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 · QA modernization

Regression testing: 3 weeks → 4 hours. Same engineers, new tooling.

A SaaS customer was burning weeks per release cycle on manual regression testing. We built an AI-augmented QA pipeline — test generation, E2E automation, performance baked into CI — and turned weeks of work into hours.

Industry: B2B SaaS
Region: United States
Engagement: AI-modernization retainer
Stack: Playwright · OpenAI · custom test gen
Timeline: 12 weeks rollout

Results

Numbers that speak.

70%

Time saved on test case creation

98%

Reduction in regression cycle time

65%

Drop in bugs reaching production

0 → ✓

Performance test coverage built in

Before vs. after

Five metrics, five step-changes.

Metric	Before	After
Test case creation	5 days	1.5 days
Regression testing	3 weeks	4 hours
Bug escape rate	High	Low
Performance testing	None	Full coverage
Documentation	Sparse	Comprehensive

The pipeline

Six stages. End-to-end automated.

01
Bug reports + requirements
Inputs flow into the pipeline — feature specs, bug tickets, customer-reported edge cases.
02
AI test generator
LLM-driven generator creates test cases from natural-language requirements. Human reviews + approves before they enter the suite.
03
E2E test runner
Playwright executes the generated tests on every PR. Parallel execution; sub-5-minute feedback loop.
04
Performance monitor
Load and stress testing baked into CI. Performance regressions caught before merge, not after deploy.
05
Quality gate
Pass / fail decisions automated against thresholds. Human review only on edge cases.
06
Auto documentation
Test results, coverage maps, and release notes generated from the test runs themselves.

"Our QA team went from being the bottleneck on every release to being the team that catches things before they ship. Same people, fundamentally different throughput."

Director of Engineering

B2B SaaS customer

Related service

AI modernization →

Embed AI capabilities (copilots, intelligent search, AI-paired tooling) into existing software. From $60,000.

Regression testing: 3 weeks → 4 hours. Same engineers, new tooling.

Numbers that speak.

Five metrics, five step-changes.

Six stages. End-to-end automated.

Bug reports + requirements

AI test generator

E2E test runner

Performance monitor

Quality gate

Auto documentation

Burning sprints on regression?