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# BonaLab

## The ML Lifecycle Platform for Healthcare

---

# The Problem

**Hospitals want AI, but can't build it themselves.**

- Vendor AI models don't perform well on their patient populations
- Building in-house requires ML expertise they don't have
- Sending data to third parties creates regulatory complexity
- "One size fits all" AI doesn't reflect local clinical practice

> "We bought three AI tools. None of them work as well as the demos."
> Chief Medical Informatics Officer, Academic Medical Center

---

# The Market Reality

## Hospitals Are Frustrated with Vendor AI

| Challenge | Reality |
|-----------|---------|
| Performance gaps | Models trained on other populations underperform locally |
| Black box models | No visibility into how AI makes decisions |
| Integration costs | $100K-500K+ for enterprise AI deployments |
| Ongoing fees | SaaS models create perpetual cost without ownership |
| Limited customization | Can't fine-tune for local workflows or specialties |

**Result:** $2B+ spent on healthcare AI with widespread disappointment.

---

# Why Local Data Matters

## AI Performance Varies by Population

```
┌─────────────────────────────────────────────────────────────────┐
│                    THE DISTRIBUTION SHIFT PROBLEM                │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│   Model trained on:        Performance on:                      │
│   ─────────────────        ───────────────                      │
│   Academic center A   →    Academic center A:  95% AUC         │
│   (diverse urban)          Rural hospital B:   78% AUC          │
│                            Community clinic C: 82% AUC          │
│                                                                  │
│   *** Same model, dramatically different performance ***        │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘
```

**AI models must be trained or fine-tuned on local data to perform optimally.**

---

# The BonaLab Solution

## Own Your AI. Train on Your Data.

```
┌─────────────────────────────────────────────────────────────────┐
│                    BONALAB ML LIFECYCLE                          │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│   ┌──────────┐   ┌──────────┐   ┌──────────┐   ┌──────────┐   │
│   │  INGEST  │ → │ ANNOTATE │ → │  TRAIN   │ → │  DEPLOY  │   │
│   └──────────┘   └──────────┘   └──────────┘   └──────────┘   │
│        │              │              │              │          │
│   Connect to     Your team      One-click      Production     │
│   PACS/VNA       labels with    training       deployment     │
│                  QA tools       + AutoML       + monitoring    │
│                                                                  │
│             ALL WITHIN YOUR HOSPITAL'S NETWORK                  │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘
```

**Data never leaves. Models are owned by the hospital.**

---

# On-Premise Architecture

## Maximum Control, Zero Data Leakage

```
┌─────────────────────────────────────────────────────────────────┐
│                    HOSPITAL FIREWALL                             │
│  ┌───────────────────────────────────────────────────────────┐ │
│  │                                                            │ │
│  │   Hospital's           BonaLab              Hospital's    │ │
│  │   PACS/VNA      →     Platform      →      AI Models     │ │
│  │   (images)            (deployed)           (production)   │ │
│  │                                                            │ │
│  │   Annotation by        Training on          Inference on  │ │
│  │   internal staff       local compute        local cases   │ │
│  │                                                            │ │
│  └───────────────────────────────────────────────────────────┘ │
│                                                                  │
│   IMAGES AND MODELS STAY WITHIN HOSPITAL INFRASTRUCTURE         │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘
```

**No cloud dependencies. No external data transfers. No third-party access.**

---

# The Complete ML Lifecycle

## From Raw Images to Production AI

| Stage | What Happens | Hospital Control |
|-------|--------------|------------------|
| **Ingest** | Connect to PACS/VNA, pull de-identified cases | Chooses which data to use |
| **Annotate** | Internal team labels with consensus validation | Own radiologists, own labels |
| **Train** | One-click training with AutoML optimization | Owns resulting models |
| **Validate** | Test against holdout sets, clinical review | Sets acceptance criteria |
| **Deploy** | Production deployment with monitoring | Controls rollout |
| **Monitor** | Track drift, performance degradation | Continuous visibility |

**Every step happens within the hospital's control.**

---

# Why Hospitals Choose BonaLab

## The Build vs. Buy vs. BonaLab Calculation

| Option | Time | Cost | Risk |
|--------|------|------|------|
| **Build in-house** | 18+ months | $2M+ (ML team) | High (expertise gaps) |
| **Buy vendor AI** | 3-6 months | $150K-500K/yr | Medium (performance gaps) |
| **BonaLab** | 3-6 months | $50K-150K/yr | Low (own data, own models) |

**BonaLab provides the platform; hospitals provide the data and clinical expertise.**

---

# Use Cases: What Hospitals Build

## Real Applications in Production

| Application | Description | Impact |
|-------------|-------------|--------|
| **Chest X-Ray Triage** | Auto-prioritize critical findings | 40% faster critical case routing |
| **Incidental Finding Detection** | AI second-read for nodules, masses | Reduced miss rates |
| **Quality Assurance** | Compare AI vs. final reports | Continuous learning loop |
| **Research Datasets** | IRB-ready labeled datasets at scale | Accelerated publications |

**Every hospital's needs are different. BonaLab lets them build what matters to them.**

---

# Annotation Platform

## Built for Clinical Workflows

- **Intuitive Interface**: Designed by radiologists, for radiologists
- **Multi-modality**: X-ray, CT, MRI, pathology, dermoscopy, retinal imaging
- **Consensus Validation**: Multiple annotators with agreement thresholds
- **Quality Control**: Expert review queue, gold standard benchmarking
- **Progress Tracking**: Analytics dashboard for annotation campaigns

**Labeling shouldn't require ML expertise, just clinical expertise.**

---

# Training Pipeline

## One-Click Model Training

```
┌─────────────────────────────────────────────────────────────────┐
│                     TRAINING CONFIGURATION                       │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│   Model Type:        [Binary Classifier ▼]                      │
│   Architecture:      [EfficientNet-B4 ▼]                        │
│   Dataset:           [Chest X-Ray Campaign Q4 2025 ▼]           │
│   Validation Split:  [20% ▼]                                    │
│                                                                  │
│   ┌─────────────────────────────────────────────────────────┐   │
│   │  [ ] Enable AutoML hyperparameter optimization          │   │
│   │  [ ] Use transfer learning from base model              │   │
│   │  [x] Require minimum 95% validation AUC                 │   │
│   └─────────────────────────────────────────────────────────┘   │
│                                                                  │
│                    [ Start Training ]                            │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘
```

**No coding required. Clinical teams can train and validate models.**

---

# Model Performance Dashboard

## Track Every Metric That Matters

- **Accuracy Metrics**: AUC, sensitivity, specificity, PPV, NPV
- **Subgroup Analysis**: Performance by age, modality, indication
- **Calibration Curves**: Model confidence vs. actual outcomes
- **Drift Detection**: Alert when performance degrades
- **Version Comparison**: A/B testing between model versions

**Full transparency into model behavior, with no black boxes.**

---

# Compliance Architecture

## Built for Healthcare Regulations

| Regulation | How BonaLab Complies |
|------------|---------------------|
| **HIPAA** | On-premise deployment, no PHI leaves hospital |
| **GDPR** | Data stays in jurisdiction, deletion capabilities |
| **SOC 2** | Access controls, audit logging, encryption |
| **FDA** | Model documentation, validation workflows |

**Compliance is built-in, not bolted-on.**

---

# Market Opportunity

## Healthcare AI Infrastructure

| Segment | Market Size |
|---------|-------------|
| Healthcare AI (2028) | $45B |
| Medical Imaging AI (2028) | $7.4B |
| ML Operations Tools (2028) | $4.2B |

## BonaLab's Position

**We're not building AI models. We're providing the infrastructure for hospitals to build their own.**

The "Databricks for Healthcare" or "Palantir for Medical Imaging"

---

# Customer Profile

## Health Systems Ready for AI Ownership

| Attribute | Description |
|-----------|-------------|
| **Who** | Academic medical centers, large health systems, specialty hospitals |
| **Size** | 200+ beds, radiology volume of 100K+ studies/year |
| **Trigger** | Disappointment with vendor AI, strategic AI initiative |
| **Champion** | CMIO, Chief AI Officer, Radiology Chair |
| **Budget** | IT capital budget or innovation fund |

**5,000+ hospitals in the US meet this profile. 50,000+ globally.**

---

# Go-To-Market Strategy

## Land and Expand Within Health Systems

**Phase 1: Radiology First**
- Start with chest X-ray triage (highest volume, clearest ROI)
- Single department deployment
- Prove value in 90 days

**Phase 2: Expand Modalities**
- CT, MRI, mammography
- Additional AI use cases (follow-up tracking, report QA)

**Phase 3: Enterprise Platform**
- Hospital-wide ML infrastructure
- Pathology, cardiology, other specialties
- Research partnerships

---

# Business Model

## Platform Licensing + Professional Services

| Revenue Stream | Price | Description |
|---------------|-------|-------------|
| **Platform License** | $50K-150K/year | Core ML lifecycle platform |
| **Implementation** | $25K-75K | Deployment, PACS integration, training |
| **Success Services** | $2K-10K/month | Ongoing optimization, new use cases |
| **Expansion Modules** | Variable | Additional modalities, advanced features |

## Unit Economics

| Metric | Value |
|--------|-------|
| Average Contract Value | $100K ARR |
| Gross Margin | 80% |
| Expansion Rate | 150% (year 2) |

---

# Competitive Moats

## Why Incumbents Can't Replicate

### 01 / Healthcare-Native Design
Built by healthcare professionals for healthcare workflows. Not a generic MLOps tool adapted for healthcare.

### 02 / On-Premise First
Designed for air-gapped hospital networks. Competitors are cloud-native trying to go on-prem.

### 03 / Clinical Annotation Tools
Purpose-built for medical imaging. Not generic labeling tools with medical "features" added.

### 04 / Regulatory Framework
HIPAA, GDPR, SOC 2 compliance built from day one. Not afterthoughts.

---

# Competitive Landscape

## We're Creating a New Category

| Competitor Type | Examples | Limitation |
|-----------------|----------|------------|
| **Generic MLOps** | MLflow, Weights & Biases | Not healthcare-native, cloud-only |
| **Labeling platforms** | Scale AI, Labelbox | No training, no deployment |
| **AI vendors** | Aidoc, Qure.ai | Models, not platform |
| **EHR vendors** | Epic, Cerner | Limited AI capabilities |

**BonaLab is the only end-to-end ML lifecycle platform purpose-built for hospital-owned AI.**

---

# Team

## Healthcare + ML Infrastructure Experience

| Role | Background |
|------|------------|
| **CEO** | Former health system CMIO, built internal AI program |
| **CTO** | Ex-Google Health, ML infrastructure |
| **VP Product** | Radiology AI product lead at [Major Vendor] |
| **VP Engineering** | Healthcare interoperability, PACS integration |

**We've built healthcare AI from both sides: inside hospitals and at vendors.**

---

# Traction

## Early Validation

| Milestone | Status |
|-----------|--------|
| Platform MVP | Complete |
| Pilot deployment at academic medical center | In progress |
| Letter of Intent from 3 health systems | Secured |
| HIPAA compliance architecture | Validated |
| SOC 2 controls | Implemented |

**Building with design partners to ensure product-market fit.**

---

# The Ask

## Raising $3M Seed

## Use of Funds

| Category | Allocation | Focus |
|----------|------------|-------|
| Engineering | 50% | Platform development, PACS integrations |
| Go-to-market | 25% | Sales, customer success, marketing |
| Compliance | 15% | SOC 2 Type II, FDA guidance |
| Operations | 10% | Team, admin |

## 18-Month Goals

- 5 production deployments
- $500K ARR
- SOC 2 Type II certified
- Series A ready

---

# Vision

## The Future of Healthcare AI

**Year 1**
Platform validation with early adopter health systems

**Year 3**
Standard infrastructure for hospital AI programs

**Year 5**
The default platform for healthcare-owned AI globally

---

# Why Now?

## Three Converging Trends

**1. AI Maturation**
Pre-trained models make healthcare AI accessible without massive datasets

**2. Regulatory Clarity**
FDA guidance on AI/ML, hospital AI governance maturing

**3. Vendor Fatigue**
Hospitals frustrated with black-box vendor AI, want ownership

**The window is open for an infrastructure player focused on hospital empowerment.**

---

# The Opportunity

**Healthcare is the largest industry in the US, at $4.3T annually.**

**AI will transform every clinical workflow.**

**Hospitals need infrastructure to build, not just buy.**

**BonaLab is building that infrastructure.**

---

# Contact

**BonaLab**

The ML Lifecycle Platform for Healthcare

[Email] founders@bonalab.ai
[Web] bonalab.ai

---

*"The best AI for your patients is trained on your patients."*