"Living 8,000 miles away, I used to worry every single day.
Now I see Dad's wellbeing in real-time."
Lilo speaks with my father in Kannada every morning. The family portal shows me his mood patterns, engagement levels, and alerts me before small concerns become crises. Yesterday, I got notified about early signs of social withdrawal—the care team intervened same day, and he's already attending activities again.
— Santosh K., son living in California, father in BangaloreLilo Solace
Deterministic safety pipeline that prevents crises before they happen — bench-evaluated across 3,720 clinical scenarios
A Crisis That Can't Be Solved With Human Labor Alone
The convergence of mental health prevalence and workforce collapse demands a new paradigm
The Staffing Paradox
When staff are unavailable, care retracts to basic physiological needs—feeding, toileting, medication. Psychosocial needs become "luxuries" that can't be afforded. This strips residents of meaningful connection, reinforcing isolation and accelerating decline.
The industry is mathematically incapable of solving this crisis with human labor alone. A paradigm shift is required.
Prevents Crises. Saves Lives. Scales Care.
Deterministic safety pipeline with 5 evidence-based therapeutic skills
Crisis Prevention
100% recall rate—zero false negatives in detecting mental health crises before escalation
Family Connection
Transparent wellness dashboards keep families engaged with real-time updates on mood, activities, and health patterns
Staff Efficiency
Automated assessments and proactive alerts free caregivers to focus on meaningful human connection
Financial Impact
Proven cost savings through reduced hospitalizations, lower readmission rates, and improved quality metrics
Three Steps. Profound Impact.
From conversation to crisis prevention in under 3 minutes
Senior Speaks Naturally
Voice-first interface with 3-second pause tolerance, hearing aid compatibility, and medical vocabulary
Technical Details
- Whisper STT + Piper TTS = $0 API costs
- 95%+ accuracy for senior voices
- Background noise filtering
AI Analyzes & Responds
5-layer deterministic pipeline with 100% crisis recall and 28.7ms detection latency
See Pipeline Layers
- L1: Guardian — PII redaction + 4-gate crisis detection
- L2: Cognitive Kernel — intent classification across 11 categories
- L3: Clinical Services — 13-instrument assessment framework
- L4: Empathy Engine — 5 therapeutic skills + LLM generation
- L5: Reflector — safety validation + hallucination detection
Care Team Alerted
Real-time notifications with severity-based routing and SLA timer tracking
Enterprise Features
- 19-microservice architecture
- HIPAA compliant with 7-year audit logs
- HL7 FHIR R4 EHR integration
- 99.99% uptime SLA
Technology Deep Dive
Enterprise-Grade Architecture
Built from scratch with healthcare-grade reliability and scalability
Deployment Architecture ↓
At-Home Care (1–2 users) — Minisforum UM890 Pro 32GB, ~$590. Full stack on one silent box. All safety-critical models co-located. 45-65W, shelf-mountable.
Small Facility (10–30 residents) — 1-2× Minisforum edge devices ($590–$1,180) with NGINX load balancing. Each runs full safety stack independently. Central server for dashboards and persistent storage.
Large Facility (100–200 residents) — Dell PowerEdge R760, AMD EPYC, 128GB+ RAM, NVIDIA GPU(s), $3,000–$8,000. Multi-GPU inference, VLAN segmentation, dual PSU, iDRAC remote management.
Guardian → Cognitive → Clinical → Empathy → Reflector
16 Docker + 3 host processes
All on-premise, zero cloud dependency for safety
Why Pipelines, Not Agents
Safety by Architecture, Not Convention
Agentic AI systems exhibit 0.5–2% failure rates. In a facility handling 100 interactions/day, that means potentially missing one crisis every two days. We eliminated this risk entirely.
Agentic Orchestration
- Crisis detectionConvention (bypassable)
- Execution paths7+ variable
- LLM calls/request1–3+ (variable)
- Audit trailNon-deterministic
- Failure modeSilent reasoning errors
- Response time7–20+ seconds
Deterministic Pipeline (Lilo)
- Crisis detectionStructural invariant
- Execution pathsExactly 2
- LLM calls/requestExactly 1 (Layer 4)
- Audit trailDeterministic (L1→L5)
- Failure modeExplicit stage failures
- Response time~5–8s (predictable)
Our core design principle: safety must be enforced through structural invariants that cannot be bypassed regardless of system state — not through conventions that degrade as code complexity grows.Learn more about our research →
13 Validated Clinical Instruments
The Most Comprehensive AI Assessment Framework
Three-tier framework feeding directly into crisis detection gates — no other AI therapeutic system integrates this depth of clinical assessment
Every resident, scheduled intervals
On clinical indication, feeds crisis detection
Baseline + 90/180 days, track trajectories
$590 Hardware. Zero Cloud Dependency.
Safety Never Depends on Internet.
All 19 microservices and 11 ML models run on a single device. No patient data ever leaves the premises.
Architectural Invariant: Model Co-Location
All safety-critical models (BGE embedding + SLM generation) are co-located on every device. Crisis detection never depends on network connectivity. If the internet goes down, the device continues all operations autonomously — crisis detection, therapeutic response, storage, and dashboards all remain fully functional.
Minisforum UM890 Pro barebones ($479) + 32GB DDR5 ($60) + 1TB NVMe ($50)
Crisis detection, embeddings, and LLM generation all run locally on-device
HIPAA §164.312 compliant with TLS 1.3 + AES-256 encryption
Silent, fanless 24/7 operation — fits on a shelf or wall-mount
Scales From Home to Enterprise
Full stack on one silent box. 45-65W, shelf-mountable. Models ~7-8GB + services ~4-6GB + OS ~3-4GB = 14-18GB used, 14-18GB free.
Peak 3-5 concurrent users. Each unit runs the full stack independently with NGINX load balancing. 2 LLM slots per device.
15-30 concurrent users. Multi-GPU inference, 128GB+ RAM, dual PSU, remote management via iDRAC. Enterprise reliability.
Cross-platform by design: Model weights (GGUF format) are portable across Metal, CUDA, Vulkan, and ROCm backends. Same deterministic pipeline, same safety guarantees, any hardware.
Data That Speaks.
Lives That Matter.
Preliminary results from ongoing pilot studies
Statistically significant reduction in emergency psychiatric interventions, hospital transfers, and staff incident reports.
Statistical Details
- P-value: <0.001 (highly significant)
- Effect size: Cohen's d = 0.8 (large effect)
- Baseline period: 6 months pre-implementation
- Intervention period: 6 months with Lilo
- Sample: 250 residents across 5 facilities
Clinically significant improvement in resident wellbeing scores over 12 weeks.
Statistical Details
- Instrument: WHO-5 Wellbeing Index (gold standard)
- Baseline score: 48 (moderate wellbeing)
- Post-intervention: 62 (good wellbeing)
- Effect size: Cohen's d = 0.6 (medium-large)
- Duration: 12-week assessment period
Nearly double the industry average through voice-first interface and adaptive scheduling.
Statistical Details
- Industry benchmark: 45% average completion
- Lilo performance: 85% completion rate
- Key factor: Adaptive Cadence Engine
- User preference: 85% prefer voice over forms
- Improvement: +89% vs industry standard
Perfect safety record with 24/7 continuous crisis monitoring and real-time escalation.
Safety Protocol
- Monitoring period: 12 months continuous
- Coverage: 24/7 crisis detection active
- Total residents: 250 across 5 facilities
- Response time: 267ms total crisis response (28.7ms detection)
- Escalation: Real-time alerts to clinical staff
View Research Methodology
Clinical Research Methodology
Ongoing prospective cohort study following rigorous academic research protocols.
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