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 · Real Estate

90% faster listings. 35% more engagement.

A multi-state brokerage deployed an AI listing agent to automate property descriptions, photo curation, market analysis, and multi-platform publishing.

Industry: Real Estate Tech
Region: United States
Size: Multi-state brokerage
Stack: LLM APIs · Vision models · MLS integration
Engagement: 6 weeks build

Results

Data-driven outcomes.

90%

Reduction in listing creation time

More listings published per agent per week

35%

Increase in listing engagement

$920K

Annual productivity gains

The challenge

Listing creation was eating agent time.

01
Creating a property listing took agents 45-90 minutes: writing descriptions, selecting and ordering photos, researching comparable sales, and formatting for multiple platforms.
02
Listing quality was inconsistent. Top agents wrote compelling descriptions; newer agents produced generic copy. This directly affected time-on-market and client satisfaction.
03
Photo selection was subjective and time-consuming. Agents would review 50-100 photos per property and often choose poor hero images that underperformed in search results.
04
Market data research was manual. Agents spent 20+ minutes pulling comparable sales, neighborhood stats, and pricing context for each listing.

The solution

Four layers of listing intelligence.

AI property description generator

The agent analyzes property features, photos, and market data to generate compelling listing descriptions. Adapts tone for luxury vs. starter homes, highlights unique selling points, and includes SEO-optimized keywords for listing portals.

Intelligent photo curation

Vision models score and rank property photos by quality, composition, and appeal. Automatically selects hero images, orders the gallery for maximum engagement, and flags photos that need retaking.

Automated market analysis

Pulls comparable sales, days-on-market trends, and neighborhood data from MLS. Generates a competitive positioning summary that informs pricing strategy and listing copy.

Multi-platform publishing

One-click publish to MLS, Zillow, Realtor.com, social media, and the brokerage website. Each platform gets optimized formatting and copy length. Updates propagate automatically when listing details change.

"Our agents used to spend an hour per listing. Now they review an AI-generated draft in five minutes and hit publish. The descriptions are better than what most of them were writing manually."

Director of Operations

A Multi-State US Brokerage