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.

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Agentic AI vs. generative AI

Generative AI is a capability: producing text, images, code, and audio on demand. Agentic AI is an architectural pattern: a system that uses a language model to plan and take multi-step actions toward a goal, calling tools along the way. Every agentic system uses generative AI under the hood. Not every generative system is agentic.

Updated · 2026-05-02 · 6 min read

What is generative AI?

Generative AI is any model that produces new content — text, code, images, audio, video — from a prompt. The underlying technology is usually a large language model (LLM) or a diffusion model trained on large datasets.

Examples of generative AI in production: ChatGPT answering a question, GitHub Copilot suggesting the next line of code, a marketing tool generating ad copy variants, a summarization API condensing a long document.

The pattern is the same in all of them: input prompt → model → generated output → done. The system responds and stops. No tools. No follow-up actions. No loop.

What is agentic AI?

Agentic AI is software that uses a language model to plan and take multi-step actions toward a goal, calling tools (APIs, databases, other systems) along the way. The minimal pattern: a model + a set of tools + a control loop.

The model doesn't just produce an answer — it decides what action to take next, executes it, observes the result, and decides what to do after that. This continues until the goal is met, a human is needed, or a safety condition is hit.

Examples: a customer-support agent that reads a ticket, looks up the order status, issues a refund, and emails the customer. A document agent that ingests contracts, extracts structured fields, validates them against business rules, and posts to an ERP. A sales-research agent that gathers news signals, drafts personalized outreach, and writes to a CRM.

Read the full primer: What is agentic AI? →

Key differences

The table below compares them across the dimensions that matter for scoping a production system:

Dimension	Generative AI	Agentic AI
Output	Text, code, images, audio	Actions in external systems
Steps	Single inference pass	Multi-step loop
Tools	None — model only	APIs, DBs, side-effecting actions
Memory	Context window	External state + context window
Failure mode	Hallucination	Tool errors, cost spirals, injection
Typical ROI	Productivity gains	Process automation + cost reduction

How they relate

Agentic AI and generative AI aren't competing technologies — agentic systems use generative AI as their reasoning engine. The language model inside an agent is doing exactly what it always does: taking a prompt and generating a response. The difference is what surrounds it.

In a generative system, the response is the end state. In an agentic system, the response is a decision — often a tool call — that feeds back into the next prompt. The generative capability stays constant; the architecture turns it from a content producer into an actor.

Think of it this way: generative AI is the engine. Agentic AI is the vehicle.

When to use which

Use generative AI when the value is in the content itself and a human will act on it:

Drafting copy, code, or structured data for a human to review and apply
Summarizing documents or threads for human consumption
Classifying or labeling items in a human-reviewed pipeline

Use agentic AI when the value is in taking action across systems — and a human reviewing every step would eliminate the efficiency gain:

High-volume, structured workflows (claims processing, order management, lead research)
Cross-system tasks where data needs to be gathered, transformed, and written back
24/7 customer-facing interactions that require action (not just answers)

Most production ROI lives in the agentic pattern — but start with generative if the workflow is new and the team isn't ready to own a long-running agent in production. You can graduate a generative system to agentic once you understand the inputs and success metrics.

If you're scoping a build: How to build an AI agent →