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.

Compare . Team strategy

In-house vs. agency AI team

Both models work. The right choice depends on whether AI is your product or a capability your product needs. Here are the real numbers and trade-offs.

Updated . 2026-05-02 . 6 min read

Side-by-side

The comparison

Factor	In-house team	Agency (AISD model)
Time to first delivery	3-9 months. Recruiting, interviewing, onboarding, then building. Pipeline for senior AI talent is 60-90 days minimum	2-6 weeks. Team is pre-assembled and experienced. Discovery sprint to working prototype in 14 days
Annual cost (3-person team)	$750K-1.2M fully loaded. Base salaries ($180-250K each) plus benefits, equity, tooling, management overhead, and recruiting fees ($60-90K)	$300K-600K project-based. Pay for output, not headcount. No benefits, equity, or recruiting costs. Scale up/down without HR
Knowledge depth	Deep in your domain over time. Slow to build initially. Risk of knowledge silos if team is small	Broad across domains from day one. Patterns from dozens of AI deployments. Less depth in your specific business logic initially
Retention risk	High. AI engineers are the most sought-after talent in tech. Average tenure: 18-24 months. Losing one person can stall the whole team	Low. Agency manages its own retention. If one engineer leaves, they backfill without disrupting your project
Scalability	Slow to scale. Each new hire is a 3-month process. Scaling down means layoffs or idle headcount	Fast to scale. Add or remove capacity per sprint. No long-term commitments required
IP ownership	You own everything by default. Clear and simple	You own deliverables per contract. Make sure work-for-hire is explicit. AISD transfers all IP at handoff
Best for	AI is your core product differentiator and you need a permanent team building institutional knowledge	AI is a capability you need but not your core business. You want production systems without building a permanent team

Decision framework

Five questions to decide

01
Is AI your core product differentiator?
Yes: In-house. Build institutional knowledge. . No: Agency. Pay for outcomes.
02
Do you have 6+ months before you need production AI?
Yes: In-house is viable. . No: Agency ships faster.
03
Can you afford $750K+/year for a 3-person team?
Yes: In-house is an option. . No: Agency stretches budget further.
04
Do you have a senior AI leader to manage the team?
Yes: In-house can work. . No: Agency brings leadership built in.
05
Will your AI needs be constant for 2+ years?
Yes: In-house ROI improves over time. . No: Agency gives you flexibility.

The hybrid model

Most smart teams do both.

Start with an agency to ship your first production AI system fast. Use that time to hire your first internal AI engineer. Transfer knowledge, codebase, and runbooks. Scale in-house as the work justifies it.

AISD is built for this handoff. Every engagement ends with documentation, architecture diagrams, and a runbook your internal team can own. We'd rather make ourselves replaceable than create dependency.

In-house vs. agency AI team

The comparison

Five questions to decide

Most smart teams do both.

Not sure which model fits?