How to Evaluate FDE Front-Line Deployment Engineering Vendors: A Pre-Purchase Due Diligence Checklist

Last week I sat in on a vendor selection meeting with an IT director from a manufacturing company. Three vendors took turns at the podium, each with increasingly polished slides. Their AI agents fielded tough questions smoothly on screen. Everyone nodded along. When we wrapped up, he asked me one thing: "Which of these three has actually pushed something into production, and still has people using it today?" No one in the room could answer.

That's the reality of evaluating FDE vendors. What you need is engineers to get AI into production and have people use it. What you see in a pitch room is mostly demo. The gap between demo and production is real, and bridging it requires actual evidence of adoption, not presentation skill. This guide provides a checklist you can bring to your vendor meetings. It focuses your evaluation on real deployment and adoption data, not slide quality.

Why adoption evidence matters most in FDE vendor selection

A customer bought an AI customer service system and moved quickly on implementation. When I measured actual usage on-site, it was 4 percent. The system worked. It was designed for typical customers. But this company's workflows, data structures, and approval chains were nothing like typical.

FDE vendor evaluation has one core test: Is the vendor selling you 'the ability to get something into production' or a well-crafted pitch? In presentations, these look identical. You separate them by asking the right questions. If they answer 'our model can handle that,' they're using product language. If they can say 'this customer, this process type, adoption rate was X percent 90 days after launch, and this person maintains it,' that's production evidence. That's what matters.

Pre-purchase due diligence checklist

Six dimensions matter most when evaluating FDE vendors. These are what we're most often asked about when customers evaluate us. Each row shows the question to ask, what a strong answer looks like, and the red flags worth pushing back on.

The first two dimensions carry the most weight. Production evidence and adoption rate don't lie. One phone call to their existing customers asking 'Are you still using this?' and 'How many people touch it daily?' usually shows whether the marketing holds up.

Contract language: make 'live and in use' part of your acceptance criteria

Evaluation alone doesn't guarantee results. If your acceptance criteria just say 'system delivered,' you won't get adoption. Tie adoption rates to milestone payments instead. Acceptance shouldn't mean 'system operational'. It should mean 'after N weeks in production, target users hit the adoption threshold you agreed on.' Without that, final payment should wait.

Pin down the team in your contract. Specify the engineers, on-site hours, and knowledge transfer deliverables in your SOW. Otherwise you get senior engineers in the sales phase and junior-level engineers in delivery. Clarify IP and source code ownership as well. If you paid for it, changing one line shouldn't trigger additional licensing fees or support calls. Get these items in writing and vendors will be direct about what they can deliver.

Red flags: watch for these phrases

'We hit 95% accuracy.' That's offline test performance. Ask what the manual fallback rate is after launch.

'This demo runs on your actual data.' It's usually a clean sample they selected. Ask what happens with messy data, missing fields, or systems that don't integrate.

'Once it's live, it's straightforward, hand it off to your team.' That's where adoption fails. Vendors who have done this talk about ongoing support after launch, not about disappearing after you sign.

We handle this by running implementation through launch, measuring adoption, and handing operation to your team. The standard is simple: live systems with active users. Pretty demos don't count. When you ask any FDE vendor about live systems with people using them daily, that answer applies to all of them.