How much does it cost to build an AI system in 2026?

Most AI vendors answer this question with "it depends," then ask for a discovery call. It does depend — but the ranges are knowable, the cost drivers are knowable, and you should be able to sanity-check a quote before you ever talk to anyone. We publish our own pricing, so we'll publish the market's too.

What "an AI system" actually means

The reason quotes vary by 10× is that the phrase covers wildly different things. A demo that answers questions about a few documents is a weekend project. A system your operations team relies on every day is not. The difference is everything around the model:

• Measured accuracy — an evaluation set built from your real data, so "it works" is a number, not an impression.
• Integrations — the system reads from and writes to the software you already run, instead of creating another inbox.
• Guardrails and human review — low-confidence cases route to a person; the system knows what it doesn't know.
• Monitoring and maintenance — accuracy is re-checked as models change and your data drifts.

That gap is why, per S&P Global research, 88% of AI pilots never reach production. The demo was cheap; the system was never actually scoped or priced.

The 2026 price landscape

Approach	Typical cost	Best when
Off-the-shelf AI SaaS	$20–$150 per user/month	Your workflow matches the product's template closely; light integration needs
Freelancer / automation agency	$5K–$50K per project	Internal tools and prototypes where downtime and errors are cheap
Boutique AI engineering firm	$80K–$250K per system	A workflow specific to your business where accuracy is measurable and errors are expensive
Large consultancy / SI	$500K–$2M+	Enterprise-wide programs, heavy procurement and compliance apparatus
In-house AI team	$600K–$900K per year	AI is core to your product, or you're operating several systems already

Two notes on that table. First, the in-house figure is just two senior AI engineers and part of a product manager at US fully-loaded cost — before infrastructure, before tooling, and before the months it takes to hire them. Second, cheap and expensive both fail in predictable ways: the $15K build usually dies at the first edge case in production, and the $1M program often produces a strategy deck before it produces software. MIT research covered by Fortune found the large majority of enterprise generative-AI pilots fail to reach production — and that projects built with external partners succeed at roughly twice the rate of internal-only builds.

What actually drives the price

Within the custom-build range, four factors move the number far more than the choice of AI model does:

1. The accuracy bar. Going from 90% to 98% accuracy is not a 9% increase in work. The last few points come from systematically hunting edge cases, expanding the eval set, and adding human-review routing for the cases that remain. High-stakes workflows justify it; low-stakes ones don't.
2. Integrations. A system that reads a shared inbox and writes to one database is the base case. Each additional system — your ERP, your case-management tool, your document store, your SSO — adds real engineering, especially when APIs are old or undocumented.
3. Data quality and variety. One clean document template is easy. Scanned PDFs in fourteen layouts from hundreds of counterparties is where extraction projects earn their budget. This is usually discovered, not estimated — which is what a pilot is for.
4. Compliance and deployment constraints. Deployment inside your own VPC or on-premise, audit trails, and data-handling agreements add cost — less than most people fear, but not zero.

The costs nobody puts in the quote

Two line items are routinely missing from proposals and account for most post-launch disappointment:

Evaluation. If a vendor can't tell you how accuracy will be measured — on your data, against a defined standard of "correct" — the number in the sales deck is an anecdote. Building the eval set is real work (typically 10–20% of a project) and it is the part that makes every other number trustworthy.

Maintenance. AI systems degrade without anyone touching them: model providers update or retire models, your document mix shifts, volumes grow. A system that shipped at 97% accuracy can quietly slide to 90% in a year. Budget $3K–$10K per month for monitoring, re-running evals, and fixes — or budget for an unpleasant surprise instead.

How we price it

Our pricing is public because we think you should be able to check it against everything above — and because "it depends" is how open-ended consulting meters get justified.

• 30-Day Production Pilot — $9,500 fixed. One workflow live on your real data, a written accuracy report, and a go/no-go production plan with real costs. Fully credited toward the build if you proceed. If AI turns out to be the wrong answer for your workflow, we say so in writing — that outcome costs $9,500 instead of a six-figure lesson.
• Production Build — $80K–$250K. Scoped in the pilot's Week-4 plan, so the estimate rests on measured evidence from your data rather than a discovery-call guess.
• Reliability Retainer — from $5K/month. The maintenance line item, made explicit: evals re-run as models and data change, drift monitoring, continuous improvement.

We work on text-heavy back-office workflows — contract and document intake, operations paperwork, internal knowledge agents — for mid-sized US companies. We don't build voice or call-center AI, and we don't serve banks, lenders, or asset managers, so if that's your project, the table above is still yours to keep.

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