The vendor demo was impressive. But will the platform work with your data, your infrastructure, your compliance requirements? Most companies find out after signing. The Vendor Trap costs European companies €millions every year in wrong AI platform choices, abandoned implementations, and expensive switchovers. The vendor shows you a polished demo on clean data. Your data has 14 sources, 3 formats, and quality issues nobody wants to discuss. The vendor says 'integration is straightforward.' Your CISO says 'we need a 6-month security review.' I have zero vendor partnerships. €0 in kickbacks. I've evaluated 50+ AI platforms across every major stack. My job is to protect you from the Vendor Trap — not to sell you into one.
Stage 1: The impressive demo. Clean data, prepared scenarios, best-case results. You're sold on the vision. The Vendor Trap starts with a demo that doesn't represent your reality.
Stage 2: The optimistic timeline. 'We'll have you live in 12 weeks.' They mean 12 weeks to basic setup. Integration with your systems? Data migration? Security review? That's extra. And extra time. And extra cost.
Stage 3: The contract lock-in. Multi-year commitment. Data locked in proprietary formats. Exit costs that make switching impossible. The Vendor Trap gets expensive when you want to leave.
Stage 4: The customization spiral. 'Our platform does 80% of what you need out of the box.' The other 20% takes 80% of the budget. You're building custom solutions on top of a platform you chose to avoid custom solutions.
Stage 5: The quiet failure. 18 months in, adoption is at 15%. The platform technically works. Nobody uses it. The Vendor Trap ends not with a bang but with a budget line nobody wants to explain to the board.
A 3-6 week engagement that evaluates AI vendors objectively — before you sign. No vendor bias. No kickbacks. Just clear-eyed analysis of what works for your specific requirements.
Document your actual requirements — not the vendor's feature list. Use cases, data sources, integration points, compliance needs, scalability requirements. What do you actually need?
Map the relevant vendors — established platforms, emerging alternatives, and open-source options. Include options the vendor you're already talking to hopes you don't consider.
Evaluate finalists against your requirements with weighted scoring. Test with your data, not demo data. Assess integration with your infrastructure, not a clean environment.
Review contracts for lock-in, exit costs, data portability, SLA terms, and hidden fees. Negotiate from a position of knowledge. Choose with confidence.
Developed from evaluating 50+ AI platforms and advising companies on vendor selection. PROCURE protects you from the Vendor Trap by ensuring every evaluation criterion is defined before a single demo is watched.
You're about to make a significant AI platform investment (€500K+). You've seen impressive demos but want an independent evaluation before signing. You don't trust the vendor to tell you their weaknesses. You want someone who has no financial relationship with any AI vendor.
Standard RFPs work for commodity purchases. AI platforms aren't commodities. The RFP asks vendors to self-assess against your requirements — and vendors are excellent at making their weaknesses look like strengths. This service adds independent evaluation: testing with your data, integration assessment with your infrastructure, and contract risk analysis. The RFP tells you what vendors claim. This tells you what's actually true.
Analyst reports evaluate vendors generically. This evaluates vendors against your specific requirements, your data, your infrastructure, and your compliance needs. Gartner can tell you who the 'Leaders' are. I can tell you which leader actually works with your 14 data sources, your GDPR requirements, and your IT team's capabilities. Generic recommendations produce generic outcomes.
Always included in the evaluation. Open source often wins on flexibility, cost, and data portability — but loses on support, governance, and time-to-production. The build vs. buy analysis weighs total cost of ownership, not just license fees. Sometimes the right answer is an open-source foundation with commercial support. Sometimes it's a managed platform. The evaluation tells you which.
With a TCO model that accounts for the real costs of both. Building means hiring ML engineers (€120K-€180K each), maintaining infrastructure, handling model lifecycle. Buying means license fees, integration costs, vendor dependency, and customization limits. I model both scenarios over 3-5 years. The answer depends on your team, your use case, and your strategic priorities — not on a vendor's pitch.
Then the evaluation either confirms your choice with evidence or reveals risks you haven't considered. Either outcome is valuable. I've seen preferred vendors survive independent evaluation and become stronger choices. I've also seen preferred vendors fail on data compatibility, integration complexity, or contractual terms that the sales process conveniently overlooked. Better to know before signing.
Let's discuss how this service can address your specific challenges and drive real results.