It's like having a tireless, perfectly trained receptionist who answers every call instantly, asks all the right medical and legal questions, and routes each case to the right person without ever putting anyone on hold.

Wayco deploys a real-time conversational voice AI agent built on VAPI and Twilio/WebRTC infrastructure that handles inbound and outbound calls for medlegal case intake and coordination. The system uses large language models fine-tuned on medlegal terminology and workflows to conduct natural-sounding conversations, extract structured case data (injury type, treatment history, liability details), and triage cases by urgency and fit. The voice agent integrates with the platform's backend (Python, PostgreSQL, AWS/GCP) to instantly update case records, trigger provider matching workflows, and escalate complex cases to human operators. Embedding models and vector databases (Pinecone, Qdrant, Weaviate) power semantic understanding of caller intent and retrieval of relevant case context during live calls, enabling the agent to answer follow-up questions and provide accurate status updates. This eliminates the bottleneck of manual phone-based intake, ensures no lead is lost after hours, and dramatically increases the volume of cases a firm can process without adding headcount.

It's like replacing a law firm's entire phone tree with a brilliant paralegal who never sleeps, never forgets a detail, and somehow already knows every doctor in town.

It's like having an AI paralegal that can read a thousand-page medical file in seconds and hand you a perfectly organized summary with every key detail highlighted.

Wayco's document intelligence engine uses a retrieval-augmented generation (RAG) architecture combining embedding models, vector databases (Pinecone, Qdrant, Weaviate), and fine-tuned LLMs to process the full spectrum of medlegal documents—medical records, independent medical examination (IME) reports, lien filings, billing statements, and treatment notes. Documents are ingested, chunked, embedded, and indexed in vector storage for semantic search. When a user queries a case or requests a summary, the system retrieves the most relevant document segments and feeds them to an LLM that generates structured, legally precise summaries tailored to the needs of attorneys and medical providers. The pipeline includes classification models that automatically tag document types, extract key entities (diagnoses, procedures, providers, dates, costs), and flag inconsistencies or missing information. This replaces hours of manual paralegal review per case, ensures consistency across thousands of simultaneous cases, and surfaces insights (e.g., treatment gaps, lien discrepancies) that humans routinely miss under time pressure. The system is designed for HIPAA-compliant data handling across its cloud infrastructure.

It's like turning a mountain of medical paperwork into a perfectly organized Netflix queue where every episode is already summarized and the spoilers are exactly what your lawyer needs.

It's like a matchmaking app for injured people and doctors, except the AI already knows which doctor gets the best results for your exact type of injury in your zip code.

Wayco's matching engine uses machine learning models trained on case attributes (injury type, severity, treatment needs, legal jurisdiction), provider profiles (specialty, location, availability, acceptance of liens, historical case outcomes), and geographic data to generate ranked provider recommendations for each new medlegal case. The system ingests structured and unstructured data from intake (including voice AI transcripts), applies NLP to extract relevant clinical and legal features, and feeds them into a predictive model that scores provider-case fit. Vector similarity search across the provider knowledge base enables rapid retrieval of the best-matched providers even as the network scales. The model continuously improves through feedback loops—tracking treatment outcomes, case resolution times, and client satisfaction—to refine its recommendations over time. This replaces the manual, relationship-driven referral process that dominates the medlegal industry, where provider selection is often based on personal networks rather than data. By optimizing matches, Wayco reduces treatment delays, improves medical outcomes, and ultimately strengthens the legal case for the client, creating a measurable competitive advantage for firms using the platform.

It's like if Tinder actually worked, but instead of awkward dates you get the perfect orthopedic surgeon for your client's herniated disc in Pasadena.