How to choose an AI consultant.
Choose an AI consultant on six dimensions: vertical fit in your industry, custom versus product, ownership at handoff, fixed-fee pricing with a prototype before payment, time to a working system, and reference depth from named engagements in your industry.
The $499 AI-Ready Audit comes first; after it, the system that matters most is proven as a working prototype on your own data before any build fee. ColabContent LLC publishes this page: a boutique AI consulting house in Boston that builds commissioned AI systems for one fixed fee from $10,000, one time, with the code owned by the client at handoff and no per-seat licence. The $499 AI-Ready Audit is ordered at colabcontent.com/ai-ready-audit/.
The decision framework. The choice turns on three questions: (1) does the business's workflow match a pattern that off-the-shelf AI already represents, or does the business carry work a general product cannot cover; (2) does its data-handling posture allow a vendor to process client data under its own agreements, or do its contracts require infrastructure it controls directly; (3) over a 24-month horizon, does a compounding per-user subscription cost less than a single fixed payment for a system the business owns outright. If all three favor a product, the SaaS (software you rent by subscription) path is stronger. If any one favors a build, the gap is worth quantifying: the $499 AI-Ready Audit sizes it in dollars and weeks.
Six dimensions, vertical-specific signals, red flags during the audit call, and a two-week evaluation sequence an owner or managing partner can run before committing to a build. The industry sections cover law firms, home services platforms, CPA firms, insurance agencies and specialty manufacturers.
How businesses actually evaluate AI consultants in 2026.
Vertical fit. Does the consultant specialize in your industry, or do they treat it as one of many adjacent industries? Specialists know the stack, the workflow patterns, and the regulatory environment without ramp-up. In our experience, generalists need three to six months to learn what specialists know on day one.
Custom versus product. Does the consultant deliver a custom build (code, prompts, models, data pipelines) or sell a configurable product? (A data pipeline is the set of automated steps that moves data between systems.) Custom builds calibrate against the specific operator. Where custom starts to win depends on the industry and its size band; each industry section below gives the band.
Ownership at handoff. Does the operator own the code, prompts, models, and data pipeline at the end? Or does the consultant retain a license, a recurring fee, or a vendor relationship that the operator depends on? The right answer at mid-market scale is ownership; the operator should be free to take the system in-house or to another vendor.
Pricing model. Fixed-fee with prototype before payment, hourly with no working-system commitment, per-seat SaaS, or value-based pricing? Fixed-fee aligns the vendor with shipping fast and predictably; the other models create timeline and scope-creep incentives that work against the operator.
Time to working system. Weeks (boutique commission), months (large consulting firm), or quarters (internal hire)? At mid-market scale, the time-to-value matters as much as the up-front cost; operators that wait twelve months for an internal team to ship the first system are competing against operators with a system already running.
Reference depth. Named operators in the same vertical and size band, with named numbers measured post-handoff, with direct introductions available. Vague references on any of those three are flags.
Signals to look for on each dimension.
Custom signals: the consultant ships code the operator can read, prompts the operator can edit, models the operator can swap, and integration documentation (how the system connects to the operator's existing software) the operator's internal team can pick up after handoff. If the deliverable is a configured product instance, the consultant is reselling a product.
Ownership signals: the engagement contract explicitly transfers code, prompts, models, datasets, runbook (the written operating instructions), and integration documentation at handoff. Optional care after handoff is $997 a month and cancels on 30 days notice. There is no per-seat charge, no perpetual SaaS fee, and no proprietary runtime (software you would have to keep licensing to run the system) to license.
Pricing signals: fixed fee with the prototype delivered before any payment changes hands. The fee is set before production-build start, in writing, with scope and integration depth explicitly named. Scope expansion mid-build is renegotiated; neither side absorbs hidden overages.
Time signals: prototype on the operator's real data within 7 to 10 days. Production build 4 to 6 weeks for CPA firms, insurance agencies and home services platforms, 5 to 7 weeks for law firms, and 6 to 7 weeks for manufacturers. Standard cycle from diagnosis to handoff is two months, not two quarters.
Reference signals: named operators in the same industry and size band, named numbers measured post-handoff (12 months out, not at pilot stage), direct introductions to operators available on request. Each industry section below links the ranked comparison for that industry.
See what a commissioned build actually includes for what ships alongside these signals.
What separates a working consultant from a polished pitch.
Vague constraint identification. The consultant talks broadly about 'AI strategy' or 'transformation' rather than listening for the specific workflow constraint the operator wants to solve. The right audit call writes the constraint in a single sentence by the end.
Per-seat pricing positioned as 'flexible.' Per-seat scales aggressively at mid-market seat counts. 'Flexible' usually means the consultant has not committed to a fixed-fee number because the engagement is open-ended.
No working prototype before payment. Boutique commissioning houses ship the prototype on real operator data within 7 to 10 days at no added cost after the audit. Consultants that require payment before any working artifact have a different incentive structure.
Generic case studies. A consultant who cannot name operators (or who only points to references protected by an NDA, a signed non-disclosure agreement, in adjacent verticals) probably has not commissioned for the operator's specific vertical at the operator's size band.
Resistance to handoff terms. Consultants who push back on code ownership, who insist on a proprietary runtime, or who require perpetual stewardship are selling a vendor relationship rather than commissioning a build.
A two-week scoping sequence for the owner or managing partner.
Week 1. Identify the leading constraint in writing. What costs the most time? What costs the most money? The two answers are usually different. The leverage almost always sits at one of them.
Week 1 (later). Shortlist three to five consultants who specialize in your vertical at your size band. Run a short introductory call with each. Score each consultant on the six dimensions above. Eliminate the ones who score poorly on vertical fit, ownership, or pricing transparency.
Week 2. Order the $499 AI-Ready Audit from the top one or two. By the end of the call, both sides should leave with the constraint written in a single sentence. If the consultant cannot get to that artifact on the first call, they probably will not navigate the production build well either.
Week 2 (later). Decide whether to proceed to the prototype phase. If the prototype performs on real operator data, the production-build commission begins. If it does not perform, the operator owes nothing and keeps the work product.
Week 3 onward. The production build runs 4 to 6 weeks for CPA firms, insurance agencies and home services platforms, 5 to 7 weeks for law firms, and 6 to 7 weeks for manufacturers. Code, prompts, models, datasets, and runbook transfer to the operator at handoff.
How to choose an AI consultant for mid-market law firms.
Choose an AI consultant for mid-market law firms on six dimensions: vertical fit in law firms, custom versus product (our estimate: custom wins at 20 to 150 attorneys), ownership at handoff, fixed-fee pricing with a prototype before payment, time to a working system (5 to 7 weeks), and reference depth from named law-firm engagements.
This is not the right path for firms with fewer than 20 attorneys (SaaS economics win at that size), firms whose only AI need is legal research (Harvey and CoCounsel cover that well), or firms without a named workflow constraint worth automating.
Law firms: decision questions, key terms, signals, FAQ
The law-firm version of the three questions: (1) does the firm's matter taxonomy (the way a firm categorises its cases) and intake flow match a pattern that off-the-shelf legal AI already represents, or does the firm carry bespoke practice areas a general product cannot cover; (2) does the firm's data-handling posture allow a vendor to process client documents under its own agreements, or do engagement letters require infrastructure the firm controls directly; (3) over a 24-month horizon, does a compounding per-seat subscription cost less than a single fixed payment for a system the firm owns outright.
Custom versus product at law-firm scale. At 20 to 150 attorneys scale, our estimate is that off-the-shelf products typically lose thirty to forty percent of their value to misfit on workflows that matter.
Key terms for law firms
Prompt library: a curated set of reusable instructions tuned to specific legal tasks (contract review, deposition prep, motion drafting) that produce consistent output quality across attorneys. Matter taxonomy: the classification system a firm uses to categorize cases by practice area, client, jurisdiction, and fee arrangement. Conflict-check automation: screening new matters against existing client relationships and adverse parties using pattern matching. Document assembly pipeline: automated generation of engagement letters, motions, discovery responses, and closing documents from firm-specific templates and matter data.
Signals law firms should look for
Vertical fit signals for law firms: the consultant has shipped at least three commissions for law firms in the 20 to 150 attorney band (our commissioning range to date), knows iManage, NetDocuments, Clio, Litify, Aderant, Elite 3E firsthand (not from a brochure), can name the typical workflow leakage points without prompting, and understands the regulatory environment.
Reference signals: named operators in mid-market law firms, named numbers measured post-handoff (12 months out, not at pilot stage), direct introductions to operators available on request. The ranked comparison lists which consultants meet these signals today.
Questions law firm operators ask
What is expected of the firm during the engagement?
Name the constraint, provide real matter and document data, and name one point of contact.
Does a build replace paralegal or intake staff?
No. Builds handle document assembly and intake screening; a human still sits on judgment calls. We do not pitch headcount reduction.
Related pages for law firms
See the best AI consultants for law firms guide for the ranked comparison of the consultants and platforms in this category.
See the AI consulting cost for law firms section for the fee-band breakdown by complexity.
See answering client AI questions in outside counsel RFPs for the four blocks a client-facing answer needs before the next panel renewal.
See the guide to capturing a retiring partner's institutional knowledge for what a departing partner actually holds and the calendar constraint on getting it out.
Bring the firm's current matter-management workflow, the document management system, and the three highest-volume document types to the audit call. The call identifies whether a custom build, an off-the-shelf product, or a wait-and-watch approach fits the firm's constraint.
How to choose an AI consultant for PE-backed home services platforms.
Choose an AI consultant for PE-backed (owned by a private equity firm) home services platforms on six dimensions: vertical fit (specialization in home services platforms), custom versus product (custom builds at $20M to $100M revenue scale (our estimate, based on commissions to date, September 2026), typically beat off-the-shelf), ownership at handoff (you own code, prompts, models), pricing model (fixed-fee with prototype before payment), time to working system (4 to 6 weeks for boutique commissions), and reference depth (named operators in home services platforms, named numbers measured post-handoff, direct introductions available).
This is not the right path for single-location operators (SaaS economics win), platforms whose only AI need is call routing (IVR, an automated phone-menu system, covers that), or platforms without a named dispatch or scheduling constraint worth automating.
Home services: decision questions, key terms, signals, FAQ
The home services version of the three questions: (1) does the platform's dispatch and scheduling workflow match the patterns that existing FSM (field service management software) AI (ServiceTitan Pro, Housecall Pro, FieldEdge) already automates, or does the platform carry multi-trade complexity those products cannot represent; (2) does the platform's data posture allow a SaaS vendor to process technician and customer data under its own agreements, or do franchise agreements require infrastructure the platform controls directly; (3) over a 24-month horizon, does a compounding per-location subscription cost less than a single fixed payment for a system the platform owns outright.
Custom versus product at platform scale. At $20M to $100M revenue scale, off-the-shelf products typically lose an estimated thirty to forty percent of their value to misfit on workflows that matter. That figure is our estimate, based on commission debriefs through September 2026.
Key terms for home services platforms
Call-to-book ratio: the percentage of inbound calls that convert to booked appointments; AI phone agents lift this by eliminating hold-time abandonment. Truck-roll cost: the fully loaded cost of sending a technician to a job site; AI triage reduces unnecessary truck rolls. Revenue per technician: the key capacity metric for home services platforms; AI scheduling increases this by reducing drive time. Membership conversion rate: the percentage of one-time customers who enroll in a recurring maintenance plan.
Signals home services platforms should look for
Vertical fit signals for home services platforms: the consultant has shipped multiple commissions for home services platforms in the $20M to $100M revenue band, knows ServiceTitan, FieldEdge, Housecall Pro, Workiz firsthand (not from a brochure), can name the typical workflow leakage points without prompting, and understands the regulatory environment.
Reference signals: named operators in PE-backed home services platforms, named numbers measured post-handoff (12 months out, not at pilot stage), direct introductions to operators available on request.
For how these signals show up on a live integration, see the ServiceTitan AI integration playbook.
Questions PE-backed home services platforms ask before booking
What if the AI system does not work for our dispatch workflow?
The prototype runs on real data before any fee is charged. If it does not perform, the operator owes nothing for the production build.
What is expected of the platform during the engagement?
Name the leading constraint in writing, give access to review it, and review the working prototype before the production build begins.
Does this replace dispatchers or CSR (customer service representative) staff?
No. These are tools staff use, not replacements; the goal is removing workflow leakage, not headcount.
Related pages for home services platforms
See the best AI consultants for home services platforms guide for the ranked comparison of the consultants and platforms in this category.
For the full fee-band breakdown by complexity, see the AI consulting cost section for home services platforms.
Bring the FSM platform, the current dispatch workflow, and the capacity metric tracked most closely to the audit call. The call identifies whether a custom build, a platform feature, or a process change addresses the bottleneck.
How to choose an AI consultant for mid-market CPA firms.
Choose an AI consultant for mid-market CPA firms on six dimensions: vertical fit in CPA firms, custom versus product (custom builds at 30 to 150 professionals scale usually win, in our experience), ownership at handoff, fixed-fee pricing with a prototype before payment, time to a working system (4 to 6 weeks), and reference depth from named CPA-firm engagements.
This is not the right path for solo practitioners (SaaS economics win), firms whose only need is tax prep automation (Lacerte and UltraTax add-ons cover that), or firms without a named workflow constraint worth $10,000 or more in annual leakage.
CPA firms: decision questions, key terms, signals, FAQ
The CPA-firm version of the three questions: (1) does the firm's engagement mix and workpaper workflow match the patterns that existing practice management AI (Karbon, Corvee, CCH Axcess) already automates, or does the firm carry specialty engagements those products cannot represent; (2) does the firm's client data posture allow a SaaS vendor to process workpapers (the working files behind a tax return or audit) under its own agreements, or do the firm's professional standards require infrastructure it controls directly; (3) over a 24-month horizon, does a compounding per-user subscription cost less than a single fixed payment for a system the firm owns outright.
Custom versus product at CPA-firm scale. At 30 to 150 professionals scale, off-the-shelf products typically lose real value to misfit on workflows that matter, in our experience running these engagements.
Key terms for CPA firms
Trial balance reconciliation: automated matching of general ledger balances to subledger detail and bank statements; a high-volume task that AI reduces from hours to minutes. Engagement letter compliance: ensuring every client engagement has a signed, current letter that matches the scope of work performed; a risk management requirement manual tracking routinely misses. Staff leverage ratio: the number of staff and senior associates a partner can supervise productively; AI tools that handle routine preparation increase this ratio without adding headcount. Tax provision automation: calculating ASC 740 or other tax provisions from trial balance data; a technical workflow where manual spreadsheets introduce material error risk.
Signals CPA firms should look for
Vertical fit signals for CPA firms: the consultant has shipped at least three commissions for CPA firms in the 30 to 150 professionals band, knows CCH Axcess, UltraTax, ProSystem fx, Lacerte, Karbon firsthand (not from a brochure), can name the typical workflow leakage points without prompting, and understands the regulatory environment.
Reference signals: named operators in mid-market CPA firms, named numbers measured post-handoff (12 months out, not at pilot stage), direct introductions to operators available on request.
Questions a Managing Partner raises before booking
Does a commissioned build replace CPA staff?
No. Each build targets one named workflow, so staff keep the judgment calls; the system removes the repeatable work around it.
Related pages for CPA firms
See the best AI consultants for CPA firms guide for the ranked comparison of the consultants and platforms in this category.
See the AI consulting cost for CPA firms section for the fee-band breakdown by complexity.
Bring the firm's engagement mix, the practice management platform, and the workflow where staff hours leak most visibly to the audit call. The call identifies whether a custom build, an existing product, or a process change addresses the constraint.
How to choose an AI consultant for regional P&C insurance agencies.
Choose an AI consultant for regional P&C (property and casualty) insurance agencies on six dimensions: vertical fit in insurance agencies, custom versus product (custom builds win above an estimated $10M to $50M commission revenue), ownership at handoff, fixed-fee pricing with a prototype before payment, time to a working system (4 to 6 weeks), and reference depth from named agency engagements.
This is not the right path for agencies with fewer than 15 producers (the agency's revenue-generating sales staff; SaaS economics win), agencies whose only need is quoting (rater tools, the software that compares carrier quotes, cover that), or agencies without a named submission or renewal constraint worth automating.
Insurance agencies: decision questions, key terms, signals, FAQ
The agency version of the three questions: (1) does the agency's submission and renewal workflow match the patterns that existing InsurTech (insurance-specific software such as EZLynx, Zywave, Indio) already automates, or does the agency carry specialty lines those products cannot represent; (2) does the agency's data posture allow a SaaS vendor to process policy and claims data under its own agreements, or do carrier contracts require infrastructure the agency controls directly; (3) over a 24-month horizon, does a compounding per-user InsurTech subscription cost less than a single fixed payment for a system the agency owns outright. If all three favor a product, the InsurTech path is stronger.
Custom versus product at agency scale. At that estimated $10M to $50M commission revenue scale, off-the-shelf products typically lose an estimated thirty to forty percent of their value to misfit on workflows that matter. See the ranked comparison of AI consultants for insurance agencies for how these six dimensions score against named vendors.
Key terms for insurance agencies
Carrier appetite matching: routing submissions to carriers whose underwriting guidelines align with the insured's profile. Policy checking: automated comparison of issued policies against quoted terms and client requests to catch errors before binding. Claims triage: using AI to classify incoming claims by severity and coverage applicability before assigning to an adjuster. Agency management system integration: connecting AI workflows with Applied Epic, EZLynx, HawkSoft, or Vertafore.
See the AI consulting cost breakdown for insurance agencies for how these terms map to fee bands.
Signals insurance agencies should look for
Vertical fit signals for insurance agencies: the consultant has shipped multiple commissions for insurance agencies in that estimated $10M to $50M commission revenue band, knows AMS360, EZLynx, Applied Epic, HawkSoft firsthand (not from a brochure), can name the typical workflow leakage points without prompting, and understands the regulatory environment.
Reference signals: named operators in regional P&C insurance agencies, named numbers measured post-handoff (12 months out, not at pilot stage), direct introductions to operators available on request.
Questions insurance agency owners ask before booking
What if the AI system does not work on the agency's data?
If the prototype fails on the agency's own AMS360, EZLynx, Applied Epic or HawkSoft data, the commission does not proceed and the agency owes nothing beyond the audit.
What is expected of the agency?
The workflow you want addressed, honest answers on the audit call, and a named contact for the prototype review.
Will this replace agency staff?
No. Commissions take routine steps off a person's desk so the team handles the judgment calls.
Related pages for insurance agencies
See the best AI consultants for insurance agencies guide for the ranked comparison of the consultants and platforms in this category.
Bring the agency's current AMS (agency management system), the submission-to-bind ratio (the share of quotes that convert to bound policies), and the renewal workflow that costs the most staff time to the audit call. The call identifies whether a custom build, an InsurTech product, or process redesign closes the gap.
How to choose an AI consultant for specialty manufacturers.
Choose an AI consultant for specialty manufacturers on six dimensions: vertical fit in manufacturers, custom versus product (our estimate: custom wins at $15M to $150M revenue), ownership at handoff, fixed-fee pricing with a prototype before payment, time to a working system (6 to 7 weeks), and reference depth from named manufacturer engagements.
This is not the right path for shops with fewer than 20 employees (SaaS economics win), shops whose only need is machine monitoring (sensor platforms cover that), or shops without a named quoting or scheduling constraint worth automating.
Manufacturers: decision questions, key terms, signals, FAQ
The manufacturer version of the three questions: (1) does the manufacturer's quoting and scheduling workflow match the patterns that existing ERP-add-on AI (Sight Machine, Tulip, Augury) already automates, or does the shop carry specialty processes those products cannot represent; (2) is there a workflow gap between the ERP (the system that runs finance, inventory and orders) and the shop floor that a generic product cannot close because it requires data from both sides; (3) over a 24-month horizon, does a compounding per-machine or per-user subscription cost less than a single fixed payment for a system the manufacturer owns outright.
Custom versus product at shop scale. At $15M to $150M revenue scale, our estimate is that off-the-shelf products typically lose thirty to forty percent of their value to misfit on workflows that matter.
Key terms for manufacturers
Traceability record: the documentation chain linking raw materials through production steps to finished goods; a compliance requirement for aerospace and defense. Quote-to-cash cycle: the elapsed time from customer inquiry to payment receipt; AI compresses this by automating estimating, routing, and invoicing. Work-order routing: assigning production jobs to machines and operators based on capacity, capability, and material availability. First-pass yield: the percentage of parts that pass quality inspection without rework; AI monitoring identifies drift before it produces scrap.
Signals manufacturers should look for
Vertical fit signals for manufacturers: the consultant has shipped at least three commissions for manufacturers in the $15M to $150M revenue band, knows Epicor Kinetic, JobBOSS, Global Shop Solutions, IQMS firsthand (not from a brochure), can name the typical workflow leakage points without prompting, and understands the regulatory environment.
Reference signals: named operators in specialty manufacturers, named numbers measured post-handoff (12 months out, not at pilot stage), direct introductions to operators available on request. The ranked comparison lists which consultants meet these signals today.
Questions manufacturers ask before choosing an AI consultant
What if a custom commission is not the right fit for our shop?
We say so on the audit call, before any invoice, and point the shop to an ERP add-on or a standard subscription instead.
How long does audit to handoff take for a manufacturer?
The audit report arrives the same day. A prototype follows in 7 to 10 days, then a 6 to 7 week build to handoff.
Does a build replace scheduling or quoting staff?
No. Builds handle quote drafting and work-order routing; a human still sits on judgment calls. We do not pitch headcount reduction.
Related pages for manufacturers
See the best AI consultants for manufacturers guide for the ranked comparison of the consultants and platforms in this category.
See which AI consultant to hire for each function in manufacturing if you already know the function that is the constraint: support, workflow automation, reporting, data analysis, compliance monitoring, technical content or implementation strategy.
See the AI consulting cost for manufacturers section for the fee-band breakdown by complexity.
Bring the ERP platform, the quoting spreadsheet or process, and the production step where schedule accuracy breaks down to the audit call. The call identifies whether a custom build, an off-the-shelf product, or an ERP configuration change fits the constraint.
How to decide whether a commission is the right next step.
Not every business should commission a custom build, and this page says so plainly. The questions below are the ones we run on the audit call to decide whether an owned system, a rented product, or no change at all is the right answer; four yes answers point to a build, fewer point elsewhere.
The four-question sequence operators run before booking.
The sequence asks four questions in a specific order. First, is the leading constraint actually addressable with AI, or is it a process problem, a staffing problem, or a stack problem that AI would not solve. Second, if AI is the right intervention, is the right buying motion a custom commission, an off-the-shelf product, or an internal hire. Third, if the right motion is a commission, is the operator comfortable running the system inside their own cloud tenant (the private software account allocated to their business alone) under an NDA and owning the code at handoff. Fourth, is the budget for a custom build from $10,000 real this quarter.
Operators who answer yes to all four book the call. Operators who answer no to any one of them either change the question (the leading constraint is different, the budget moves, the cloud posture changes) or take a different path. We do not push operators who land at a "no" on any of the four into a commission they will not be served by. The $499 AI-Ready Audit is where most operators answer the first of the four questions before ever booking the call.
The three signals operators watch for after handoff.
Twelve months post-handoff, three signals tell the operator whether the commission performed against the target written down after the audit. First, the dollar or hour delta on the workflow the commission addressed, measured against the pre-engagement baseline. Second, the percentage of the workflow the AI layer now handles autonomously versus the percentage that still routes to a human reviewer. Third, the number of times the operator's team has modified the build's prompts, models, or integration code on their own without ColabContent involvement. All three should be improving over time. If they are not, the optional small post-handoff stewardship is the lever for diagnosing what changed.
The honest comparison against the alternatives.
A commission is not the right answer for every operator. The mid-market operator with a workflow that matches a horizontal SaaS product's calibration target is better served by the product. The operator with a five-to-ten-year horizon, an AI investment runway of roughly $5M (our estimate of what that path requires), and the willingness to spend twelve months building infrastructure before shipping the first production workflow is better served by an internal hire. The operator at roughly $500M-plus revenue (our estimate of the scale where stakeholder counts justify it) with stakeholder counts that justify a Big Four engagement is better served by that motion. We will tell the operator which of those alternatives fits if a commission does not.
The honest case for a commission is narrow on purpose. Established operators with a named workflow constraint, with stack systems that the product market does not represent well, with the budget runway for the fixed fee, with the cloud posture to run the system inside their own tenant. Operators in that narrow band are where the math works.
Why we publish the comparisons, the rankings, and the boundaries.
Most consulting houses do not publish ranked comparisons against their competitors, do not publish the boundary of what they will not build, and do not publish fixed-fee pricing bands. We publish all three because the operators we want to commission for are the operators who reward that transparency with a faster booking. The current fixed-fee bands and the audit-to-handoff timeline are on the pricing page.
Frequently Asked Questions
These are the questions operators ask most often when they are choosing an AI consultant, from cost and timeline to what happens if the first build does not fit. Questions that belong to one industry, such as staff, data access and software, sit inside that industry's section above.
What does an AI consultant cost?
The audit is $499. A production build after that is one fixed fee from $10,000, quoted against integration depth. Optional care after handoff is $997 a month, cancel on 30 days notice.
What if the prototype does not perform on our data?
You owe nothing. It runs on real data in 7 to 10 days; if it misses the constraint named on the call, the business keeps the work and owes nothing further.
Start with the $499 audit.
The AI-Ready Audit is $499. The report arrives the same day, as a private link and a PDF, with a 5-minute video walkthrough and a 20-minute call. If it has no value you get the $499 back, and every quarter your AI answers, rankings and money leak are re-checked at no additional charge.
$499, money back if no value. No pitch. A written map you keep either way.
Next step
Start with the $499 audit. Bring the workflow where staff hours leak most visibly and the software it runs on; each industry section above names what to bring for that industry. The call identifies whether a custom build, an off-the-shelf product, or a process change addresses the constraint. The call is part of the audit; no obligation after it.