How to evaluate AI software for a 3PL, a buyer checklist

The best way to evaluate AI software for a 3PL is to stop watching demos and test it on your own work: your past RFQs, your rate card, your systems, your approval rules. This checklist covers what to check, what to ask and how to score vendors side by side, so you can compare tools on evidence instead of on the slickest presentation.

What should a 3PL check before buying AI software?

Nine areas decide whether a tool will earn its cost: fit to your workflow, data and integrations, accuracy on your own data, human-in-the-loop controls, security and privacy, pricing and total cost, pilot design, vendor viability, and how you get out if it does not work. Work through the checklist below with each shortlisted vendor, then score them with the template further down.

One general rule first: ask vendors to back up their claims. The US Federal Trade Commission has brought enforcement actions over AI marketing claims, including a case where it alleged a company advertised AI capabilities it had no evidence for, as part of its Operation AI Comply announced in September 2024. You do not need to be a lawyer to apply the same standard: if a vendor quotes an accuracy figure or a time saving, ask how it was measured and on whose data.

1. Fit to your workflow

• Which of your workflows does it cover: quotes and RFQs, operations (job setup, labor planning, dispatch), billing (invoice lines, accessorials), or several?
• Does it handle your actual services, such as devanning, transloading, storage, cross-dock, pick and pack, drayage, rather than a generic “logistics” template?
• Can it work from your rate card structure (per container, per pallet, per hour, minimums, accessorials) without you redesigning it?
• Does the work flow from quote to job to invoice on one record, or will someone re-key it between tools?
• Who on your team will use it daily, and did they see the demo?

2. Data and integrations

• Email: does it read the shared inbox where RFQs actually arrive (Google Workspace, Microsoft 365), including attachments, PDFs and forwarded threads?
• WMS/TMS: is there a working integration with your system today, or is it “on the roadmap”? Ask to see it live.
• Accounting: does it connect to QuickBooks or your ERP, and does it write draft invoices or only export a file?
• CRM: does it read and update your customer records, or create a second customer list to maintain?
• Which direction does data flow for each integration (read only, write, two-way), and who maintains the connection when an API changes?

3. Accuracy and evaluation

• Will the vendor run a blind test on a set of your own past RFQs (see the suggested method below)?
• Extraction accuracy: what share of key fields (container size, load type, pallet count, dates, locations, special handling) did it read correctly?
• Pricing correctness: how many drafts matched the price you would have sent from your rate card, and how far off were the misses?
• Does it flag what is missing or uncertain, or does it fill gaps with guesses?
• Time saved: how long does review plus edit take compared with quoting by hand today?

4. Human-in-the-loop controls

• Can you require a person to approve every quote before it is sent? Is that the default?
• Can you set triggers (value, margin floor, new customer, unusual service, missing data) that route a quote to a more senior approver?
• Does the review screen show the source request, extracted fields and rate lines next to the draft?
• Does it keep an audit trail: original draft, sent version, who approved, timestamps?
• Can you switch automation off quickly for a customer, a service or the whole account?

5. Security and privacy

• Is there a current SOC 2 report, and is it Type 1 or Type 2? Will they share it under NDA?
• Which AI model providers and other subprocessors handle your data, and where is it stored?
• Is your data (emails, rates, customer names) used to train models, theirs or a third party’s? Is that off by default, and is it in the contract?
• How long is data retained, and can you have it deleted on request and at contract end?
• Access controls: single sign-on, multi-factor authentication, role-based permissions (who can see rates, who can approve), and access logs.

6. Pricing model and total cost

• Is pricing published? Per seat, per agent, per transaction, per document, or usage-based?
• What happens to cost if your RFQ volume doubles in peak season?
• Are setup, onboarding, integrations and support included or extra?
• Contract length, notice period, auto-renewal and price increase terms.
• Internal cost: hours your team will spend on setup, rate card cleanup and review in the first three months.

7. Pilot design

• Scope: one workflow, a defined set of customers or services, one inbox.
• Success criteria written down before the pilot starts, with a baseline measured first.
• Timeline with a fixed end date and a go/no-go meeting on the calendar.
• Named owners on both sides, and a rule that every quote is approved by your team during the pilot.

8. Vendor viability

• How long has the product been in use by logistics companies, and can you speak to a current customer of similar size?
• Who answers support, in what hours, and with what response commitment?
• Does the team understand warehouse and freight work, or will you be teaching them?
• What is on the roadmap, and which of the features you need exist today?

9. Exit and data portability

• Can you export quotes, customers, rate cards and audit logs in a standard format (CSV, JSON) at any time?
• What happens to your data when the contract ends, and how fast is it deleted?
• Is there anything you would have to rebuild by hand to switch away?

How do you test AI accuracy on your own RFQs?

Vendor demos use clean examples. Your inbox does not. The most useful thing you can do in an evaluation is a small blind test on your own history. This is a suggested method, not a formal standard; adjust it to your volumes.

1. Pick 20 to 50 past RFQs that you have already priced, with a realistic mix: routine requests, messy forwarded threads, scanned PDFs, a few new customers and at least a couple of unusual services.
2. Remove anything you cannot share, or test under an NDA and data processing terms you have reviewed.
3. Record the answer key: the fields a person extracted and the price you actually quoted (or should have, if it was later corrected).
4. Give the vendor the requests and your rate card, but not your answers.
5. Score each draft on three measures, using the formulas below.
6. Read the misses. A tool that is wrong on hazmat or missing pallet counts is a different risk from one that formats addresses oddly.

Extraction accuracy = key fields read correctly ÷ key fields in the test set × 100

Pricing correctness = drafts within your tolerance of the correct price ÷ drafts priced × 100

Time saved per quote = median manual quoting time − median review-and-edit time

Decide your pricing tolerance before you see results (for example, exact match on every rate-card line). Measure manual quoting time on real requests for a week before the pilot; the quote response time calculator helps. For what “reading an RFQ” involves and where it tends to fail, see how AI reads freight RFQs.

Testing against your own documented data also matches the approach in the NIST AI Risk Management Framework, a voluntary framework whose core document asks that test sets, metrics and tools used in evaluation be documented (MEASURE 2.1).

What should you ask about security and privacy?

You do not need to audit a vendor yourself. You need to ask the right questions and read the answers.

• SOC 2. SOC reports are assurance reports issued by CPA firms under the AICPA’s System and Organization Controls framework; SOC 2 covers controls relevant to security, availability, processing integrity, confidentiality or privacy. A Type 1 report looks at control design at a point in time; a Type 2 report also tests whether controls operated effectively over a period, as audit firm KirkpatrickPrice explains. Ask for the report itself, check the date, check which categories are in scope, and read the exceptions section. A badge on a website is not the report.
• Training on your data. Ask in writing whether your emails, customer names and rates are used to train or improve any model, and whether that applies to the vendor’s AI providers too. Get the answer into the contract.
• Retention and deletion. How long are emails and attachments kept? Can you delete a customer’s data on request?
• Access. Who at the vendor can see your data, under what circumstances, and is that access logged?

How do you score AI vendors side by side?

Use a weighted scorecard so the decision reflects what matters to your operation, not who gave the best demo. Set the weights before you meet vendors. The weights below are an example for a quote-heavy 3PL; change them to suit you.

Weighted total = sum of (weight × score) for each criterion; maximum 5.0

Score 1 for “does not meet the need”, 3 for “meets it with workarounds”, 5 for “meets it today, shown on our data”. Anything that was only promised, not shown, scores no higher than 2. Treat a very low score on security or human controls as a veto regardless of the total.

What questions should you ask in a demo?

1. Can you run this on one of our real RFQs right now, not a prepared example?
2. Show me what happens when the request is missing the pallet count or load type.
3. Show me a scanned PDF and a forwarded email thread being read.
4. Where does the price come from? Show me the rate card lines behind each quote line.
5. Can anything be sent to a customer without a person approving it? How do I check?
6. Show me the audit trail for one quote: draft, edits, approver, timestamps.
7. Which of our systems (email, WMS/TMS, QuickBooks/ERP, CRM) connect today, live?
8. Is our data used to train any model, yours or a provider's? Where is that in the contract?
9. Can we see your current SOC 2 report? Type 1 or Type 2? Which categories?
10. How long is our data kept, and how do we export and delete it if we leave?
11. What will this cost at double our current volume, and what is not included?
12. If you quoted an accuracy or time-saving figure, how was it measured and on whose data?
13. Can we speak to a current customer who runs a similar operation?
14. What would a pilot look like: scope, success criteria, timeline, cost?

How should you structure a pilot?

A good pilot is small, measured and ends on a date. Agree these in writing before it starts:

• Scope: one workflow (usually inbound quote requests), one inbox, a defined set of customers or services.
• Baseline: your current quote response time, quoting hours per week and error rate, measured before the tool goes live.
• Success criteria: targets for extraction accuracy, pricing correctness and time saved, plus a rule that every quote is approved by your team. The human approval guide has a starting approval policy you can adopt.
• Timeline: a fixed period long enough to see normal volume and at least a few unusual requests, with a go/no-go meeting booked at the end.
• Exit: what happens to your data if you do not continue.

Before you start, look at published pricing and at how vendors describe the tools they replace; our pricing and comparison pages show how we lay out ours. Hold every vendor, including us, to the same checklist.

If you are evaluating Rapid Ai 10x, run this exact evaluation on it. We will set up a guided pilot on your own accounts: test Adriana on your past RFQs and rate card, score extraction, pricing and time saved against your baseline, and review the approval flow, where every quote waits for a person on your team before it is sent.