Component Selection and Composition
Choosing retrieval, generation, rules, and conventional logic components and how they combine, since most capabilities need less AI than initially proposed.
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Hire Healthcare AI Solution Architects
Healthcare AI solution architects design how a specific AI capability is built and connected. They select components, define data flows and integration points, set the evaluation and failure behavior for that solution, and produce the design engineering builds against, within whatever portfolio standards the organization has set.
This is solution-level design rather than portfolio strategy. The architect decides how this capability works: what retrieves, what generates, where the human reviews, what happens when a dependency fails, and how it reaches clinical workflow. Getting that wrong produces a capability that demonstrates well and cannot be deployed. Our hire dedicated developers hub covers the engineering roles that build it.
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What Solution Architects Design
The deliverable is a design engineers can implement without inventing the hard decisions themselves. Component selection, data flow, failure behavior, and integration approach all belong here. The work below reflects that, drawing on the integration patterns described in our healthcare integration services.
Data Flow and Boundary Design
Mapping what data moves where, including what leaves your environment, so privacy and residency constraints shape architecture rather than being retrofitted.
Integration and Delivery Approach
Determining how output reaches clinical workflow, whether embedded, queued, or written back, which affects adoption more than any other design choice.
Failure and Degradation Behavior
Specifying what happens when a model is slow, unavailable, or returns something unusable, so clinical function never depends on an AI service being healthy.
Evaluation and Monitoring Design
Defining what will be measured before and after deployment, so the capability ships with the instrumentation that makes it maintainable.
Human Review Integration
Designing where review sits in the workflow and what reviewers see, so verification is practical at realistic volume rather than nominally present.
Healthcare Context This Role Requires
Solution architecture in healthcare is constrained by clinical workflow tolerance and data movement rules more than by technical options. A design that ignores either produces something correct that cannot deploy. The context below spans the healthcare work you assign.
01
Workflow Tolerance Constrains Design
Clinical users abandon capabilities that add steps or latency. Architecture must fit within seconds and clicks rather than optimizing for elegance.
02
Data Boundaries Are Architectural Inputs
What may leave your environment is determined by policy and agreements. Architecture works within that rather than proposing designs requiring exceptions.
03
Most Capabilities Need Less AI
Retrieval without generation, rules instead of models, and conventional automation frequently meet the requirement more cheaply and with less risk.
04
Degradation Must Be Designed
AI services fail. Architecture that lets an outage block documentation or record access is unacceptable regardless of the capability’s value.
05
Evaluation Belongs in the Design
Capabilities shipped without measurement cannot be safely changed. Instrumentation is an architectural requirement rather than a later addition.
06
Architecture Does Not Change Clinical Limits
Design decisions do not alter what software may determine. Human review before clinical effect and safety enforcement in code apply to every solution.
Skills This Role Requires
This work combines system design with AI operational knowledge and clinical workflow understanding. The differentiating skill is restraint, since the architecture that demonstrates best is rarely the one that deploys. The competencies below reflect that. Weight integration and failure design above component breadth.
AI Solution Composition
Designing how retrieval, generation, rules, and conventional components combine, with clear boundaries so each part is independently testable.
Clinical System Integration Design
Determining how capability connects to records and workflow, drawing on interface approaches covered in our FHIR API development work.
Data Flow and Privacy Architecture
Mapping data movement against policy constraints, minimizing what leaves the environment and documenting what does for review.
Failure Mode and Resilience Design
Specifying timeouts, fallbacks, and degradation behavior so clinical function continues when AI components are unavailable or slow.
Evaluation Architecture
Designing measurement into the solution, including what test data is needed and what production monitoring will capture after deployment.
Design Documentation for Builders
Producing designs engineers can implement without reinventing decisions, with rationale recorded so future maintainers understand why.
How We Evaluate Solution Architects
The distinguishing question is what they designed out. Architects who included every proposed component produced solutions harder to build, operate, and defend. Our assessment centers on restraint, failure design, and integration realism. Our delivery process includes review points where you can reassess fit.
Components They Removed
We ask what they designed out of a proposed solution. Architects who included everything requested did not exercise design judgment.
Failure Behavior Specification
We ask what happens when the model is unavailable. Designs without defined degradation make clinical function depend on a service that will fail.
Integration Realism
We ask how output reached clinical workflow. Architects who deferred this produced designs that stall at the last step, which is where AI projects die.
Evaluation in the Design
We ask what measurement the design included. Solutions shipped without instrumentation cannot be assessed or safely modified afterward.
Data Boundary Handling
We ask how privacy constraints shaped the architecture. Architects treating these as later concerns produced designs requiring redesign after review.
Verified Architecture Experience
We describe which solutions each architect designed and what reached production. We do not claim vendor or cloud certifications for architects who lack them.
Engagement Options for Solution Architecture
Architecture engagements are short and precede build. Structures below reflect that, and our engagement models accommodate design-only or design-through-build arrangements.
Design Engagement Before Build
A time-boxed period producing solution design, integration approach, failure behavior, and evaluation plan that your team or ours then implements.
Design Boundaries, Data Movement, and Clinical Limits
Architecture decisions determine data movement and failure behavior, both of which carry clinical and privacy consequences. We build to HIPAA-aligned practices where HIPAA applies; software cannot be HIPAA certified. Where intended use may create diagnostic or treatment claims, SaMD classification is assessed during discovery with your regulatory advisors.
01
Clinical Function Never Depends on AI
Designs ensure that AI unavailability degrades gracefully rather than blocking documentation, record access, or any core clinical operation.
02
Human Review Designed Into the Path
Where output influences care, review is architectural rather than procedural, with the workflow cost acknowledged in the design.
03
Data Movement Minimized and Documented
Architecture limits what leaves your environment and records what does, so review has a factual basis rather than an assurance.
04
Safety Enforcement Outside the Model
Deterministic checks for safety-critical content are architectural components rather than instructions, since instructions do not enforce.
05
Sensitive Capability Design Care
Solutions touching behavioral health require tighter boundaries. We built CHIPSS, a behavioral health system, where segmentation shaped architecture throughout.
06
Designs We Would Not Produce
We would not design solutions placing clinical determinations in software, making clinical function dependent on AI availability, or shipping without evaluation.
Cost to Engage Solution Architecture
Architecture engagements are small relative to build and reduce build cost by settling decisions before implementation. The tiers below describe build engagements the design informs. We publish no figures on delivery improvement, because that depends on your environment and capability.
MVP or Single Module
$40,000 to $80,000
Solution design and implementation for one capability, including integration approach, failure behavior, evaluation design, and delivery into workflow.
Full Platform Build
$80,000 to $200,000
Architecture and delivery across several capabilities with shared components, consistent integration patterns, evaluation infrastructure, and monitoring.
Enterprise Deployment
Starting at $200,000
Multi-facility solutions with environment variation, governance documentation, and architecture spanning several clinical systems and deployments.
Discovery Phase Scoping
Discovery is paid and time-boxed. It produces a solution design, integration feasibility assessment, data movement analysis, and an itemized fixed-scope estimate.
Cost Drivers to Expect
Capability complexity, integration surface, data movement constraints, latency requirements, failure behavior expectations, and clinical stakeholder approval cycles.
Ongoing Support Costs
Designs age as providers and systems change. Budget for architecture review after provider updates and integration changes in connected clinical systems.
Third-party licensing, cloud infrastructure, data subscriptions, and hardware are separate from engineering cost and itemised clearly.
Why Engage Architecture Through Taction
Two questions matter. Whether the architect designs for the last mile into clinical workflow, and whether they remove components rather than accumulate them. Taction Software has built healthcare software since 2013, more than twelve years, with over 200 healthcare projects delivered and ISO 27001 certification. Leadership brings more than twenty years of personal experience in the field, which is separate from company age.
Clinical Systems Built From the Inside
We built Voyant Health, an EHR platform, and CHIPSS, a behavioral health system, which informs where capability can realistically integrate.
Integration Depth Behind the Design
AI solutions succeed or fail on the last mile. Our interface work across clinical systems shapes designs that account for what integration actually requires.
Experience Under Regulatory Registration
We built Revive Ease and PainKare, both FDA-registered applications. That work informs how we treat intended use in solution design.
ISO 27001 Certified Security Management
Taction Software holds ISO 27001 certification covering our information security management practices, described under our certifications and compliance information.
We Design Less AI Than Proposed
Retrieval without generation and rules instead of models meet many requirements more cheaply. That design choice reduces the build we would otherwise deliver.
We Design the Integration First
Where the last mile is infeasible, we say so before the capability is built. That conversation occasionally ends a project that was already funded.
FAQs
Frequently Asked Questions
We review the capability, its target workflow, and your data movement constraints, then present architects with clinical AI experience for your approval.
One capability runs $40,000 to $80,000, multi-capability delivery $80,000 to $200,000, and enterprise deployment starts at $200,000. Inference, cloud, and licensing are itemized separately.
Our delivery history includes the Voyant Health EHR platform, the CHIPSS behavioral health system, and the FDA-registered applications Revive Ease and PainKare, within more than 200 healthcare projects delivered since 2013.
Clinical function continues. Designs include timeouts, fallbacks, and degradation behavior so documentation and record access never depend on an AI component being healthy.
For a bounded capability with a settled integration path, a senior engineer may suffice. Architecture matters most where integration, data movement, or failure behavior are unresolved.
That role sets portfolio-level strategy, model policy, and governance standards. Solution architects design how one specific capability is built and connected within those standards.
Share what you want to build, where the output must appear, your data movement constraints, your latency tolerance, and the engagement model you have in mind. We will design the integration first and say plainly if the last mile is infeasible. We do not promise instant matching or guaranteed availability.