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 · Legacy modernization

15 years of legacy → modern cloud platform. 9 months. Zero downtime.

Original estimate was 2+ years and several million dollars. AI-paired engineering plus phased migration delivered the new platform in nine months, with no production downtime during cutover.

Industry: Enterprise / B2B SaaS
Region: Europe
Project size: 500K LOC monolith
Timeline: 9 months (vs 2+ year estimate)
Stack: React + .NET 8 + cloud-native

Results

Outcomes worth re-quoting the project for.

9 mo

Total migration time vs. 2+ year estimate

73%

Faster dev cycles post-migration

Production downtime during cutover

60%

Infrastructure cost reduction

Before

What we inherited.

C# WPF desktop

Windows-only client. Deployment via installer. No mobile, no remote access.

WebForms legacy layer

Server-side rendering, tightly coupled to monolith. Painful to test, slow to ship.

Monolithic database

All concerns in one schema. Migrations took hours. Schema changes risky.

After

What we shipped.

React frontend

Modern SPA. Mobile-responsive. Same codebase serves desktop, tablet, phone.

.NET 8 API

Clean architecture, tested, dockerized. New endpoints in days, not weeks.

Cloud-native infrastructure

Azure / AWS deploy with autoscale, monitoring, blue/green cutover.

Decomposed data layer

Service-aligned schemas. Each microservice owns its data, versioned independently.

"Modernized a 12-year-old legacy system in under nine months with zero downtime. I didn't think it was possible."

Anja Müller

Head of Engineering, LogiCore

Related service

Legacy modernization →

Strangler-fig migrations, AI-paired refactors, zero-downtime cutovers. From $80,000.