AI Consulting Engagement Scoper: Turn Scope Into a Cost Range
This free AI consulting engagement scoper converts discovery length, solution complexity, feature count, and team size into a realistic cost range, and it is built for buyers evaluating proposals from AI consulting firms and system integrators. Enter your expected discovery phase, how complex each capability is, how many capabilities are in scope, and a defensible rate range for the team composition, and the tool returns total engagement weeks and a min-to-max cost estimate. Consulting proposals vary by three to five times for what sounds like the same scope statement, and the difference almost always traces back to complexity assumptions and team composition that were never made explicit.
Your numbers
Time to assess data readiness, define success metrics, and produce a fixed-scope statement of work.
Build weeks required per distinct capability, driven by how many systems and how much custom logic are involved.
Separate workflows or use cases in scope, not just screens or endpoints.
Average headcount actively billing across the engagement.
Lower bound of a defensible blended rate range for the team composition described.
Upper bound of a defensible blended rate range, typically reflecting specialized ML or security expertise.
Your results
Planning estimate only. Real engagement pricing depends on the specific firm, its rate card, and scope details uncovered during discovery.
Get your full engagement scoping worksheet
We will email you a personalized scope and cost range breakdown by phase and role, and a Netray consultant will follow up with a discovery proposal tailored to your systems.
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Fixed scope, time and materials, or retainer
Three engagement models cover almost every AI consulting arrangement. Fixed scope prices the statement of work as a whole and works well once discovery has removed most of the unknowns, giving budget certainty in exchange for less flexibility if requirements shift mid-build. Time and materials bills actual hours against the rate range this tool estimates, which fits discovery phases and early builds where scope is still being clarified, but requires active management to avoid drift. A retainer covers ongoing support, tuning, and incremental feature work after initial launch, priced as a fixed monthly commitment rather than a project total. Most engagements move through all three: T&M for discovery, fixed scope for the pilot build, and a retainer once the system is in production.
- Fixed scope requires discovery to have already answered the data readiness question; without it, fixed pricing is a guess wearing a contract.
- T&M shifts discovery risk to the buyer, which is appropriate when the buyer also retains the option to stop early.
- Retainers should specify response time and scope boundaries explicitly, not just a monthly hour allotment.
- Watch for firms that only offer fixed scope with no discovery phase; that pricing model usually hides risk in change orders instead of removing it.
Why complexity, not headcount, dominates the estimate
With the defaults here, two moderate-complexity capabilities at 12 weeks each produce 24 build weeks, plus 3 weeks of discovery, for 27 total weeks. At a small team of 2.5 consultants across a 3,000 to 5,500 dollar weekly rate range, that lands between roughly 202,500 and 371,250 dollars, with a midpoint near 286,875 dollars. Moving those same two capabilities to the complex tier roughly doubles build weeks and therefore roughly doubles cost, even with team size unchanged. Complexity tier is the single input worth the most scrutiny in any proposal, because it is also the easiest one for a vendor to understate to win a deal.
Red flags when comparing consulting proposals
Be skeptical of a proposal priced well below this range for equivalent scope; it usually means either the complexity tier was quietly downgraded, the team composition is thinner than described, or change orders are the intended mechanism for reaching the real price later. Be equally skeptical of a firm that cannot explain its rate range by role, since a blended rate that cannot be decomposed is hard to audit against actual staffing. A credible proposal should let you reconstruct roughly the numbers this tool produces from its own assumptions.
How Netray structures AI consulting engagements
Netray prices discovery separately from build specifically so complexity tier and team composition are based on your real systems rather than assumed at the proposal stage. We publish our rate range by role rather than hiding behind a single blended number, and we default to fixed scope for build phases once discovery has removed the major unknowns. For manufacturers running Infor SyteLine or LN, that discovery phase routinely reveals integration complexity that a generic proposal would have missed entirely.
Frequently Asked Questions
Why is there a min and max instead of a single number?
Because a single number implies false precision before discovery has actually happened. The range reflects genuine uncertainty in team composition and specialized skill requirements, such as whether the engagement needs a security-cleared engineer or a fine-tuning specialist, which shifts the effective rate even within the same complexity tier. Treat the midpoint as your planning number and the range as the bounds worth holding a vendor to.
How many features should I scope in one engagement?
Fewer than you think. Bundling four or five capabilities into a single statement of work compounds complexity risk across all of them at once, and a delay in one capability's data readiness routinely stalls the entire engagement. Most successful programs scope one or two capabilities per phase, prove value, then scope the next phase with real production experience informing the complexity tier.
Does a bigger team always finish faster?
Not proportionally. AI engagements have real coordination overhead and dependency chains, particularly around data access and evaluation, that do not parallelize cleanly. Doubling team size rarely halves calendar time; it is more realistic to expect a 30-40% schedule improvement at roughly double the cost. Use team size to add specialized skills you are missing, not primarily to compress the timeline.
What should be in the statement of work beyond price?
Named milestones with specific, testable acceptance criteria, an explicit data readiness assumption that the price is conditioned on, a change order process for scope discovered after signing, and a defined handoff or transition plan if the engagement ends without converting to a retainer. Our AI statement of work checklist covers the full list in more detail.
Get a scoped, role-by-role AI engagement proposal built from a real discovery phase against your systems.
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