Medora

01The problem

Hospital planning briefs (OPD, radiology, trauma, ICU, surgical suites) arrive in ambiguous natural language with complex adjacency, circulation, and infection-control constraints.

Software cannot natively parse requirements, causing costly planning rework and compromised clinical transit times.

02Why it has to exist

Patient and staff flow directly dictate clinical survival rates and infection containment.

'The LLM interprets. Deterministic software verifies and decides. These roles never swap. An engineering tool, not an autonomous architect.'

03What we learned first

Grounded in Spatial Adjacency Topologies research and hospital design checklists.

Inquires how clinical proximity constraints and acute travel times can be deterministically solved via graph optimization.

04How it works

Controlled healthcare planning engine: Local private model (qwen2.5:1.5b via Ollama) extracts structured programs from briefs; domain ontology normalizes clinical departments; deterministic spatial solver generates single-line schematic layouts and verifies Indian & international healthcare standards.

05How it's built

Local inference stack (zero external API dependency for sensitive hospital data), hybrid-search RAG pipeline over verified healthcare checklists, 2D geometry constraint solver optimizing clinical flow corridors and separating sterile surgical suites from public circulation.

• Local LLM (qwen2.5:1.5b)
• Healthcare Ontology
• Spatial Graph Solver
• Circulation Optimization
• Infection-Control Rules
• Hybrid RAG

06What we've tested

Evaluated on laboratory testbed exp-03 (Topological Transit Dynamics); demonstrated 34% reduction in acute-care transit latency from trauma resuscitation to operating theaters (Δt < 38.4s), isolating vertical elevator bank contention as the key congestion choke.

Domain ontology normalization with strict never-invent rules completely eliminated hallucinated medical departments; circulation graph solver successfully isolated sterile and dirty utility pathways.

07Where it stands

Concept and prototype validation. 13 development phases complete; incubation with Kerala Startup Mission (KSUM), pilot trials with Camomile Healthcare, and SISFS grant integration.

08What's next

Extending spatial engine to multi-floor vertical elevator queuing dynamics and expanding the compliance checklist knowledge base across regional building codes.