AEGIS — Enterprise Delivery Intelligence

Investor Pitch Deck

Tagline: The transcript becomes the prompt. Context-aware AI that governance teams can trust.

Problem Statement (The Opportunity)

Today's Enterprise AI is User-Centric, Not Context-Centric

The flow today:

BA has a meeting
Someone writes a prompt explaining the meeting
Someone explains who they are (role, domain expertise)
Someone explains the project context
Someone explains what governance rules apply
LLM gives a generic answer
User is still doing all the context work

The waste:

Avg. 4-5 hours per requirement — manual transcription, interpretation, documentation
Governance conflicts missed — 15-25% of requirements contradict existing standards
Zero audit trail — when decisions change, you don't know why or by whom
Rework cost — compliance violations caught downstream cost 10-50x more to fix

AEGIS Inverts This Model

The flow with AEGIS:

BA has a meeting → transcript arrives
Knowledge Graph reads project context automatically
Governance rules are known (read-only domain layer)
Persona agent knows the role
Outcome is generated
User confirms or rejects — human in the loop
Audit trail is automatic

Solution: AEGIS Platform

Core Value Proposition

Dimension	Traditional AI	AEGIS
How it works	User explains context	System knows context
Governance	Black box	Transparent, auditable
Accuracy	Generic	Persona-specific + domain-aware
Audit trail	Manual	Automatic
Time to outcome	3-4 hours	<3 minutes
Rework rate	20-35%	<5%

Key Features

1. Transcript Becomes Prompt

Upload any meeting transcript (.txt, .md, Word)
AEGIS extracts requirements, decisions, conflicts automatically
No prompt engineering required
Works with any meeting format (Sprint planning, Stakeholder review, Technical design, etc.)

2. Governed Domain Knowledge Layer

Read-only rules of engagement (compliance, UX standards, architecture principles)
Agents validate against domain knowledge but cannot overwrite
Domain expertise is baked into the system — not lost when people leave
New team members always get the same governed context

3. Conflict Detection

Automatic governance validation — every requirement checked against domain rules
Conflicts highlighted before they escalate
Red/yellow flags surface items requiring human escalation
Eliminates "compliance found this too late" cost

4. Engagement Record (Living Audit Log)

Every confirmed requirement recorded with:
- Source evidence (transcript excerpt)
- Who confirmed it & when
- Related governance rules
- Status history
Single source of truth for "what we agreed"
Eliminates "I thought we decided..." arguments

5. Persona-Specific Workspaces (Roadmap)

BA / Product Owner — Requirement extraction & review ✅ (LIVE)
UX Designer — Design standards impact & prototypes (Q3 2026)
Architect — Architecture decision records & impact (Q3 2026)
Compliance Officer — Governance audit & exception management (Q4 2026)

Business Case: ROI & Metrics

Scenario: Mid-Market Enterprise (500+ BA/PO headcount)

Cost Savings

Per requirement meeting: 4-5 hours saved × $200/hour burdened = $800-$1,000 saved
Per month (10 meetings/week): 160-200 hours saved = $32K-$40K/month
Annual savings (1 org): $384K-$480K

Risk Reduction

Missed governance conflicts (avoided): 15-20% reduction in downstream rework
Cost of rework in production: $10K-$50K per item (delays, legal, compliance)
Avoided rework annually (conservative, 10 items): $250K-$500K

Payback Analysis

Platform cost (annual): ~$50K (AWS EC2 + licenses + support)
Payback period: 4-6 weeks on cost savings alone
Year-1 ROI: 8-10x (before counting risk reduction)

Deployment Timeline

Pilot: 2-4 weeks (single team, BA/PO workspace)
Scale to org: 8-12 weeks (all BAs, governance teams)
Value realization: Immediate (savings month 1)

Market Position

Competitive Landscape

Competitor	Strength	AEGIS Advantage
ChatGPT for Confluence	Simple, familiar	Domain-governed, auditable, persona-specific
MS Copilot	Integrated with M365	Independent, works with any source, owned data
Jira Automation	Ticket routing	Requirement extraction + governance validation
Custom LLM solutions	Customizable	Pre-built, faster to value, governance-first

Key differentiator: AEGIS is governance-first, not AI-first. It combines AI extraction with rules-based governance validation — giving enterprises the speed of AI with the safety of auditable controls.

Go-to-Market Strategy

Phase 1: Pilot (Months 1-3)

Target: 1 Fortune 500 account (Capgemini internal or external customer)
Scope: BA/Product Owner workspace, single team
Success metric: 50%+ time savings, zero compliance issues, 95%+ user confidence

Phase 2: Expand (Months 4-9)

Target: Scale to full BA/PO org (100+ users)
Add: Domain Knowledge governance interface (allow clients to manage their own rules)
Success metric: Adoption >80%, ROI validated, testimonial ready

Phase 3: Extend Personas (Months 10-18)

Target: Release UX Designer & Architect workspaces
Partnerships: Design tool integrations (Figma, Adobe), ADR platforms
Success metric: 3-5 multi-persona deployments, product-market fit

Phase 4: Scale & Monetize (Year 2+)

Target: Open to market as SaaS
Pricing: Per-user-per-month (BA/PO), per-team (UX/Arch), consumption-based LLM
Revenue: $5K-$50K MRR per customer (depends on team size, LLM usage)

Technical Architecture (Why It Works)

Components

┌─────────────────────────────────────────────────────────┐
│                    AEGIS Platform                        │
├──────────────┬──────────────┬──────────────────────────┤
│  UI Layer    │  Extraction  │  Governance & Storage    │
│ (Streamlit)  │   Pipeline   │   (JSON + Neo4j)         │
├──────────────┼──────────────┼──────────────────────────┤
│  • BA        │  • Upload    │  • Domain Knowledge      │
│  • UX Designer│  • Parse     │    (read-only rules)     │
│  • Architect │  • Extract   │  • Engagement Record     │
│  • Governance│    (LLM or   │    (audit log)           │
│              │    rules)    │  • Conflict Detection    │
└──────────────┴──────────────┴──────────────────────────┘
         ↓           ↓                ↓
    ┌────────────────────────────────────┐
    │   Knowledge Graph (Neo4j)          │
    │ Context layer: projects, rules,    │
    │ decisions, requirements, people    │
    └────────────────────────────────────┘

Why This Architecture Wins

Modular: Replace LLM extraction engine without changing UI
Auditable: All decisions logged to Engagement Record
Governed: Domain Knowledge layer cannot be overwritten by agents
Extensible: Add new personas by plugging in new agent classes
Portable: Runs on-prem, AWS, Azure, or customer datacenter

Deployment Options

1. SaaS (Hosted)

AEGIS manages platform, customer data in isolated tenant
Pricing: $5K-$50K/month depending on team size + LLM usage
Fastest to value
Preferred for cloud-native orgs

2. AWS Private (Customer Account)

Deploy to customer's AWS account using CloudFormation
Pricing: $2K-$8K/month (compute + licenses) + LLM costs
Full data control
Preferred for regulated industries

3. On-Premises

Docker deployment to customer Kubernetes/VMs
Pricing: Annual enterprise license
Maximum control, highest operational burden

Roadmap (12-24 Months)

Now (Q2 2026)

✅ BA / Product Owner workspace (LIVE)
✅ Domain Knowledge (read-only governance layer)
✅ Engagement Record (audit log)
✅ Deterministic + LLM extraction (Claude, OpenAI, Bedrock)
✅ AWS deployment scripts

Q3 2026

UX Designer workspace (design standards impact detection)
Figma integration (pull design specs into knowledge graph)
Multi-language support (transcripts in any language)

Q4 2026

Architect workspace (ADR generation & validation)
Governance Officer workspace (rule management UI)
Neo4j cloud managed service integration

Q1-Q2 2027

Jira / Confluence native connectors (sync requirements bidirectionally)
SharePoint / M365 Teams integration (transcripts from Teams meetings)
Slack bot for quick requirement lookups

H2 2027

SaaS launch (multi-tenant platform)
Pricing model: $5K-$50K/month per customer

Investment Requirements

Seed Round: $500K-$1M (6 months)

Use of funds:

Engineering (2 FTE): Build UX/Architect personas, governance UI
Operations (0.5 FTE): AWS, Bedrock credits, support
Go-to-market (0.5 FTE): Pilot customer support, case study
Outcome: 2-3 paying customers, validated product-market fit

Series A: $2-5M (18 months)