How to Evaluate AI Vendor Proposals Without Getting Burned

A Tampa accounting firm recently shared three AI vendor proposals with us. All three promised "major results." One quoted $45,000 for a chatbot. Another offered the same scope for $8,000. The third proposed something entirely different for $22,000 and called it "equivalent." The firm had no way to compare them.

AI vendor proposals are hard to evaluate because the terminology is inconsistent and every vendor frames their solution differently. An AI vendor quoting a "custom conversational agent with knowledge retrieval" could mean anything from a $500 ChatGPT wrapper to a $100,000 enterprise deployment.

Start With the Problem Statement

Before you compare proposals, write down your problem in one sentence. Not "we want AI." Something specific: "Our support team spends 40 hours per week answering the same 15 questions." Or: "Quote requests take 48 hours because staff manually look up pricing for 2,000 SKUs."

This sentence becomes your filter. Every vendor should explain how their solution solves this exact problem. If a proposal spends more time describing technology than addressing your problem, the vendor is selling capability, not a solution.

Two of the three proposals that accounting firm received never mentioned the actual problem. They described features, showed architecture diagrams, and listed AI model names. The firm couldn't tell which one would actually reduce their workload.

What a Good Proposal Includes

After reviewing dozens of AI proposals for clients, we've found that strong ones cover the same ground:

A restated version of your problem, proving they understood it
A description of the solution a non-technical person can picture
What data or access they need from you, and when
A timeline with milestones, including interim checkpoints
Measurable success metrics (not "improved efficiency")
Ongoing costs after launch: hosting, API usage, maintenance
What happens if it doesn't work: refunds, revisions, exit clauses

Missing any of these should prompt a question. A vendor who can't describe success metrics probably hasn't thought about how you'll know the project worked.

Five Questions That Separate Good Vendors

Most business owners ask about price, timeline, and features. Those matter. But the questions that reveal vendor quality go deeper.

1. "What happens when the AI gets it wrong?" Every AI system makes mistakes. A vendor who says theirs won't is either lying or hasn't tested it enough. You want to hear about fallback behaviors, human escalation, and error correction.

2. "Can I see something similar you've built?" Not a polished demo. A reference from a client with a comparable problem. If they've never built anything close, you're paying for their education.

3. "What does month six look like?" Month six is where projects stall. Ask about maintenance, model updates, and performance monitoring. A system that works at launch and degrades over three months is worse than no system.

4. "What's the total cost for year one?" Not the project fee. The all-in number: API costs, hosting, monitoring, updates, support. We've seen projects where the build cost $15,000 and year-one operations cost $28,000. Our budget planning guide breaks down typical ongoing costs.

5. "Who owns the code and data?" Some vendors retain ownership. You pay for a license. Switch vendors and you start from scratch. Others deliver source code and transfer ownership. Get this in writing before signing.

Pricing Red Flags

AI pricing varies wildly because the work varies wildly. A chatbot answering five FAQ questions is a different project from one that processes documents, pulls CRM data, and books appointments. Certain patterns should make you pause.

Vague line items. "AI Development: $25,000" with no breakdown. What does that include? A credible vendor breaks work into stages with estimated hours.

No mention of API costs. Most AI solutions use cloud APIs that charge per request. At 1,000 customer interactions per day, API costs alone run $500-$2,000/month. A fixed-price quote that ignores this is hiding a surprise.

Unlimited revisions, no scope boundary. Sounds generous. Usually means the vendor hasn't defined "done." This leads to projects that drag on for months while both sides argue about what was included.

The lowball-plus-change-order pattern. A cheap initial quote, then change orders once work begins. Good vendors ask enough questions upfront to price accurately. If the discovery call lasted 15 minutes, the proposal is missing something.

Comparing Proposals Side by Side

Write the comparison down. Otherwise the most polished slide deck wins by default. Score each proposal on these categories:

Problem understanding: Did they restate your problem accurately?
Solution clarity: Can you explain what they're building to someone else?
Success metrics: Did they define measurable outcomes?
Year-one total cost: Build + operate + maintain for 12 months
Risk handling: What if it fails? Refund? Revision? Nothing?
Relevant experience: Have they built something like this before?
Post-launch support: Who maintains it after go-live, and at what cost?

Rate each 1-5 and weight by priority. A nonprofit might weight cost transparency at 3x. A medical practice might weight risk handling at 3x. The scores don't pick the winner for you, but they organize your thinking so the decision feels less overwhelming.

Check References the Right Way

Every vendor provides references they know will say positive things. The value is in what you ask: Did the project come in on budget? What went wrong, and how did the vendor handle it? How is the system performing six months later?

The most telling question: "Would you use this vendor again?" A client who says "yes, absolutely" without hesitation is a genuine endorsement. A pause or qualified answer is giving you real information.

When to Walk Away

Some proposals aren't worth negotiating:

The vendor guarantees specific outcomes ("Sales will increase 40%"). Results depend on your data, team, and customers. Nobody can guarantee that.
They pressure you on timeline ("This price expires Friday"). Good vendors don't need high-pressure tactics.
They get defensive about questions on costs or data ownership. These are standard professional questions.
The proposal is mostly marketing copy. Buzzwords aren't a plan.

Our guide on choosing an AI consultant covers broader selection criteria. This post focuses on evaluating what arrives after you've shortlisted candidates.

Get Better Proposals From the Start

Half the evaluation work happens before you receive proposals. A vague brief produces vague proposals. Give vendors specific information: the exact problem, work volume, current tools, budget range (even a rough one), and timeline expectations.

If three vendors receive identical, detailed briefs and their proposals still vary by 500%, you've learned something about the market and about each vendor's approach.

The best AI projects start with clarity about what you need and a vendor who treats your money like their own. If a first project goes well, expansion is easy. If it goes badly because the vendor was wrong for the job, recovery is expensive. Take the time to evaluate properly. The proposals tell you everything you need to know if you know where to look.