AI medical scribes are the hottest category in healthcare AI, and most organizations face the same question: buy a product like Abridge, Suki, or Nuance DAX, or build a custom scribe you control. Taction Software builds custom AI medical scribes — ambient capture, medical-grade transcription, LLM-generated clinical notes, EHR write-back, and clinician review — for health systems and digital health companies that need specialty depth, on-premises deployment, or a scribe of their own to ship as a product.
If you want a ready, service-delivered ambient documentation capability rather than a custom build, see our ambient clinical documentation offering. This page is about building a custom scribe.
Schedule a Free AI Scribe Architecture Workshop (90 min) → (NDA-protected)
Healthcare-specific AI engineering team · EHR integration experience · HIPAA + BAA
Build vs. Buy: When Custom AI Scribes Make Sense
Specialty-Specific Requirements
Off-the-shelf scribes are tuned for general clinical practice. Specialties with distinctive documentation — cardiology, orthopedics, behavioral health — often need depth the packaged products do not deliver.
Existing Voice Recognition Infrastructure
Organizations with existing voice-recognition or transcription investments may want a custom scribe that builds on what they have rather than replacing it.
Data Sovereignty / On-Premises Requirements
When data cannot leave your environment, a custom scribe with on-premises or private-cloud deployment is the only path — drawing on our on-prem LLM work.
Building AI Scribe as a Product
For health-tech companies, the scribe is the product. Custom development is the only route, and the engineering quality determines whether you can scale and sell it.
Our AI Scribe Architecture
Audio Capture & Processing
Multi-speaker diarization, background-noise handling, and privacy-preserving capture so the raw input is clean and compliant from the start.
Transcription Layer
Medical-specific ASR models, custom vocabulary and terminology, and the right choice of streaming vs. batch transcription for your latency needs.
LLM Documentation Layer
Prompt engineering for clinical notes, RAG for specialty-specific knowledge, hallucination prevention, and multi-section note generation — the layer that turns a transcript into a usable clinical note.
EHR Write-Back
SMART on FHIR integration, Epic / Cerner / athenahealth-specific write-back, and HL7 v2 document storage, built on our FHIR, Epic integration, and HL7 practices.
Clinician Review & Editing
Mobile and web interfaces, voice editing, and confidence indicators so the clinician stays in control and the note is theirs before it is signed.
LLM & Model Choices
Cloud Models (GPT-4 / Claude / Gemini)
For documentation quality, we work with leading cloud LLMs under provider BAAs where PHI is involved.
Open-Source Models (Llama, Mistral) for On-Prem
For data-sovereignty needs, we deploy open-source models on-premises or in your private cloud.
Specialty Fine-Tuning Approaches
We use fine-tuning and retrieval approaches to adapt the documentation layer to your specialty and house style.
Cost & Latency Trade-offs
We engineer the model and pipeline choices around your cost and latency targets, because a scribe that is accurate but slow does not get used.
HIPAA Compliance for AI Scribes
BAA-Covered LLM Providers
We use LLM providers that will sign a BAA when PHI is processed in the cloud, and architect so PHI is handled correctly throughout.
On-Premises Deployment Options
Where the cloud is not acceptable, we deploy fully on-premises or in your private cloud.
Data Retention & Training Data Controls
We control data retention and ensure your data is not used to train third-party models, with the contractual and technical controls to back it.
Audit Logging Requirements
We build the audit logging HIPAA expects around PHI access and processing — see our HIPAA-compliant development and data security practices.
Specialty Customization
We build for primary care, specialty practices (cardiology, orthopedics, behavioral health), inpatient, and telehealth settings, each with its own documentation patterns and note structures.
Build Timeline & Cost
MVP: 3–6 Months
A working MVP scribe typically takes three to six months, enough to validate accuracy and workflow fit.
Production AI Scribe: 6–12 Months
A production-grade scribe — robust, integrated, and scaled — generally runs six to twelve months.
Specialty Customization Add-On
Specialty customization is scoped as an add-on once the core scribe is in place. We give a firm, scoped estimate after the architecture workshop.
Schedule a Free AI Scribe Architecture Workshop (90 min) →
Frequently Asked Questions
Should we buy Abridge / Suki / DAX vs. build?
Buying is the right call when a packaged scribe fits your specialties and workflow and you do not need to own the product. Building makes sense when you need specialty depth they lack, on-premises deployment, integration with existing infrastructure, or a scribe of your own to ship. We will give you an honest build-vs-buy read for your situation.
How do you prevent hallucinations?
We ground generation in the actual transcript, constrain the model to the encounter content, surface confidence indicators, and keep the clinician in the loop to review and edit before signing. The goal is a note the clinician trusts and verifies, never an unchecked generation.
How does EHR write-back work?
We write notes back through SMART on FHIR and EHR-specific integration (Epic, Cerner, athenahealth), or via HL7 v2 document storage, so the finished note lands in the right place in the chart.
Can you do on-premises deployment?
Yes. For data-sovereignty needs we deploy open-source models fully on-premises or in your private cloud, so PHI never leaves your environment.
What about voice-to-EHR latency?
We engineer the transcription and documentation pipeline around your latency target, choosing streaming vs. batch processing and model options so the scribe is fast enough to fit real clinical workflow.
Schedule a Free AI Scribe Architecture Workshop (90 min) →
Reviewed by Taction Software’s healthcare AI engineering team. ISO 27001-certified information security management. PHI is handled under a signed BAA. For broader AI work, see our healthcare AI solutions.