Healthcare AI Consulting and Development Services
We build AI for clinical workflows: voice assistants on retrieval-augmented generation (RAG), AI patient intake with protocol-based routing, ambient documentation, and after-visit summaries. Our healthcare AI consulting is the scoping in front of that build, done by the engineers who then build it. It settles one use case with a measurable outcome, a data source that can be verified, and a clinician who reviews the output.
Healthcare AI Consulting That Ends in Working Software
Our healthcare AI consulting is done by the engineers who then build the feature, as they did for Shift.AI, a voice companion for clinicians. For Revvel, a women’s health platform, risk assessments over multi-year records are delivered 10x faster than traditional review, per the client.
The reason the review loop matters is in the American Medical Association’s March 2026 survey of nearly 1,700 physicians: 81% use AI professionally, up from 38% in 2023. Of the same physicians, 88% call safety and efficacy validation critical, 86% call data privacy important, and 85% want a say in which AI their organization adopts.
The pilot is the testable MVP of one AI use case on your data, planned at four to eight weeks. Scoping, two to three weeks before it, produces:
- A written scope for the use case that goes to pilot first, with the outcome it should move.
- Pass/fail criteria agreed with the clinicians who will review the output.
- A memo on which regulatory lane the feature sits in, explained in our healthcare app development guide.
- A build estimate for the pilot.
AI features usually land inside a larger product and inside the EHR. See our healthcare app development services, healthcare data analytics services for predictive models on operational and claims data, EHR integration services, and the healthcare software development hub. How we handle PHI under a business associate agreement has its own page, and for AI outside healthcare see our AI integration services.
Clinical AI Solutions We Build
Six use cases account for most of the AI healthcare solutions we are asked to build. All six are built with an evaluation set and logging that shows what the model saw and what it produced.
01
Voice AI and Conversational AI for Healthcare
Voice AI for healthcare on RAG over your protocols: the assistant listens and speaks, keeps conversation memory, and escalates to a human. Clinician-facing, as built for Shift.AI, or patient-facing.
01
Voice AI for healthcare on RAG over your protocols: the assistant listens and speaks, keeps conversation memory, and escalates to a human. Clinician-facing, as built for Shift.AI, or patient-facing.
02
AI Patient Intake and Triage
Conversational intake in place of forms. Symptoms are captured as structured data and mapped to your triage protocol; urgency follows your rules and is confirmed by staff before routing.
02
Conversational intake in place of forms. Symptoms are captured as structured data and mapped to your triage protocol; urgency follows your rules and is confirmed by staff before routing.
03
Ambient Documentation and After-Visit Summaries
An AI medical scribe transcribes the visit, drafts the note in your templates, and writes a plain-language after-visit summary. Billing code suggestions stay within what the signed note supports.
03
An AI medical scribe transcribes the visit, drafts the note in your templates, and writes a plain-language after-visit summary. Billing code suggestions stay within what the signed note supports.
04
Clinical Decision Support
Guideline retrieval at the point of care and risk flags with the evidence behind them, for the clinician to act on. HTI-1 source attributes are kept from day one.
04
Guideline retrieval at the point of care and risk flags with the evidence behind them, for the clinician to act on. HTI-1 source attributes are kept from day one.
05
Patient Communication Assistants
Drafts of portal replies, appointment reminders, pre-visit instructions, and follow-up outreach in the clinic’s voice, with guardrails on clinical content. Sent through your portal or another consented channel.
05
Drafts of portal replies, appointment reminders, pre-visit instructions, and follow-up outreach in the clinic’s voice, with guardrails on clinical content. Sent through your portal or another consented channel.
06
Knowledge and Research Assistants
Retrieval assistants over standards of care, formularies, protocols, and internal policies, the most common physician use of AI at 39%. Answers cite the protocol version in force and are scoped to the reader’s role.
06
Retrieval assistants over standards of care, formularies, protocols, and internal policies, the most common physician use of AI at 39%. Answers cite the protocol version in force and are scoped to the reader’s role.
What Our Healthcare AI Development Includes
Use-Case Scoping
Scoping takes two to three weeks. It ranks your candidate use cases, checks data readiness for each, and picks the one that goes to pilot first. It also defines the clinician review loop: a clinician reviews any output that affects care. The evaluation criteria are set before anyone talks about rollout. A pilot team is an ML engineer, a backend engineer, and a project manager; the reviewing clinicians are yours, and we design the review loop around their time.
Retrieval-Augmented Generation on Verified Sources
Knowledge bases are built from approved documents only, with a citation on each output and retrieval logs that show what the model saw. Shift.AI runs its voice assistant on this retrieval pattern; our compliance documentation system over more than 4,000 documents adds the source for each generated statement.
Protected Health Information and Model Vendor Terms
Identifiers are stripped before data reaches a model wherever the use case allows. Protected health information (PHI) goes only to endpoints covered by a business associate agreement (BAA) with that provider. When the data cannot leave, it goes to an open-weight model (one you host yourself) in your cloud account. We sign a BAA with you, and our engineers work with PHI only under it, through the access controls your team approves.
Evaluation, Validation, and Monitoring
Clinicians help build a held-out evaluation set (cases the model never saw in development), and accuracy and safety metrics are agreed before the build. We back-test against known cases and monitor production for drift, the accuracy loss that comes as real cases diverge from the evaluation set. A model card (a one-page record of what the model is, what it was trained on, and how it scores) and the source-attribute set are assembled from the evaluation pipeline. The clinical and fairness sections are written with your team.
Regulatory Boundary Design
We map each feature against the FDA clinical decision support guidance and the general wellness policy. The default is the non-device criteria unless you choose a device pathway. Predictive features that ship inside certified EHR technology carry the ONC HTI-1 source attributes. For products placed on the EU market, we flag the likely EU AI Act risk class at scoping for your counsel to confirm.
Voice, EHR, and Workflow Integration
Speech-to-text and text-to-speech vendors sign a BAA. A SMART on FHIR launch opens the feature inside the EHR with the patient already in context. HL7 v2 and FHIR R4 read and write where the EHR allows it.
Scoping takes two to three weeks. It ranks your candidate use cases, checks data readiness for each, and picks the one that goes to pilot first. It also defines the clinician review loop: a clinician reviews any output that affects care. The evaluation criteria are set before anyone talks about rollout. A pilot team is an ML engineer, a backend engineer, and a project manager; the reviewing clinicians are yours, and we design the review loop around their time.
Case Studies
Shift.AI
Shift.AI
AI support tool for clinicians, built for Inclusive Health.
We built a mobile app that gives clinicians brief, trauma-informed AI support in real time, with bidirectional voice chat, guided meditation and grounding flows, and conversation memory across sessions. The MVP was delivered in 3 weeks.
Revvel
Revvel
AI-powered women’s health platform.
We developed an AI platform that turns elite health experts into digital twins and reviews years of patient data in under 60 seconds, delivering risk assessments up to 10x faster and cutting time-to-care from days to minutes.
InfiniHealth IPA
InfiniHealth IPA
Medical claims automation for a US healthcare IPA.
We built an internal platform that replaces spreadsheet-driven claims work with an automated pipeline: payer CSV imports with upsert logic, automatic validation of paid amounts against provider contracts, dispute tracking, payment batching, and EOP generation.
Hola Salud
Hola Salud
Digital healthcare platform for Mexico.
We built a complete healthcare ecosystem, including a patient app, a doctor’s portal, and an admin backend, with self-registration, appointment booking, medical records, and integrated Stripe and Oxxo payments.
Flexible Developer Hiring Model
Your priorities define the way we work together — from how much control you want to the level of flexibility your project demands.
Team Augmentation
Quickly scale your workforce with skilled developers who integrate seamlessly into your in-house team, filling talent gaps and boosting productivity.
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Dedicated Development Team
A whole, cross-functional team working exclusively on your project, providing end-to-end development, management, and long-term support.
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Software Development Outsourcing
Delegate your entire project to our specialists and focus on business growth, while we handle development, delivery, and quality.
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Why Plus8Soft?
01
Experience
Multiplied by AI
Multiplied by AI
We blend deep engineering expertise with cutting-edge AI acceleration. By integrating intelligent tools into our workflow, we don't just write code—we engineer solutions faster and with higher precision.
02
Business-First
Transparency
Transparency
We look beyond the ticket. Our team operates with hyper-transparency, treating your budget and goals as our own. We align technical decisions with your business strategy to create real, measurable value.
03
Committed to
Overdelivery
Overdelivery
Meeting requirements is our baseline; exceeding them is our culture. Whether it's optimizing performance, refining UX, or anticipating future scalability, we consistently go the extra mile.
Healthcare AI Consulting and Development FAQ
What does your healthcare AI consulting include, and can we hire you for scoping only?
A written scope for the use case that goes to pilot first, the accuracy and safety bar the pilot is measured against, a regulatory lane memo, and a build estimate. If you have several candidate use cases, scoping starts with a prioritization pass and a data-readiness check for each. The scope is yours to take to any team. Clients who continue into the pilot keep the engineers who wrote the scope, so the estimate is one those engineers stand behind.
How long does a healthcare AI pilot take with Plus8Soft?
Scoping takes two to three weeks. The pilot, the testable MVP of one AI use case on your data with the clinician review loop in place, is planned at four to eight weeks after that. The figure is our planning estimate and is confirmed in the scope. Production rollout depends on integration depth and the validation your organization requires.
How much does healthcare AI development cost with Plus8Soft?
Scoping is quoted as a fixed fee before it starts. Pilots are priced per use case from the scope it produces, and AI features added to an existing product are estimated on their own. Integrated healthcare platforms with AI features start at $250,000. Model usage fees and any speech vendor costs are paid to those providers and scale with volume.
Which model providers see our patient data, and can we bring our own?
Only providers that sign a BAA for the API tier or endpoints the feature uses, and only after identifiers are stripped wherever the use case allows. Retention and training-use terms are confirmed in the provider's enterprise terms, with zero-retention endpoints where the provider offers them. If your security team has already approved a model or speech vendor, we work with it under the same rule. The retrieval and review layers sit behind a model interface, so the endpoint can be swapped without touching the feature.
How do you design an AI feature against the FDA non-device criteria?
We map each feature at scoping and design to the non-device criteria in the FDA clinical decision support guidance. Software that presents information and the evidence behind it for a clinician to act on can stay outside the device definition. Software that makes or drives the decision, or interprets images or signals for diagnosis, generally cannot. If a device pathway is the plan, we say so at the start.
How do you handle ONC HTI-1 source attributes?
If your AI ships inside certified EHR technology, we supply the 31 HTI-1 source attributes that the certified EHR developer publishes. They cover training data, inputs, fairness testing, performance, and validation schedule, and are assembled from the evaluation pipeline. If it does not, we keep the same set, because it is what a buyer or a certified EHR developer will ask for.
Can the AI feature run inside Epic or another EHR?
Yes, where the EHR allows it. A SMART on FHIR launch opens the feature inside the clinician's EHR session with the patient in context, and FHIR R4 and HL7 v2 carry data in and out. Read access is usually available once the health system approves the app; write-back is certified separately and takes longer. The approval path is described under our EHR integration services.
Can you add AI features to a product we already have?
Yes. We start with the use case and the data your product already holds, then add the model layer behind your existing API, so the feature ships without a rewrite. The clinician review loop and the evaluation set are built in the same way as for a new product.
