IoT and AI solution reducing biotech lab equipment downtime by 40% through predictive maintenance. Targets research facilities with real-time monitoring and failure alerts. SaaS model with tiered pricing. €1.7B market opportunity.
BioSight addresses the critical challenge of unexpected equipment failures in biotechnology laboratories. By combining IoT sensors with machine learning, the platform predicts failures in PCR machines and centrifuges 7-14 days in advance. This solution targets mid-to-large research facilities experiencing costly downtime, offering actionable insights through an intuitive dashboard. With proven 40%+ downtime reduction in pilots, BioSight creates substantial operational savings for high-throughput labs processing 500+ samples weekly.
Core Functionality
IoT sensors continuously monitor performance metrics (vibration, temperature, cycle counts) on laboratory equipment. Machine learning algorithms analyze this data to predict failures 7-14 days in advance. The system provides:
- Automated maintenance alerts via web/mobile dashboard
- Technician dispatch integration with ServiceNow/ServiceMax
- Equipment health scoring system
- Maintenance history tracking and reporting
Target User and Segment
Primary customers include:
- Lab managers and operations directors
- Mid-to-large biotech research facilities (20+ employees)
- Pharmaceutical QC laboratories
- Academic research centers
Core segment: North American/European labs processing 500+ samples weekly.
Recommended Tech Stack
- Hardware: Raspberry Pi/Arduino sensors with LoRaWAN
- Cloud: AWS IoT Core + Time Series Database
- ML: Python (TensorFlow) predictive models
- Frontend: React.js dashboard + React Native mobile
- Backend: Node.js + PostgreSQL
Estimated MVP Hours and Costs
Total development: 820 hours at €100/hour = €82,000
- Hardware prototyping: 180h (€18,000)
- Predictive ML model: 220h (€22,000)
- Dashboard & alerts: 200h (€20,000)
- API integrations: 120h (€12,000)
- QA & deployment: 100h (€10,000)
SWOT Analysis
- Strengths: Proprietary algorithms, 40%+ downtime reduction, sticky subscription model
- Weaknesses: Hardware installation friction, limited equipment compatibility
- Opportunities: Expand to chromatography/HPLC systems, predictive reagent ordering
- Threats: Equipment manufacturers adding native monitoring, data security concerns
First 1000 Customers Strategy
Acquisition channels with cost projections:
- Equipment service partnerships (30% rev share): CPA €300
- Biotech conference demos (10 events/year): CPA €450
- LinkedIn ABM targeting lab directors: CPA €220
- University pilot referrals: Organic growth channel
KPIs: 35% trial conversion, 14-month CAC payback, 120 labs in Year 1 (~1,200 devices).
Monetization
Business Model: Tiered SaaS + hardware lease
Pricing:
- Essential: €99/device/month (monitoring)
- Pro: €199/device/month (predictive alerts + maintenance coordination)
- Hardware lease: €1,500/unit (3-year term)
Break-even: Achieved at 220 Pro devices (€43,780 monthly revenue). Profitability expected at 18 months post-MVP with €200k/month operating costs.
Core Team: 2 full-stack devs, 1 data scientist, 1 HW engineer, 2 sales reps (Year 1 cost: €650,000)
Market Positioning and Competitors
Market Size: €1.7B+ predictive maintenance for biotech equipment (2026 projection)
Competitors:
- ServicePower (generic FSM – lacks biotech specialization)
- Siemens MindSphere (enterprise-focused pricing)
- Thermo Fisher Connect (vendor-locked to their equipment)
Differentiation: Equipment-agnostic AI trained specifically on PCR/centrifuge failure patterns with SLA guarantees
Sales Strategy: Land-and-expand through regional service partners with downtime reduction guarantees
