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 · Decision framework

Build vs. buy AI agents

The build-vs-buy decision for AI agents is not the same as for traditional SaaS. Agents touch your data, your workflows, and your customers. The wrong choice locks you in or leaves you exposed. Here is how to decide.

Updated · 2026-05-02 · 8 min read

Side-by-side

The comparison table

Dimension	Build (custom)	Buy (off-the-shelf)
Time to first value	4-12 weeks (custom agent, production-ready)	1-2 weeks (vendor setup + integration)
Customization depth	Unlimited. Own the model, prompts, eval, and orchestration	Limited to vendor config. Prompt templates, workflow builders, pre-built connectors
Data control	Full. Data stays in your infra, your models, your audit log	Shared. Vendor processes your data on their infra under their DPA
Total cost (Year 1)	$50K-200K build + $2-8K/mo infra	$12-60K/yr SaaS + per-seat + usage fees
Total cost (Year 3)	$80-300K cumulative (drops sharply after year 1)	$36-180K cumulative (compounds linearly)
Vendor lock-in risk	None. You own the code, models, and infra	High. Migration = rebuild from scratch
Maintenance	Your team or retained partner. You control the roadmap	Vendor handles infra. You wait for feature requests
Eval and reliability	Custom eval harness, domain-specific test suites, regression CI	Vendor-level monitoring. Limited visibility into failure modes

Build when

Signals that favor building custom

Your AI agent touches regulated data (healthcare, finance, insurance) and you need full audit trails
The workflow is core to your product and competitive differentiation depends on it
You need to fine-tune models on proprietary data for domain-specific accuracy
Your volume exceeds 10K+ agent runs per month and per-usage pricing becomes prohibitive
You already have an engineering team capable of maintaining production AI
You need sub-second latency or custom orchestration that vendor APIs can't deliver

Buy when

Signals that favor buying

The use case is generic (meeting summarization, basic email triage, FAQ bots)
You need to validate the concept before committing to a custom build
Your team has no AI engineering capacity and hiring isn't on the roadmap
Volume is low (under 1K runs/month) and per-usage pricing is manageable
Time-to-market matters more than long-term cost optimization

The hybrid path

Start with a vendor. Graduate to custom.

The smartest teams we work with don't treat this as a binary choice. They use off-the-shelf tools to validate the use case, measure ROI, and learn what the agent actually needs to do in production. Then they build custom when three things happen:

01Volume exceeds the vendor's pricing sweet spot
02Customization requirements outgrow the vendor's config surface
03Data sensitivity or regulatory requirements demand full control

AISD runs this exact path with clients: a 2-week discovery sprint that tests the vendor option against a custom prototype. The output is a recommendation with real numbers, not opinion.

Build vs. buy AI agents

The comparison table

Signals that favor building custom

Signals that favor buying

Start with a vendor. Graduate to custom.

Not sure whether to build or buy? We'll tell you.