AI systems for the Health Sector.
We build custom AI applications for healthcare providers, dental networks, and clinical researchers — from neural network image training for dental X-rays to clinical RAG copilots. Every system is engineered to be accurate, secure, and aligned with healthcare compliance requirements.
Generic LLMs guess.
Clinical RAG systems know.
Off-the-shelf LLMs are trained on public internet data, making them unsafe for medical use — they hallucinate, they lack specific clinical context, and they are not updated with your hospital's protocols.
Wolkomtech engineers Retrieval-Augmented Generation (RAG) pipelines that anchor the AI strictly to your verified medical documents. The LLM does not generate medical advice; it synthesizes and cites the exact clinical literature and patient records you provide.
Security-first architecture
Built for PHI and PII from day one.
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Compliance-aligned design: Architecture designed to meet HIPAA and SOC 2 requirements — encryption at rest and in transit, strict access controls, and audit logging.
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No training on your data: Your patient records and clinical documents are never used to train base models.
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Citation & traceability: Every AI-generated response cites the source clinical text it was drawn from.
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On-premise or VPC deployment: Run models entirely within your hospital's private cloud or infrastructure.
We build to support your compliance program; final certification and validation are owned by your organization.
Clinical AI use cases
Dental AI Diagnostics
Neural network image training to detect caries, plaque, and anomalies in dental X-rays with high precision. Built to assist dentists, not replace them.
Clinical RAG Copilot
Custom LLM training and RAG pipelines powering a copilot that lets healthcare providers query patient records, procedures, and medical literature instantly.
Automated Medical Coding
Custom LLMs that read clinical notes and suggest ICD-10 and CPT codes automatically — minimizing denied claims and accelerating revenue cycles.
Built on proven, healthcare-ready foundations.
Healthcare AI questions, answered.
How do you protect patient data and PHI?
Every engagement begins with an NDA. We encrypt data at rest and in transit, enforce role-based access controls, and — where sensitivity requires it — train and deploy models entirely within your own VPC or on-premise environment. Your patient data is never used to train base models.
Is your diagnostic AI a regulated medical device?
That depends on the intended use and jurisdiction. Our imaging systems are built as clinical decision-support tools to assist clinicians, not replace them. If your product requires regulatory clearance (for example, FDA in the US or CDSCO in India), we align the development and validation process with your regulatory pathway.
How do you validate diagnostic accuracy?
Models are validated against clinician-verified ground-truth datasets before production deployment, with documented performance reports covering sensitivity, specificity, and error analysis.
Can the AI integrate with our existing EHR or practice management system?
Yes. We integrate through secure APIs and work within your existing infrastructure. Specific integration requirements are assessed during discovery and included in the architecture plan.
How long does a healthcare AI project take?
Typical engagements run 8–14 weeks from discovery through production deployment. A proof of concept on your own data can usually be delivered in 3–4 weeks, so you can evaluate real-world performance before committing to full development.
Let's modernize your clinical workflows.
Tell us about your EHR systems and clinical bottlenecks. We'll architect a secure, compliant AI solution that your clinical teams will want to use.