SageCouncil.com

AI that predicts governance risks before council votes

Score: 7.4/10AustraliaHard BuildReady to Spawn
Brand Colors

The Opportunity

Problem

ANU has suffered $100 million in reputational damage from a string of governance failures.

Solution

SageCouncil uses RAG and fine-tuned models trained on anonymized Australian university governance cases to analyze meeting papers and proposed decisions in real time. It returns risk scores, precedent matches, and mitigation recommendations so senior administrators can prevent failures instead of reacting to them.

Target Audience

Vice-chancellors, university council members, and senior administrators at prestigious Australian universities

Differentiator

Only platform with a vector database of Australian higher-ed governance failures and outcomes, delivering contextually relevant advice no international generic AI tool can match.

Brand Voice

supportive

Features

Decision Risk Analyzer

must-have55h

AI scores every agenda item for governance and reputational risk

Precedent Finder

must-have45h

Surface similar past cases from Australian universities

Meeting Paper Review

must-have40h

Upload papers and receive instant feedback before circulation

Mitigation Recommendations

must-have35h

Specific, actionable steps to reduce identified risks

Risk Dashboard

must-have30h

University-wide view of upcoming decision risk profile

Secure Document Upload

must-have25h

Encrypted ingestion pipeline with automatic redaction options

Council Briefing Pack

nice-to-have30h

Auto-generated one-pager highlighting AI findings

Historical Case Library

nice-to-have35h

Curated, searchable library of governance lessons

Slack Integration

nice-to-have20h

Receive risk alerts directly in governance channels

Fine-tuning Pipeline

future65h

Allow universities to contribute anonymized data to improve model

Total Build Time: 380 hours

Database Schema

universities

ColumnTypeNullable
iduuidNo
nametextNo
created_attimestampNo

Relationships:

  • has_many: documents
  • has_many: analyses

documents

ColumnTypeNullable
iduuidNo
university_iduuidNo
titletextNo
embeddingvectorYes
created_attimestampNo

Relationships:

  • belongs_to: universities
  • has_one: analysis

analyses

ColumnTypeNullable
iduuidNo
document_iduuidNo
risk_scoreintNo
findingstextNo
precedentstextYes
created_attimestampNo

Relationships:

  • belongs_to: documents

API Endpoints

POST
/api/analyze

Upload document and return AI risk analysis

🔒 Auth Required
GET
/api/precedents/search

Semantic search of historical governance cases

🔒 Auth Required

Tech Stack

Frontend
SvelteKit + Tailwind + Melt UI
Backend
SvelteKit server routes
Database
PostgreSQL + pgvector
Auth
BetterAuth
Payments
Stripe
Hosting
Railway
Additional Tools
LangChain.jsOpenAIUpstash

Build Timeline

Week 1: RAG foundation

48h
  • Vector DB setup with pgvector
  • Document ingestion pipeline
  • Basic auth

Week 2: Core AI analysis

55h
  • Prompt engineering + LangChain chains
  • Risk scoring logic
  • Precedent retrieval

Week 3: UI and workflows

50h
  • SvelteKit frontend
  • Meeting prep flow
  • Dashboard

Week 4: Polish and launch

40h
  • Stripe billing
  • Landing page
  • Beta with 2 pilot unis
Total Timeline: 4 weeks • 280 hours

Pricing Tiers

Starter

$25/mo

10 analyses/mo

  • 10 analyses per month
  • Basic risk scoring
  • Email delivery

Professional

$89/mo

Unlimited

  • Unlimited analyses
  • Precedent search
  • Briefing packs
  • Priority support

Enterprise

$249/mo

Custom

  • Everything in Pro
  • Private model fine-tuning
  • On-premise option
  • Dedicated AI trainer

Revenue Projections

MonthUsersConversionMRRARR
Month 11822%$1,050$12,600
Month 69538%$6,200$74,400

Unit Economics

$210
CAC
$4100
LTV
2.8%
Churn
79%
Margin
LTV:CAC Ratio: 19.5xExcellent!

Landing Page Copy

Never Be Surprised By Governance Failure Again

AI trained on Australian university cases predicts risks before you vote. Used by vice-chancellors who refuse to repeat ANU's mistakes.

Feature Highlights

Predictive risk scoring
Australian precedent matching
Instant mitigation advice
Zero hallucination design
Enterprise privacy

Social Proof (Placeholders)

""It caught a conflict-of-interest risk we completely missed." — University Council Member"

First Three Customers

Target early-adopter VCs at universities that recently had minor governance issues via LinkedIn outreach and offer free AI audits of their last three council meetings. Secure pilot agreements with written commitment to become case studies.

Launch Channels

LinkedIn thought leadershipAI in Education conferencesProductHunt AI categoryUniversity CIO WhatsApp groups

SEO Keywords

ai governance tool universityTEQSA risk predictionaustralian university ai compliancecouncil decision aihigher education predictive governance

Competitive Analysis

Enterprise
Strength

Strong analytics

Weakness

No AI precedent matching for Australian higher education

Our Advantage

Specialized RAG dataset of local governance failures

🏰 Moat Strategy

Continuous improvement loop where each university's anonymized decisions further trains the shared Australian governance model.

⏰ Why Now?

Explosion of accessible LLM technology combined with heightened governance scrutiny post-ANU scandal creates perfect timing for an AI-native governance co-pilot.

Risks & Mitigation

technicalhigh severity

LLM hallucination on governance advice

Mitigation

Strict RAG-only architecture with human-in-the-loop validation and clear disclaimers

Validation Roadmap

pre-build18 days

Test RAG accuracy with 40 real governance cases

Success: 85%+ relevance score from expert reviewers

Pivot Options

  • Sell the RAG dataset to consulting firms
  • Expand to corporate governance in ASX companies

Quick Stats

Build Time
280h
Target MRR (6 mo)
$6,800
Market Size
$35.0M
Features
10
Database Tables
3
API Endpoints
2