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Pricing

Every project is scoped to what you actually need to ship, with a clear view of what moves the number. We have built production software since 1998, so the estimate reflects the work in front of us.

How we price

We scope each engagement to what it actually requires, then give you the number and the assumptions behind it. Two projects at the same notional day rate can differ by an order of magnitude in real cost, depending on scope, data, and how many systems the work has to touch. A flat rate card would hide the very things that determine what you pay.

NASA Kennedy Space Center and Ford are among the organizations running production systems that Convective built. The case studies show the scope behind those engagements.

Engagement models

Most work falls into one of a few shapes. Each is scoped and priced on its own terms, and many clients start with an assessment before committing to a larger build.

AI readiness assessment

A fixed-scope diagnostic. We look at your data, systems, and goals and tell you honestly where AI will pay off and where it will not. The cleanest place to start if you are not sure what is worth building.

AI strategy and training

Advisory and enablement work: roadmaps, architecture decisions, and hands-on training for your team. Usually scoped as a defined engagement with clear deliverables rather than an open-ended retainer.

AI implementation

We build and integrate the system: data pipelines, evaluation, and the production plumbing real software needs. Priced to the scope of what actually ships.

Build engagements

Custom software and application development, from greenfield products to modernizing systems that still run the business. Scoped per project once we understand the surface area and the constraints.

Team augmentation

Senior engineers embedded alongside your team for a defined period. Priced by the people and the duration, with the same senior-by-default standard as the rest of our work.

What drives cost

When we scope an engagement, three things move the estimate more than anything else. Knowing where you stand on each is the fastest way to a realistic number.

Scope
What actually has to ship, and how certain it is. A tightly defined build with a clear definition of done costs less to estimate and less to deliver than an open-ended exploration. We would rather narrow the first engagement than pad it.
Data readiness
Whether your data is accessible, clean, and structured enough to build on. The unglamorous data work is often where AI projects succeed or stall, and it is usually the single biggest swing in effort between two otherwise similar engagements.
Integration surface
How many systems the work has to touch and how cooperative they are. A self-contained tool is straightforward; something that has to sit inside legacy systems, auth, and existing workflows carries more integration and testing work.

The honest version: the more defined the scope and the more ready your data, the tighter and lower the estimate. When something is uncertain, we will say so and propose a smaller first engagement to settle it before committing to the larger build.

Want a number for your project?

Tell us what you are trying to build and where your data and systems stand today. We will scope it honestly, including when the right answer is a smaller first step.

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