# Direvaluate

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

## Startup Overview

This software ingests unstructured student assignments and maps them directly to ABET accreditation criteria. Engineering and architecture faculty continuously gather, anonymize, and evaluate high, average, and low performance artifacts across fragmented formats—from handwritten thermodynamics exams to CAD files and GitHub repositories. Without native extraction tools, assessment coordinators rely on relentless email follow-ups to compile this evidence into centralized spreadsheets, creating severe administrative bottlenecks prior to an accreditation visit.

The platform parses these diverse artifact types natively, without forcing departments to standardize their grading rubrics. It evaluates scanned PDFs, structural analysis code, and proprietary design files, isolating the specific components that demonstrate mandated proficiencies like complex problem-solving. By autonomously extracting and redacting this evidence in the background, the application eliminates the need for professors to double-grade coursework for both student feedback and institutional compliance.

Legacy assessment suites like Watermark Taskstream and AEFIS operate as rigid databases requiring manual data entry and strict rubric conformity. Likewise, manual LMS extraction forces faculty to hand-pull question-level scores to prove individual competencies. By remaining format-agnostic across all submission types and executing outcome mapping autonomously, this system allows instructors to maintain their native grading workflows while generating continuous, multi-year compliance dossiers.

## Startup Founding Hypothesis

**Problem**: [ABET Accreditation Data Collection](/Occupations/Engineering_and_Architecture_Teachers,_Postsecondary/Problems/ABET_Accreditation_Data_Collection)
**Approach**: a software that lets faculty manually tag unstructured student artifacts
**Competitors**:
- [Watermark Taskstream](/Competitors/Watermark_Taskstream)
- [AEFIS](/Competitors/AEFIS)
- [spreadsheet outcome mapping](/Competitors/spreadsheet_outcome_mapping)
**Differentiator2x2**: format-agnostic and manually extracted, avoiding standardized rubrics and autonomous parsing

## Startup Solution Coordinate

**Solution**: [Proficiency Vault](/Software/Proficiency_Vault)

## Startup Offer

**Proof**:
- Engineering programs: target zero manual double-grading for accreditation compliance
- Architecture departments: aim to automatically parse and redact multi-page visual studio portfolios
- Computer Science faculty: designed to map GitHub commits to specific ethical responsibility criteria
**Tiers**:
- Name: Per Course · Price: ~$400–$800 per course evaluated · Inclusions: Evaluation of up to 1,000 unstructured student artifacts per semester (including handwritten exams, CAD files, and code), automated PII redaction, and direct export of ABET outcome matrices.
- Name: Department Block · Price: ~$12,000–$15,000 per academic year · Inclusions: Pre-paid capacity for up to 30 courses per year, intended API connections for Canvas or Blackboard, and autonomous collation of high, average, and low performance samples for department-wide reporting.
**Guarantee**: Direvaluate maps raw, unstructured student work to ABET learning outcomes without requiring faculty to alter their grading rubrics. If the software cannot parse a supported assignment format like a handwritten exam or code repository, we manually complete the outcome mapping for those artifacts within 48 hours.
**Business Function**: ProvideService
**Objection Handlers**:
- Faculty refuse to change their grading rubrics: Direvaluate reads the raw artifacts and maps them independently, requiring zero changes to existing course grading structures.
- FERPA prevents uploading unredacted exams to third parties: The system is designed to strip student personally identifiable information locally before cloud outcome processing.
- Visual formats like CAD and handwritten math are too complex to parse: The extraction engine uses multimodal vision-language models specifically intended to interpret technical engineering formats.
**Pricing Architecture**: UsageMeter
**Agent Checkout Support**:
- agentic-commerce-protocol

## Startup Brand

**Voice**: Institutional academic register grounded in strict evidentiary precision.
**Tagline**: Maps unstructured engineering assignments directly to ABET learning outcomes.
**Icon Concept**: Caliper
**Palette Intent**: institutional-cool
**Visual Identity**: A stark palette of drafting-paper white, blueprint blue, and institutional navy pairs with monospaced typography to reflect the precision of engineering schematics and accreditation dossiers.
**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**:
- Single-semester, 5-course engineering pilot: Prove the system can automatically redact PII and extract ABET learning outcomes from handwritten PDF exams and code repositories with at least 95% accuracy, requiring zero changes to faculty rubrics.
- Full academic year, single-department rollout: Validate the autonomous collation of high, average, and low performance samples across 40 concurrent courses, generating a complete continuous improvement report without a single manual data entry task.
**Target Metrics**:
- target: 0 hours spent by faculty double-grading coursework for accreditation compliance
- aim: 100% elimination of end-of-semester faculty artifact-chasing emails
- target: >95% accurate ABET criteria extraction from unstructured formats (handwritten exams, CAD, code)
- aim: 90% reduction in hours required to compile multi-year outcome data for accreditation reports
**Target Case Studies**:
- Large public research university, Mechanical Engineering Department Chair: Transforms end-of-semester manual artifact chasing and manual redaction into an automatically collated, PII-stripped repository of high, average, and low student performance samples mapped directly to ABET criteria.
- Mid-sized private engineering college, Assessment Coordinator: Replaces weeks of manual spreadsheet data entry and LMS extraction with a continuous multi-year outcome dashboard, generated autonomously from non-standardized faculty grading rubrics.
- Polytechnic institute, Architecture Professor: Eliminates the need to double-grade complex visual submissions, allowing the software to natively parse unstructured CAD drawings and map the internal components to specific program proficiencies.
**Testimonial Targets**:
- Engineering Department Chair: Relief that faculty no longer complain about altering their preferred teaching and grading styles just to feed a rigid accreditation database.
- Assessment Coordinator: Confidence in having a complete, audit-ready continuous improvement dataset, with high/average/low samples automatically pulled from raw coursework rather than relying on last-minute faculty compliance.
- Engineering Faculty Member: Satisfaction that they grade handwritten thermodynamics exams and GitHub code repositories exactly once, while the software handles the granular, question-level compliance mapping in the background.

## Startup Solution Stack

- [Artifact Assessment Service](/Services/Artifact_Assessment_Service) — Service-as-Software
- [Multimodal Ingestion Agent](/Agents/Multimodal_Ingestion_Agent) — Agent
- [Identity Redaction Agent](/Agents/Identity_Redaction_Agent) — Agent
- [Criterion Alignment Worker](/Agents/Criterion_Alignment_Worker) — Agent
- [Gradebook Extraction API](/Software/Gradebook_Extraction_API) — Software
- [Proficiency Matrix SDK](/Software/Proficiency_Matrix_SDK) — Software

## Startup Story Brand

**Hero**:
- **Need**: to protect academic freedom while substantiating program quality to ABET reviewers
- **Want**: to gather student learning evidence without chasing faculty for manual spreadsheet mapping
- **Identity**: the engineering department chair or assessment coordinator
**Plan**:
- Step: Upload Artifacts · Detail: Submit raw student work exports from Canvas, Blackboard, or local directories in any file format.
- Step: Check Alignments · Detail: Review the autonomous mapping of individual exam questions to specific ABET criteria for accuracy.
- Step: Export Tables · Detail: Download formatted accreditation reports with anonymized evidence samples ready for the self-study.
**Guide**:
- **Empathy**: When the accreditation cycle begins, you discover that critical evidence of student proficiency is trapped in local hard drives and unparsed GitHub repositories.
**Problem**:
- **Villain**: standardized rubric mandates
- **External**: Maintaining ABET compliance requires instructors to manually double-grade assignments and extract question-level scores from Canvas LMS or handwritten thermodynamics exams.
- **Internal**: You feel like an administrative clerk begging colleagues for PDFs instead of an academic leader.
- **Philosophical**: Why should engineering faculty accept administrative data entry when their expertise belongs in the lab and classroom?
**Success**: Your self-study is backed by precise, question-level evidence from every course, compiled without a single manual data-entry hour.
**One Liner**: ABET Accreditation Data Collection costs engineering departments thousands of hours in manual data entry. Direvaluate autonomously maps unstructured student work to learning outcomes so faculty can focus on teaching.
**Positioning**:
- **So That**: map unstructured artifacts to ABET outcomes without mandating standardized rubrics
- **Unlike**: Watermark Taskstream and manual spreadsheet mapping
- **For Whom**: engineering and architecture assessment coordinators
- **Category**: Autonomous accreditation software for engineering departments
**Call To Action**:
- **Direct**: Submit a course
- **Transitional**: View sample artifact registry
**Failure Stakes**:
- Loss of ABET accreditation status
- Hundreds of hours in faculty burnout
- Gaps in multi-year continuous improvement data
**Transformation**:
- **To**: mentoring faculty through program improvement instead of chasing artifacts
- **From**: an email-chasing coordinator redacting PDFs by hand
**Controlling Idea**: Accreditation evidence should be a byproduct of teaching, not a separate labor.

## Neighborhood

### Competitors

- [Scale AI](/Competitors/Scale_AI) — competes with · Competitors
- [Arize AI](/Competitors/Arize_AI) — competes with · Competitors
- [Manual Red Teaming](/Competitors/Manual_Red_Teaming) — competes with · Competitors
- [Patronus AI](/Competitors/Patronus_AI) — competes with · Competitors
- [Hugging Face Evaluate](/Competitors/Hugging_Face_Evaluate) — competes with · Competitors
- [Watermark Taskstream](/Competitors/Watermark_Taskstream) — competes with · Competitors
- [spreadsheet outcome mapping](/Competitors/spreadsheet_outcome_mapping) — competes with · Competitors
- [AEFIS](/Competitors/AEFIS) — competes with · Competitors
- [AEFIS Platform](/Competitors/AEFIS_Platform) — competes with · Competitors
- [Canvas LMS](/Competitors/Canvas_LMS) — competes with · Competitors
- [manual LMS extraction](/Competitors/manual_LMS_extraction) — competes with · Competitors
- [Manual Double-Grading](/Competitors/Manual_Double-Grading) — competes with · Competitors
- [Blackboard Learn](/Competitors/Blackboard_Learn) — competes with · Competitors
- [manual spreadsheet mapping](/Competitors/manual_spreadsheet_mapping) — competes with · Competitors
- [Anthology Portfolio](/Competitors/Anthology_Portfolio) — competes with · Competitors
- [manual double-grading workflows](/Competitors/manual_double-grading_workflows) — competes with · Competitors
- [manual question-level LMS extraction](/Competitors/manual_question-level_LMS_extraction) — competes with · Competitors
- [AEFIS Assessment Software](/Competitors/AEFIS_Assessment_Software) — competes with · Competitors
- [double-grading coursework](/Competitors/double-grading_coursework) — competes with · Competitors
- [double-grading](/Competitors/double-grading) — competes with · Competitors

### Embodies

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

### What it offers

- [Private Eval Engine](/Software/Private_Eval_Engine) — offers · Software
- [Proficiency Vault](/Software/Proficiency_Vault) — offers · Software

### Composed of

- [Identity Redaction Agent](/Agents/Identity_Redaction_Agent) — composes · Agents
- [Artifact Assessment Service](/Services/Artifact_Assessment_Service) — composes · Services
- [Proficiency Matrix SDK](/Software/Proficiency_Matrix_SDK) — composes · Software
- [Gradebook Extraction API](/Software/Gradebook_Extraction_API) — composes · Software
- [Criterion Alignment Worker](/Agents/Criterion_Alignment_Worker) — composes · Agents
- [Multimodal Ingestion Agent](/Agents/Multimodal_Ingestion_Agent) — composes · Agents
- [Course Ingestion API](/Software/Course_Ingestion_API) — composes · Software
- [Accreditation Dossier Service](/Services/Accreditation_Dossier_Service) — composes · Services
- [Artifact Stratification Agent](/Agents/Artifact_Stratification_Agent) — composes · Agents
- [Submission Anonymization Worker](/Agents/Submission_Anonymization_Worker) — composes · Agents
- [Multimodal Parsing Engine](/Software/Multimodal_Parsing_Engine) — composes · Software

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