The mandate was never to automate clinical judgment—it was to give clinicians better structured evidence and keep them the final decision-maker.

Kokum Assist came to Corazor Technology to close the gap between what caregivers observe at home and what therapists can act on in the clinic. The goal was not to digitize therapy notes—it was to build an intelligent platform that captures structured behavioral data, produces clinically explainable insights, and keeps therapists in control of every recommendation.

In five months, a five-person Corazor team designed and delivered SpectrAble: an AI-powered therapy management platform built around three role-specific applications—Caregiver, Therapist, and Administration—supported by a Retrieval-Augmented Generation (RAG) engine. The result is a production-ready system for continuous patient monitoring, structured therapy management, AI-assisted reporting, and clinical workflows designed to scale across additional healthcare specialties. See our AI development & automation service for how we approach production AI.

About the Client

Kokum Assist is a healthcare technology company improving therapy outcomes for neurodivergent individuals through digital innovation. Its vision was a platform where caregivers document patient progress consistently, and therapists receive structured, actionable information instead of fragmented notes scattered across messaging apps, spreadsheets, and paper.

That vision reached beyond autism therapy: a configurable digital healthcare platform capable of serving multiple clinical specialties through AI-powered patient engagement.

The Challenge

Therapy management depends heavily on caregiver observations between sessions. Those observations carry valuable behavioral signal, but in practice they were recorded inconsistently, shared over WhatsApp or phone calls, missing historical context, difficult to analyze longitudinally, and impossible to quantify objectively.

Therapists spent significant time reconstructing context from unstructured notes before every consultation—time that should have gone into treatment planning. Underneath sat deeper operational gaps: no centralized therapy management system, no structured progress tracking, no standardized home-care plans, no intelligent summarization, manual reporting, and no unified caregiver–therapist communication workflow.

Critically, Kokum Assist did not want AI for its own sake. They wanted clinically explainable AI—recommendations a therapist could review, modify, approve, or reject before they ever reached a caregiver. That single requirement shaped the architecture, adding first-class needs for explainability, auditability, and human oversight.

"The mandate was never to automate clinical judgment—it was to give clinicians better structured evidence and keep them the final decision-maker."

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AI Product Engineering

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Why Corazor

Kokum Assist chose Corazor for an end-to-end product engineering engagement, not isolated development resources. Rather than scope this as a mobile app project, Corazor treated it as a full product initiative spanning product architecture, AI system design, backend engineering, mobile development, workflow design, data architecture, security, and production deployment—one accountable team from architecture through launch. Explore our services across build, audit, and compliance.

Production readiness was a first principle. Scalability, maintainability, and future compliance were built into the architecture from day one instead of being retrofitted later.

Our Approach

Phase 1 — Product Discovery. Workshops mapped therapy workflows from both caregiver and therapist perspectives: daily interactions, the therapy-session lifecycle, clinical documentation needs, home-care planning, progress monitoring, administrative workflows, and concrete AI opportunities. Instead of digitizing existing forms, Corazor redesigned the entire therapy journey into structured digital workflows.

Phase 2 — Platform Architecture. A modular architecture with three independent applications—Caregiver, Therapist, and Administration—sharing a common backend while exposing role-specific functionality through strict authorization. The modularity was deliberate: support new healthcare specialties without rebuilding the core.

Phase 3 — AI System Design. Rather than lean on a standalone LLM, Corazor implemented a RAG architecture: journal preprocessing, context retrieval via vector search, prompt orchestration, AI suggestion generation, therapist review, and a caregiver feedback loop. Suggestions stay grounded in the patient's actual history, and the therapist always remains the final decision-maker. Read our guide on building an AI MVP for the same scope-first philosophy.

Phase 4 — Product Development. Delivered iteratively across modules. The Caregiver app covers daily journaling, activity tracking, therapy-plan access, home-care plans, AI suggestions, a Track Your Journey view, patient metrics, progress visualization, and notifications. The Therapist app covers patient management, therapy-session creation, therapy history, goal management, AI suggestion review, home-care plan assignment, progress analytics, and metric-trend analysis. The Admin portal covers user and therapist management, platform monitoring, role administration, data oversight, and reporting.

Phase 5 — Testing & Pilot Readiness. Full end-to-end validation before pilot: role-based authentication, therapy workflows, the complete AI suggestion lifecycle, data synchronization, session history, progress visualization, API validation, user acceptance testing, and production deployment readiness.

The Solution in Detail

SpectrAble was engineered as a cloud-native healthcare platform: React Native mobile apps, Node.js backend services, Python AI services, MongoDB, a Qdrant vector database, a RAG pipeline, and Llama API integration.

Caregiver journals are transformed into structured contextual data, relevant history is retrieved from the vector database and supplied to the language model, and generated insights are routed through therapist approval before becoming visible to caregivers—improving consistency while preserving clinician oversight.

Key engineering decisions: AI was isolated in dedicated Python microservices rather than embedded in the mobile app, enabling independent AI updates, better scalability, model flexibility, easier experimentation, and future model replacement without touching the mobile clients. Patient analytics were similarly separated from transactional workflows through dedicated aggregation services, so progress charts, weekly trends, therapy summaries, behavioral-improvement views, and historical comparisons generate without degrading app performance.

Security and quality: because the platform handles sensitive health information, it incorporated role-based authorization, secure API authentication, encrypted communications, structured audit trails, controlled AI approval workflows, and a modular design ready for future compliance enhancements—with a consistent emphasis on production stability over prototype functionality.

Results & Impact

The engagement delivered a production-ready digital therapy ecosystem, now evolving through pilot deployments. Deliverables spanned the Caregiver and Therapist mobile apps, a web administration portal, a RAG-based clinical insight engine with therapist approval, production-ready backend APIs, patient progress analytics and trend analysis, a digital therapy-session lifecycle, home-care assignment and tracking, and a human-in-the-loop AI suggestion workflow.

In business terms, SpectrAble replaced fragmented caregiver communication with structured digital workflows, enabled continuous patient monitoring outside therapy sessions, reduced therapist dependence on manual note review through AI-assisted summaries, established a scalable architecture ready for additional healthcare specialties, and created a configurable foundation for future multi-specialty digital health solutions.

As the platform moves through pilot and production, Corazor and Kokum Assist are instrumenting the outcomes that matter most—therapist preparation time, caregiver logging consistency, and patient engagement—so future results can be reported with measured evidence rather than estimates. See our guide on AI integration in existing systems for related patterns.

Key Takeaways

AI delivers the most value when it is integrated into clinician workflows rather than replacing clinical judgment. Retrieval-Augmented Generation produces more reliable, contextual output than standalone LLMs for healthcare use cases. Modular product architecture enables expansion into new specialties with minimal re-engineering. Early investment in workflow design meaningfully reduces downstream product complexity. And production-ready healthcare platforms demand equal focus on engineering, usability, scalability, and governance.

Conclusion

Building an AI-powered healthcare product or modernizing an existing clinical platform? Corazor Technology partners with startups and enterprises to design, engineer, and launch production-grade AI solutions with one accountable team—from architecture to deployment. Talk to Corazor about your product.

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