Clinical document agent
47% ↓
doctor documentation time
Ambient scribe-style AI that turns encounters into structured documentation. HIPAA-aligned, with sub-3-second response time and full audit logging.
Healthcare
AI agents for providers that need to ship under HIPAA.
AISD builds AI for healthcare providers — clinical document agents, patient onboarding and scheduling, RCM agents, provider copilots, and member-services deflection. HIPAA-aligned, on-prem deployment available.
6 proven use cases · 4–9 mo typical payback · BAAs available
Use cases
Six places AI moves the needle for providers.
Patient onboarding + scheduling
10–15%
more patients served
Conversational 24/7 intake collects demographics, insurance, and history. Books optimal appointment slots without back-and-forth.
RCM agents
30–50%
claim-cycle reduction
Eligibility checks, prior authorization, claims appeals — extract from EHR and payer correspondence, validate, route exceptions.
Provider copilots
2–3×
research throughput
Surface relevant prior cases, drug interactions, and care guidelines during the visit. Provider stays in control; the copilot does retrieval.
Care-pathway monitoring
Real-time
quality alerts
Parse clinical notes for adherence to evidence-based pathways. Flag gaps to care teams before they affect outcomes.
Member-services deflection
25–40%
auto-resolution
Handle plan questions, ID-card requests, and billing inquiries without human handoff. Hand off cleanly when judgment is required.
Compliance & data handling
Built for the constraints healthcare actually has.
HIPAA, state regulators, model risk management, payer audits — the constraints that make healthcare AI different from generic enterprise AI.
BAAs + PHI handling patterns
Standard BAAs available; data-minimization at every boundary; agents see only the fields needed; field-level audit logging.
On-prem / VPC deployment
For the most regulated workloads, we run open-weight models on dedicated infrastructure with no data leaving your perimeter.
Audit trail for regulators
Every decision logged with rationale — exportable for state DOI exams, OIG audits, and internal compliance review.
Human-in-the-loop on care decisions
Coverage and clinical decisions are AI-assisted, not AI-made. The agent surfaces evidence; the clinician or adjuster decides.
Featured case study
Clinical document agent reduced doctor documentation time by 47%.
A leading US healthcare provider chain deployed an AI agent for 24/7 patient onboarding and scheduling. 10–15% more patients served, 35% fewer no-shows.
Read the full case study →Outcome
47%
reduction in documentation time per encounter
Services for healthcare
Healthcare case studies
Frequently asked
Common questions.
What healthcare AI use cases does AISD ship?
Six patterns shipped to production. Clinical document agents (ambient scribe-style structured documentation from encounters). Patient onboarding + scheduling agents (24/7 conversational intake). RCM agents (eligibility, prior auth, claims appeals). Provider copilots (surface relevant prior cases, drug interactions, guidelines). Quality-of-care monitoring (parse notes for adherence to care pathways). Customer-service deflection for member services.
How does AISD handle HIPAA / PHI?
BAAs available; PHI handling patterns established. Data-minimization at every boundary — agents see only the fields needed. Field-level audit logging. On-prem / VPC deployment for the most regulated workloads, with open-weight models on dedicated infrastructure. SOC 2 Type II audit in progress; we deliver HIPAA-aligned engagements but do not yet hold a third-party HIPAA attestation.
What outcomes have AISD healthcare customers seen?
Documented outcomes from our healthcare engagements: 47% reduction in doctor documentation time on a clinical document agent. 10–15% increase in patients served via AI-driven scheduling. 35% reduction in no-shows. 85% reduction in scheduling wait times. 40% reduction in admin staff burden. Read /case-studies/healthcare-ai-agent for the full story.
How long does it take to build a production AI agent?
Working prototype: 2 weeks. Production-grade agent (with eval harness, guardrails, observability, and a runbook): 6–10 weeks. The prototype-to-production gap is where most projects fail — the prototype handles the happy path; production has to handle the long tail.
What does it cost to build an AI agent?
A production AI agent at AISD typically costs $40,000–$150,000 depending on complexity. Drivers: number of integrated systems, evaluation rigor required, compliance overhead, and ongoing operational scope. Prototypes alone are cheaper ($10k–$25k) but rarely worth it without a path to production.
How do you ensure AI features are reliable in production?
Five layers: an offline eval harness with golden test sets run on every PR; confidence thresholds and structured-output validation that gate downstream side effects; runtime observability — every model call logged with inputs, outputs, latency, cost; circuit breakers and deterministic fallbacks for every model dependency; and a weekly review ritual where prompt regressions get caught before they become incidents.
How is AISD different from a typical software development agency?
Three differences. First, every AISD engineer is senior — minimum 5 years building production software, with shipped AI features. Second, we publish hourly engagement bands and project ranges so you know roughly what an engagement costs before the first call. Third, we take fewer concurrent projects so a partner stays close to delivery.
Is AISD SOC 2 / GDPR / HIPAA compliant?
GDPR: yes — we handle EU personal data under standard data-processing agreements and apply data-minimization patterns (redaction at source, retention windows, right-to-erasure tooling). SOC 2: Type II audit in progress. HIPAA: we deliver HIPAA-aligned engagements (BAAs available, PHI handling patterns established) but do not yet hold a third-party HIPAA attestation. We will not claim certifications we don't hold.