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 · SaaS

41% activation lift. 68% faster time-to-value.

A B2B SaaS platform replaced its static onboarding wizard with an AI copilot that personalizes every trial experience. Activation doubled. Support tickets halved.

Industry: SaaS / B2B platform
Region: Global (US HQ)
Size: 120 employees, 8K+ customers
Stack: LLM APIs · React · Postgres
Engagement: 8 weeks build

Results

Data-driven outcomes.

41%

Increase in trial-to-paid activation

68%

Reduction in time-to-first-value

52%

Fewer support tickets (first 14 days)

3.2x

ROI within 6 months

The challenge

Static onboarding was killing activation.

01
Trial users hit a wall within the first 10 minutes. The product had 200+ features, but the onboarding flow was a static 7-step wizard that ignored what the user actually needed.
02
Support was overwhelmed by 'how do I...' tickets from trial users. 38% of all support volume came from the first 14 days of a trial.
03
Activation rate (defined as completing a core workflow) was stuck at 23%. Product tours and tooltip walkthroughs had been tried and abandoned.
04
Enterprise prospects required custom onboarding calls with a solutions engineer. The team could only handle 12 per week, creating a 3-week backlog.

The solution

Four capabilities of the onboarding copilot.

Context-aware onboarding copilot

An LLM-powered assistant embedded in the product UI. It reads the user's role, industry, and stated goals, then builds a personalized onboarding plan and walks them through it step by step.

Interactive task completion

Instead of passive tooltips, the copilot performs actions alongside the user. It pre-fills forms, suggests configurations, and validates each step before moving on.

Proactive engagement triggers

If a user stalls, the copilot re-engages with contextual nudges: 'You set up your first pipeline but haven't added a data source yet. Want me to help?' Timing and messaging are LLM-generated per user.

Self-serve enterprise setup

For enterprise trials, the copilot handles SSO configuration, role mapping, and data import guidance that previously required a solutions engineer call.

"We spent two years tweaking our onboarding wizard. The AI copilot outperformed every iteration in the first month. Users tell us it feels like having a dedicated CSM."

Head of Product

A B2B SaaS Platform (8,000+ Customers)

Related services

AI agent development →

Copilots, assistants, and autonomous agents embedded in your product. From $40,000.

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