Honest ranges first: a focused custom AI automation — one workflow, integrated into your real systems, production-ready — typically lands between $30K and $75K, delivered in three to six weeks. Platform-scale systems with multiple workflows, deep compliance requirements, or greenfield product surfaces run into six figures. And the ongoing cost of operating a well-architected automation is usually a few hundred dollars a month, not a second salary.

Those aren’t hypotheticals. Two engagements we’ve published the numbers on:

The interesting question isn’t the sticker price. It’s why quotes for “the same project” range from $20K to $500K — and which factors you actually control.

The anatomy of a custom AI quote

Every credible estimate decomposes into five parts:

1. Discovery and requirements — Mapping the workflow, the systems, the edge cases, and the success metric. Skimping here is the single most expensive decision available to you (more below).

2. The build itself — Models, orchestration, and business logic. Counterintuitively, this is often the smallest slice. Modern model APIs have commoditized the “AI” part; the engineering around it is where the effort lives.

3. Integration — Connecting to your Jira, your EHR, your ERP, your GitHub. Cost scales with the number of systems and the quality of their APIs, not with the sophistication of the AI.

4. Validation — Proving it works before users see it: accuracy against real cases, compliance requirements, failure behavior. In regulated industries this is a first-class line item, not a rounding error.

5. Operations setup — Monitoring, cost controls, alerting, documentation, handover. The difference between software you own and a demo you inherited.

If a quote doesn’t itemize something like these five, ask why. Either the vendor hasn’t thought it through, or the missing parts will reappear later as change orders.

What makes costs balloon

Unclear requirements — by a wide margin. Ambiguity discovered mid-build is the most expensive kind: it triggers rework, re-integration, and re-validation all at once. This is the entire thesis of shift-left quality, and it applies to AI projects with interest: teams that invest in requirements up front routinely see rework drop by well over half.

Integration sprawl. Each additional system multiplies edge cases. A workflow touching two systems is a project; one touching seven is a program.

Compliance depth. HIPAA, financial audit trails, and government security standards add genuine engineering — traceability, explainability, access controls. Budget for it deliberately rather than discovering it in review.

Data readiness. If your data needs cleanup before AI can use it, that’s real work — but it should be scoped as its own phase with its own value, not buried inside the AI line item.

What ongoing costs look like

For the automation class of system, our published operating numbers are typical: roughly $400/month in infrastructure and API usage, plus about four hours a quarter of maintenance attention. Vendor-platform alternatives for the same healthcare workflow would have run $900–$1,200/month in license fees alone — the math is in our translation automation FAQ.

Two rules of thumb: usage-based API pricing beats per-seat licensing at team scale, and any system without cost monitoring will eventually surprise you.

How to think about ROI

Skip the abstract ROI model. Do this instead:

  1. Pick a friction point that already has a number — hours per sprint on translation files, monthly rework hours, support tickets per week.
  2. Price the friction annually. (340 rework hours/month at a $138 blended rate is $564K/year — that was a real number.)
  3. Compare against the build + a year of operations.
  4. Demand a baseline measurement before the build starts — it’s the only way you’ll ever prove the return.

When the target is chosen this way, payback in weeks is normal, because you’re deleting a cost that was already being paid monthly.

How to keep your own project in the healthy range

  • Start with one workflow, not a platform vision — platforms are earned, not purchased
  • Write the success metric down before contacting anyone
  • Insist on requirements rigor up front (it’s cheaper than any discount)
  • Ask every vendor: what does this cost to run in year two?

How we approach it

Auxiliary Digital scopes fixed-timeline engagements around measured friction: Gather establishes the requirements and the baseline, Build ships the system, Validate proves it — one team, three to six weeks for the automation class of problem, with the numbers reported in business terms.

If you want a real estimate instead of a range, schedule a consultation. Bring the friction point; we’ll bring the decomposition — and if the honest answer is that the ROI isn’t there, we’ll tell you that too.