AISD is an AI-native software development company that builds production AI for mid-market and enterprise teams. Three core services: AI Modernization (embedding AI into existing products — copilots, intelligent search, predictive analytics), AI Agents (autonomous workflows for support, document processing, sales outreach), and AI Workflow Automation (n8n, Zapier, Make, Clay).

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.

How long does it take to build an AI MVP?

Most AI MVPs at AISD ship a usable version in 4–8 weeks. Week 1 is a discovery sprint. Weeks 2–6 are the build, with weekly demos and a working version by week 4. Weeks 7–8 harden, document, and hand off.

What does an AI MVP cost?

AISD AI MVPs typically range $45,000–$120,000 depending on scope. Drivers: number of model integrations, complexity of retrieval/data layer, custom UI surface area, and compliance requirements. We publish indicative bands on the pricing page so buyers can budget before the first call.

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 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 does pricing work — fixed-price, T&M, or retainer?

All three. Fixed-price for AI MVPs and agent builds where scope is well-defined after a discovery sprint. Time-and-materials for staff augmentation, billed monthly with a not-to-exceed ceiling. Retainer for ongoing optimization, eval-harness operations, and managed AI services — flat monthly fee for a defined scope of capacity.

Case study · Healthcare

10–15% more patients served. 35% fewer no-shows.

A leading US healthcare provider chain deployed an AI agent for 24/7 patient onboarding and appointment scheduling. Throughput up; no-shows and admin burden down.

Industry: Healthcare / patient services
Region: United States
Size: 500+ employees
Stack: Cursor · Databricks · LLM APIs
Engagement: 12 weeks build

Results

Data-driven outcomes.

10–15%

Increase in patients served

85%

Reduction in scheduling wait times

35%

Fewer no-shows

40%

Less admin staff burden

The challenge

Manual scheduling was leaving patients behind.

01
Scheduling was handled manually by call center staff, with peak-hour hold times exceeding 30 minutes just to book an appointment.
02
No-show rates averaged 18–22%, costing the provider network millions in lost revenue and wasted provider time across hundreds of locations.
03
Patient onboarding required paper forms or clunky web portals, leading to incomplete data, duplicate records, and delays at point of care.
04
Follow-up scheduling was inconsistent. Many patients fell through the cracks, missing critical post-visit appointments.

The solution

Four pillars of intelligent patient services.

24/7 AI conversational intake

An LLM-driven agent handles patient onboarding through natural conversation — chat, phone, or portal. Demographics, insurance, and history collected without forms or hold times.

Intelligent appointment matching

The agent cross-references provider availability, patient preferences, location, and urgency to book optimal slots in seconds. No back-and-forth.

Databricks-powered patient data pipeline

All patient data flows through Databricks for real-time cleansing, de-duplication, and enrichment. HL7/FHIR feeds normalized and pushed to downstream systems.

Smart reminders + no-show prevention

Multi-channel reminders (SMS, email, push) with escalating nudges. The system predicts no-show risk and proactively offers rescheduling.

"Our call center used to be the biggest bottleneck in patient access. Now the AI agent handles the majority of onboarding and scheduling — staff focus on patient care instead of phone queues."

Chief Operating Officer

A Leading US Healthcare Provider Chain

Related services

AI agent development →

Production AI agents for support, document processing, sales, and ops. From $40,000.

See AI for insurance carriers →