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 · Insurance

73% faster resolution. $2.1M saved annually.

A major North American P&C insurer deployed an AI triage agent to automate FNOL intake, severity scoring, and document review. Adjusters now focus on decisions, not data entry.

Industry: Insurance / P&C
Region: North America
Size: 2,000+ employees
Stack: LLM APIs · OCR pipeline · n8n
Engagement: 10 weeks build

Results

Data-driven outcomes.

73%

Faster first-contact resolution

60%

Reduction in manual claim reviews

92%

Auto-triage accuracy

$2.1M

Annual cost savings

The challenge

Manual triage was costing millions.

01
FNOL intake relied on phone calls and PDF forms. Adjusters manually keyed data into the claims management system, averaging 18 minutes per new claim.
02
Triage was inconsistent. Similar claims were routed to different severity queues depending on which adjuster handled them, leading to SLA misses on high-priority cases.
03
Document review (police reports, medical records, repair estimates) was a bottleneck. Adjusters spent 40% of their day reading attachments instead of making decisions.
04
Fraud detection was reactive. Suspicious patterns were caught only during downstream audits, weeks after initial payout.

The solution

Four layers of claims intelligence.

AI-powered FNOL intake

An LLM agent collects loss details via chat or phone transcript, extracts structured fields (date, location, parties, damage type), and creates the claim record in seconds.

Intelligent severity triage

The agent scores each claim on complexity, liability signals, and estimated reserve. Simple claims auto-route to fast-track; complex ones go to senior adjusters with a pre-built summary.

Document understanding pipeline

OCR + LLM extraction reads police reports, invoices, and medical records. Key facts are highlighted and mapped to claim fields so adjusters review a structured summary, not raw PDFs.

Real-time fraud signals

Pattern matching runs at intake: duplicate claimants, inconsistent narratives, and known fraud indicators are flagged before any payout is authorized.

"We went from 18-minute intake calls to sub-60-second AI-processed claims. Our adjusters finally have time to investigate instead of type."

VP of Claims Operations

A Leading North American P&C Insurer

Related services

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Production AI agents for claims, underwriting, support, and ops. From $40,000.

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