spreadsheet.
- **Philosophical**: Subject matter expertise belongs in student mentorship, not in the administrative double-grading of exam scripts.
**Success**: Your department maintains a continuous, audit-ready registry of student proficiency where evidence flows from the classroom to the dossier with zero manual data entry.
**One Liner**: Every semester, engineering faculty struggle with manual ABET evidence collection. Compilationloom parses unstructured student artifacts into compliant outcome data so departments secure accreditation without administrative burden.
**Positioning**:
- **So That**: map unstructured student work to ABET outcomes without standardized rubrics
- **Unlike**: Watermark Taskstream and manual spreadsheets
- **For Whom**: Engineering and architecture assessment coordinators
- **Category**: Autonomous accreditation data software
**Call To Action**:
- **Direct**: Post a course mapping
- **Transitional**: Review sample outcome dossier
**Failure Stakes**:
- Lost accreditation status
- Hundreds of wasted faculty hours
- Incomplete evidence for 2024-2025 audits
**Transformation**:
- **To**: one of the few coordinators who masters continuous assessment
- **From**: an administrator chasing PDFs and spreadsheet rows
**Controlling Idea**: Accreditation data should be an autonomous byproduct of the native teaching workflow.

## Neighborhood

### Composed of

- [Accreditation Alignment Service](/Services/Accreditation_Alignment_Service) — composes · Services
- [Artifact Parsing Agent](/Agents/Artifact_Parsing_Agent) — composes · Agents
- [LMS Extraction API](/Software/LMS_Extraction_API) — composes · Software
- [Criterion Alignment Engine](/Software/Criterion_Alignment_Engine) — composes · Software
- [Anonymization Worker](/Agents/Anonymization_Worker) — composes · Agents

### What it offers

- [Compilation Loom](/Software/Compilation_Loom) — offers · Software

### Embodies

- [Software](/Theses/Software) — embodies · Theses

### Competitors

- [manual LMS extraction](/Competitors/manual_LMS_extraction) — competes with · Competitors
- [double-grading coursework](/Competitors/double-grading_coursework) — competes with · Competitors
- [Watermark Taskstream](/Competitors/Watermark_Taskstream) — competes with · Competitors
- [AEFIS](/Competitors/AEFIS) — competes with · Competitors
- [Anthology Portfolio](/Competitors/Anthology_Portfolio) — competes with · Competitors
- [manual question-level LMS extraction](/Competitors/manual_question-level_LMS_extraction) — competes with · Competitors
- [Blackboard Learn](/Competitors/Blackboard_Learn) — competes with · Competitors
- [Canvas LMS](/Competitors/Canvas_LMS) — competes with · Competitors
- [spreadsheet outcome mapping](/Competitors/spreadsheet_outcome_mapping) — competes with · Competitors
- [AEFIS Software](/Competitors/AEFIS_Software) — competes with · Competitors
- [manual double-grading](/Competitors/manual_double-grading) — competes with · Competitors
- [double-grading assignments](/Competitors/double-grading_assignments) — competes with · Competitors
- [manual spreadsheet outcome mapping](/Competitors/manual_spreadsheet_outcome_mapping) — competes with · Competitors
- [Manual Spreadsheet Mapping](/Competitors/Manual_Spreadsheet_Mapping) — competes with · Competitors
- [manual question-level extraction](/Competitors/manual_question-level_extraction) — competes with · Competitors

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