Module 5: AI Vendor Selection

Learning Content

Module Overview

Most Malta businesses lack the resources to build all AI capabilities in-house. Selecting the right AI vendor or platform partner is often the difference between success and failure. But with thousands of AI vendors claiming revolutionary capabilities, how do you separate genuine solutions from hype?

This module provides a systematic framework for evaluating and selecting AI vendors, platforms, and consultants. You'll learn what questions to ask, what capabilities matter, how to run proof-of-concepts, and how to structure partnerships that protect your interests.

🔑 Key Concept: Vendor Capabilities vs. Marketing

Many AI vendors have impressive demos that don't translate to your real-world data and use cases. Always insist on proof-of-concept testing with YOUR data before commitment. A vendor's performance on benchmark datasets means little for your specific problem.

Types of AI Vendors

1. AI Platforms (End-to-End Solutions)

What They Provide: Complete AI infrastructure from data preparation through model deployment and monitoring.

Examples: MAIA (neurosymbolic AI), AWS SageMaker, Google Vertex AI, Azure Machine Learning, DataRobot

Best For: Organizations wanting comprehensive solutions without building from scratch. Malta SMEs with limited AI talent.

Typical Cost: €10K-€150K annually depending on usage and features

2. Point Solution Vendors (Specific Use Cases)

What They Provide: Pre-built AI for specific problems (e.g., fraud detection, chatbots, document processing).

Examples: Intercom (chatbots), Riskified (e-commerce fraud), Tesseract (OCR), Tableau (analytics)

Best For: Businesses needing proven solutions for standard use cases quickly.

Typical Cost: €5K-€50K annually per solution

3. AI Consultancies

What They Provide: Custom AI development, strategy, and implementation services.

Examples: Accenture, Deloitte, PwC AI practices, boutique ML consultancies

Best For: Organizations with unique requirements or needing strategic guidance.

Typical Cost: €50K-€500K+ per project depending on scope

4. Open Source + Cloud Infrastructure

What They Provide: DIY approach using open source tools (TensorFlow, PyTorch, scikit-learn) on cloud infrastructure.

Best For: Organizations with strong in-house ML teams wanting maximum control and customization.

Typical Cost: €20K-€100K annually in infrastructure + in-house team costs

The Vendor Evaluation Framework

Dimension 1: Technical Capabilities

Supported ML Techniques: Does vendor support the ML approaches your use case requires? (supervised learning, unsupervised, deep learning, NLP, computer vision, recommender systems, etc.)
Model Explainability: Can you understand WHY the model made a decision? Critical for regulated industries (MGA, MFSA). Neurosymbolic AI (like MAIA) provides superior explainability vs. black-box neural networks.
Real-Time vs. Batch: Does solution support real-time predictions (millisecond latency) if needed? Or only batch processing?
Scalability: Can solution handle your data volumes and prediction volumes? (10K transactions/day vs. 10M/day requires very different infrastructure)
Data Requirements: How much data does vendor need for success? Some approaches (neurosymbolic, transfer learning) work with less data than pure neural networks.
Integration Options: APIs, SDKs, pre-built connectors to your existing systems (Salesforce, SAP, custom databases)?

Dimension 2: Regulatory & Compliance Fit

GDPR Compliance: Is vendor GDPR-compliant? Where is data processed and stored? (EU data residency requirements)
Industry Certifications: Does vendor have relevant certifications? (ISO 27001 security, SOC 2, PCI DSS for payments)
Explainability for Compliance: For MGA (iGaming) or MFSA (FinTech), can AI decisions be explained to regulators? Black-box models are risky.
Audit Trails: Does platform provide complete audit logs of AI decisions for regulatory review?
Data Ownership: Do YOU own your data and models? Or does vendor claim rights? (Critical clause in contracts)

Dimension 3: Ease of Use & Support

Technical Expertise Required: Can your existing team use the platform? Or need dedicated ML engineers?
Documentation Quality: Comprehensive docs, tutorials, examples? Or minimal documentation requiring extensive vendor support?
Support & Onboarding: What level of support? (Email only vs. dedicated customer success manager)
Training Provided: Does vendor train your team? Or you're on your own?
Community & Resources: Active user community, Stack Overflow presence, YouTube tutorials?

Dimension 4: Vendor Viability & Risk

Financial Stability: Is vendor financially healthy? Funded? Profitable? Risk of shutdown? (Check Crunchbase, LinkedIn employee count trends)
Customer Base: How many customers? Any in Malta or similar markets? Reference customers in your industry?
Roadmap & Innovation: Is vendor actively improving product? Or stagnant?
Lock-In Risk: How hard is it to switch vendors if needed? Can you export models and data easily? Or proprietary lock-in?
Geographic Presence: Does vendor have European presence for support? Or US-only requiring middle-of-night calls?

Dimension 5: Cost Structure & ROI

Pricing Model: Subscription, usage-based, per-transaction, one-time license? (Understand total cost, not just sticker price)
Hidden Costs: Implementation fees, training costs, infrastructure costs not included in platform fee?
Scalability Costs: How do costs scale as usage grows? Linear? Or exponential pricing cliffs?
Time to Value: How long until solution is live and delivering business value? (Faster = better ROI)
Exit Costs: What happens if you cancel? Data migration costs? Contractual penalties?

The Proof-of-Concept (POC) Process

Step 1: Define POC Scope (Week 1)

Select ONE specific use case to test (don't try to validate everything at once)
Define clear success criteria (e.g., "achieve 85%+ accuracy on our fraud detection test set")
Prepare sample dataset representative of your real data (1,000-10,000 examples)
Set timeline (typically 2-4 weeks for POC)

Step 2: Run Parallel POCs (Weeks 2-4)

Test 2-3 vendors simultaneously with SAME dataset and success criteria
Vendors should demonstrate on YOUR data, not their curated benchmark datasets
Evaluate both technical performance (accuracy) and practical usability (ease of implementation)

Step 3: Evaluate Results (Week 5)

Compare vendors on performance, ease of use, support quality, and cost
Check for overfitting: Did vendor tune excessively to your POC data? (Test on holdout data they haven't seen)
Assess integration complexity: How hard to integrate into your production systems?

Step 4: Negotiate & Contract (Weeks 6-8)

Use POC results to negotiate pricing and terms
Ensure contract includes: data ownership, exit rights, SLA guarantees, GDPR compliance, IP ownership
Start small: 6-12 month initial contract with option to expand (avoid multi-year lock-in upfront)

Key Contract Clauses to Negotiate

Data Ownership: "Customer retains all rights, title, and interest in Customer Data. Vendor has no rights to use Customer Data beyond providing contracted services."
Model Ownership: "Custom models trained on Customer Data are owned by Customer. Vendor grants perpetual license to use models."
GDPR Compliance: "Vendor is Data Processor under GDPR. Vendor will sign Data Processing Agreement (DPA) and maintain EU data residency."
SLA Guarantees: "Platform uptime: 99.5% monthly. If breached, Customer receives [X]% service credits."
Exit Rights: "Customer may export all data and models in standard formats (JSON, CSV, PMML, ONNX) within 30 days of termination."
Pricing Protection: "Annual price increases capped at [5-10%] or inflation index, whichever is lower."

Malta Case Study: iGaming Operator's Vendor Selection

Context: Malta iGaming operator, 1.5M players, wanted AI churn prediction. Evaluated 4 vendor options.

Vendor Option A: Global Enterprise AI Platform

Strengths: Comprehensive features, proven at scale, strong brand
Weaknesses: Complex (requires dedicated ML engineers), expensive (€120K/year), 6-month implementation, US-based support (time zone challenges)
POC Result: 87% churn prediction accuracy, but integration complexity high

Vendor Option B: MAIA Neurosymbolic AI Platform

Strengths: Explainable AI (MGA compliance advantage), efficient (works with less data), Malta-friendly time zone, €45K/year
Weaknesses: Smaller company (viability concern), fewer features than enterprise platforms
POC Result: 85% churn prediction accuracy, PLUS full explanation of why each player flagged (neurosymbolic reasoning). Integration moderate complexity.

Vendor Option C: Custom AI Consultancy

Strengths: Fully customized solution, deep expertise
Weaknesses: €180K project cost, 9-month timeline, knowledge leaves with consultants, ongoing maintenance dependency
POC Result: Declined to do POC without €25K upfront payment

Vendor Option D: Open Source DIY

Strengths: Full control, no vendor lock-in, lower platform costs (only infrastructure)
Weaknesses: Requires hiring 2-3 ML engineers (€180K/year personnel cost), 12+ month build time, operational overhead (MLOps, monitoring, maintenance)
POC Result: Not feasible given lack of in-house ML talent

Decision: Selected MAIA (Option B)

Key Decision Factors:

Explainability: Neurosymbolic AI provided transparency needed for MGA compliance (could explain to regulators WHY player flagged for churn risk)
Cost-Performance Balance: 85% accuracy acceptable (vs. 87% from Option A), at 1/3 the cost (€45K vs. €120K)
Speed to Value: 6-week implementation vs. 6 months (Option A) or 9 months (Option C)
Malta-Friendly: European time zone support, understanding of Malta iGaming market
Data Efficiency: Worked well with 18 months of player data (some options wanted 3+ years)

Contract Negotiation Wins:

Negotiated 6-month pilot period (€22.5K) before committing to annual contract
Secured data ownership and model export rights in contract
Added performance guarantee: If accuracy below 80% in production, partial refund
Included 20 hours of training for internal team in contract

Results After 12 Months:

Churn prediction live, 86% accuracy maintained in production (exceeded POC results)
20% churn reduction through targeted retention campaigns = €1.6M additional revenue
MGA audit accepted AI approach due to explainability (critical regulatory win)
ROI: €1.6M value ÷ €45K cost = 3,456% ROI 🎉
Expanded to 3-year contract, added fraud detection module (Year 2)

Red Flags: When to Walk Away from a Vendor

🚩 Refuses POC on Your Data: If vendor won't demonstrate on your real data before purchase, they're hiding something.
🚩 No Reference Customers: Can't provide 2-3 reference customers in similar industry/use case? High risk.
🚩 Vague on GDPR Compliance: Can't clearly articulate data residency, DPA, or GDPR controls? Don't risk it.
🚩 Pressure Tactics: "This discount expires today!" or "Sign now or lose access!" = unethical sales, poor partnership mindset.
🚩 Claims 100% Accuracy: AI is probabilistic. Anyone claiming perfection is lying or inexperienced.
🚩 Proprietary Lock-In: Can't export your data or models? You'll be hostage forever.
🚩 Unclear Pricing: Can't provide clear cost estimate for your expected usage? Hidden fees ahead.
🚩 No Support SLAs: Won't commit to response times or uptime guarantees in contract? You'll be deprioritized.

Vendor Scorecard Template

Evaluation Criteria	Weight	Vendor A	Vendor B	Vendor C
Technical Capabilities	30%	__/10	__/10	__/10
POC Performance (Your Data)	25%	__/10	__/10	__/10
Regulatory & Compliance Fit	20%	__/10	__/10	__/10
Cost & ROI	15%	__/10	__/10	__/10
Vendor Viability & Support	10%	__/10	__/10	__/10
TOTAL SCORE		__/10	__/10	__/10

Key Takeaways

Always run proof-of-concept tests with YOUR real data before committing—vendor demos on curated datasets prove nothing
Evaluate vendors across 5 dimensions: Technical Capabilities, Regulatory Compliance, Ease of Use, Vendor Viability, and Cost/ROI
For Malta businesses: GDPR compliance is non-negotiable. Insist on EU data residency and Data Processing Agreements
For MGA-regulated iGaming or MFSA-regulated FinTech: Explainable AI (like MAIA's neurosymbolic approach) provides regulatory advantage over black-box neural networks
Negotiate key contract clauses: data ownership, model ownership, exit rights, SLA guarantees, pricing caps
Test 2-3 vendors in parallel to compare objectively and create negotiating leverage
Red flags: refuses POC, no reference customers, vague on GDPR, pressure tactics, claims 100% accuracy, proprietary lock-in
Start with 6-12 month pilot contracts before multi-year commitments—prove value first, then scale
Balance cost and capability: sometimes 85% accuracy at €45K/year beats 87% accuracy at €120K/year (ROI matters more than perfection)