PHI sent to the wrong services
Prompts, logs, analytics and error trackers can all leak PHI to vendors that have not signed a BAA. Every data path needs to be mapped, not just the main model call.
HIPAA-Compliant AI Development Company
HIPAA-Compliant AI Development: Agents, Chatbots and LLM Apps
We build AI agents, chatbots, RAG assistants and clinical documentation tools that handle protected health information safely, with encryption, access control, audit trails, vendor BAAs and human approval designed in from day one.
- Clinical documentation time per patient (SureScribe)
- 60+ → ~20 min
- Clinical documentation time per patient (SureScribe)
- and CharmHealth EHR integrations in production
- Athenahealth
- and CharmHealth EHR integrations in production
- Encryption, vendor agreements and automated deletion implemented
- PHI lifecycle
- Encryption, vendor agreements and automated deletion implemented
- Before AI output reaches the medical record
- Human approval
- Before AI output reaches the medical record
What Is This Service?
What makes an AI application HIPAA-ready
HIPAA does not certify software, and no model is 'HIPAA compliant' on its own. What matters is how your application handles protected health information: which vendors receive it and under which business associate agreements, how it is encrypted and stored, who can access it, what is logged, how long it is kept, and whether AI output is reviewed before it affects care.
Torch Solutions designs healthcare AI around those questions. We decide which data a model actually needs, minimize and de-identify where possible, route PHI only to services covered by appropriate agreements, keep audit trails of AI actions, and put clinicians or staff in control of anything that changes the record.
The same principles apply whether you are building an AI scribe, a patient-intake chatbot, a prior-authorization or scheduling agent, or a RAG assistant that answers questions from clinical policies and patient documents.
Business Challenges
Where healthcare AI projects create compliance risk
Agents with too much access
An agent that can read or write any record is a security problem. Tools need narrow permissions, allowlists, rate limits and approval steps.
No audit trail for AI actions
When AI drafts, summarizes or updates information, you need to know what it saw, what it produced and who approved it.
Unreviewed output in clinical workflows
Summaries and suggestions must be grounded in source documents and reviewed by the accountable person before they are used.
Features & Capabilities
Healthcare AI we build
AI medical scribes and documentation
Encounter transcription, structured notes, clinician review and EHR write-back, as delivered in SureScribe.
Patient intake and support chatbots
Conversational intake, FAQs, scheduling help and routing, with clear boundaries on clinical advice and escalation to staff.
Administrative AI agents
Agents that gather information, prepare forms, check status and route tasks across approved systems, with human confirmation for sensitive actions.
RAG over policies and records
Grounded answers with citations from clinical policies, payer rules or selected patient documents, limited to what each user is allowed to see.
EHR-connected workflows
Integrations with EHR APIs for registration, reports, labs and notes, including Athenahealth and CharmHealth.
Care-team and caregiver apps
Mobile and web experiences for care coordination with conversational AI support, informed by our HomeTeams caregiving platform.
Business Benefits
Business value designed into the system
Lower compliance risk
Data flows, vendors, access and retention are decided deliberately and documented, which makes security reviews and customer due diligence easier.
Faster security reviews
Architecture diagrams, data-flow maps and audit capabilities answer the questions hospital and payer security teams ask.
Trust from clinicians and staff
Human approval, citations and visible action history make AI output easier to check and adopt.
Room to change vendors
Provider abstraction and clear boundaries let you switch models or speech engines as BAAs, pricing and quality change.
Our Approach
Compliance designed in, not bolted on
Use case and PHI mapping
We define the workflow, the data each step needs and every place PHI could travel, including logs, monitoring and third-party tools.
Vendor and architecture decisions
We select model, speech and cloud services that fit your agreements and requirements, and design storage, encryption, networking and access control.
Guardrails and human review
We set tool permissions, allowlists, validation, approval checkpoints and escalation paths before the AI is allowed to act.
Build and evaluate
We implement the application and test it on successful paths, missing data, unsafe requests and failure cases with representative, appropriately protected data.
Audit logging and retention
Every AI action, approval and data access is logged; retention and deletion rules are automated.
Launch and monitor
We roll out to a pilot group, monitor quality, cost and incidents, and improve based on evidence.
Technologies We Use
A production stack selected for your requirements
We select providers and infrastructure based on your BAAs, hosting requirements and the measured quality of each option.
- OpenAI
- Anthropic
- LangChain
- LlamaIndex
- AssemblyAI
- AWS Transcribe
- Python
- FastAPI
- Django
- PostgreSQL
- Vector Databases
- AWS
- Azure
- Google Cloud
- FHIR
- Athenahealth
Who It's For
Built for teams like yours
Digital health and health-tech startups
Products that need to pass customer security reviews and handle PHI correctly from the first release.
Outpatient and specialty practices
Documentation, intake, scheduling and administrative automation that reduces staff workload.
Home health and caregiving organizations
Care coordination, visit documentation and caregiver support outside the clinic.
Healthcare software vendors
Existing platforms adding AI features without putting their customers' data at risk.
Why Torch Solutions
Healthcare AI engineering you can defend in a security review
Shipped clinical AI
SureScribe combined speech recognition, LLM pipelines, RAG and EHR integration with encryption, vendor agreements, access controls and automated PHI deletion.
Narrow, observable AI
We scope AI to the responsibilities where it creates value and make every action, approval and failure visible.
Full-stack delivery
Apps, APIs, integrations and cloud infrastructure are built by the same team, so security is consistent end to end.
Clear communication
We work under NDAs, document architecture decisions and keep your compliance team informed of tradeoffs.
Related Case Studies
Healthcare AI in practice
SureScribe AI Clinical Documentation Platform
HIPAA-aware AI scribe: clinical speech recognition, multi-stage LLM notes, clinician approval and Athenahealth / CharmHealth integration. Documentation cut from 60+ to about 20 minutes per patient.
Read Case Study →
HomeTeams AI Elderly Care Platform
Caregiving app with voice-based conversational AI, care plans, task coordination, secure team chat and accessible design for older users.
Read Case Study →Engagement & Timelines
Ways to start
Pick the starting point that matches how defined your use case is.
Use-case assessment
About 1–2 weeks
Validate the use case, map PHI flows and choose vendors before committing to a build.
- PHI data-flow map
- Vendor and architecture recommendation
- Risks, guardrails and delivery estimate
Proof of concept
About 4 weeks
A working prototype on representative data to prove quality and workflow fit.
- Working prototype
- Evaluation results
- Plan and estimate to production
Production build
Phased
Secure, integrated, monitored software delivered in milestones.
- Integrations and audit logging
- Admin, roles and monitoring
- Ongoing support
Healthcare projects are planned around HIPAA-aware architecture and workflows. Your compliance and legal teams make the final compliance determination.
Related Services
Combine this capability with the application, cloud, data, integration, and product engineering required to operate it reliably.
Frequently Asked Questions
Questions about HIPAA-Compliant AI Development
Can you use OpenAI or Anthropic models with PHI?
Only through offerings and agreements that cover PHI, such as a business associate agreement with the provider or access through a cloud platform under your existing BAA. We confirm the current terms during discovery and route PHI only to covered services.
Is there such a thing as HIPAA-certified AI?
No. HIPAA has no official software certification. Compliance depends on how the whole system and your organization handle PHI: agreements, safeguards, access, logging and policies. We design the software to support those requirements.
How do you stop an AI agent from accessing the wrong records?
Agents get narrow tools with explicit permissions, allowlists, validation and rate limits, inherit the user's access rights, and require human approval for sensitive actions. Every tool call is logged.
Do you sign BAAs and NDAs?
We work under NDAs on every project. Where our team will handle PHI on your behalf, we discuss a business associate agreement as part of the engagement.
Can the system run in our own cloud account?
Yes. We can deploy into your AWS, Azure or Google Cloud environment with private networking, managed secrets, encryption and logging configured to your requirements.
How long does a HIPAA-compliant AI project take?
An assessment takes about one to two weeks and a proof of concept about four weeks. Production builds are delivered in phases; a focused MVP typically takes four to eight weeks.
Need to check whether your healthcare AI idea can handle PHI safely? Contact Torch Solutions.
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