MedEncAI - Agentic AI for Medical Coding and Clinical Notes Extraction

The agent does not simply prompt a language model and return the result. It plans its approach, calls grounded tools (AWS Comprehend Medical for ICD-10 coding, Bedrock Knowledge Bases for CPT retrieval), drafts a structured note, submits it to a supervisory LLM for rubric-based review, and self-corrects before emitting a final output. Every clinical claim in the note carries references to the specific transcript segments that support it, enabling downstream human review without re-listening to the encounter.

MedEncAI - Encounter Assistant is delivered through a professional services engagement. Loka's engineering team handles environment setup, customer-specific branding and UX customization, EHR integration where applicable, physician training, and a metrics-driven trial period before general rollout.

**Benefits:**

• Full IP ownership and white-labeling: deployed in the customer's AWS account under the customer's brand with no vendor lock-in
• Autonomous agentic architecture with self-correction: a reasoning agent that plans, grounds, reviews, and revises rather than a single-prompt pipeline
• Built-in hallucination verification: every output field cites its source transcript segment, structurally preventing fabricated clinical content
• Grounded medical coding: ICD-10 via AWS Comprehend Medical and CPT via Bedrock Knowledge Bases, never generated from the language model alone
• Continuous production evaluation: 10% of real invocations scored automatically for correctness, faithfulness, and coding accuracy
• Deeply customizable: note templates, agent behavior, rubric criteria, evaluation thresholds, branding, and UX adapt to each organization's clinical workflows
• AWS-native, consumption-based pricing: no per-provider or per-encounter licensing fees
• HIPAA-eligible architecture: encryption at rest and in transit, audit trails, PHI minimization, zero-trust access controls, and stateless execution with no cross-session data leakage