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The Engineer's Guide to Evaluating AI Vendors

The demo was flawless. The AI vendor's solution analyzed 10,000 technical documents in seconds, answered complex engineering questions with apparent precision, and promised seamless integration with existing systems.

Six months and $300K later, the system was still hallucinating answers, couldn't handle the company's actual document formats, and required so much manual oversight that engineers spent more time babysitting the AI than they saved using it.

The problem? Nobody asked the right technical questions during evaluation.

With 71% of companies now using generative AI regularly in at least one business function, and vendors multiplying faster than you can count, the stakes for making the right decision have never been higher.

According to Forrester research, 67% of software projects fail because of wrong build vs. buy choices. For AI projects, where 80-90% already fail for other reasons, choosing the wrong vendor accelerates your path to that statistic.

After building RAG systems for energy and engineering companies, I've seen both sides: brilliant vendors who deliver real value, and smooth-talking ones who promise the moon but deliver disappointment. Here's the framework you need to separate the two.

The 5-Pillar Vendor Evaluation Framework

Based on industry best practices and real-world deployment experience, every AI vendor evaluation should examine these five critical areas:

1. Technical Capabilities & Model Transparency

This is where most evaluations start—and where most get it wrong. The question isn't "Does it work in the demo?" It's "Will it work with our data, in our environment, at our scale?"

Critical technical questions:

  • What models are you using? (GPT-4, Claude, Llama, proprietary?) Why those specific models for this use case?
  • How do you handle domain-specific accuracy? Do you fine-tune, use RAG, or rely on prompt engineering?
  • What's your vector database and embedding strategy? How do you ensure retrieval relevance with technical jargon and specialized vocabulary?
  • Can you swap models if needed? What if GPT-5 comes out, or licensing terms change, or latency becomes an issue?
  • How do you prevent hallucinations? What guardrails, validation layers, and safety frameworks are in place?

According to High Peak Software research, knowing model provenance reveals performance characteristics, licensing costs, and update paths. If a vendor can't clearly explain their technical architecture, that's your first red flag.

From my experience building RAG systems:

When I built the RAG system for analyzing academic papers (Quiet Links), I specifically chose: - Docling for document parsing (handles complex PDFs with tables, images, formulas) - Weaviate as vector database (supports hybrid search, crucial for technical documents) - OpenAI embeddings for semantic search (best balance of accuracy and cost for English technical content)

A vendor evaluation should reveal this level of technical specificity. Generic answers like "we use the latest AI models" or "our proprietary technology" are red flags.

2. Data Security, Privacy & Governance

Research from Northwest AI shows that data protection and privacy concerns form the foundation of responsible AI governance in enterprise environments.

Essential security questions:

  • Where is our data stored? Is it encrypted at rest and in transit? What are the data residency requirements?
  • Will you use our data to train your models? This should be an unequivocal "NO" for enterprise solutions.
  • What compliance certifications do you hold? SOC 2 Type II, ISO 27001, GDPR compliance, HIPAA BAA (if applicable)?
  • How do you handle consent and data subject rights? Can we delete our data? Can we audit what you're storing?
  • What's your incident response process? How quickly will we be notified if there's a breach?

According to the Wharton 2025 AI Adoption Report, security risks rank as the #1 barrier to AI adoption among enterprise decision-makers. This isn't paranoia—it's prudence.

Governance framework questions:

  • Do you follow NIST AI Risk Management Framework? Or ISO/IEC 42001?
  • How do you ensure ethical AI use? Do you have an internal ethics board? Documented testing for bias?
  • What's your model evaluation process? How do you test for fairness across demographics, accuracy across edge cases?
  • Can you provide audit trails? For model decisions, data processing, and access logs?

FairNow AI research emphasizes that these questions create a documented record of vendor claims—invaluable for internal stakeholder alignment and regulatory compliance.

3. Integration, Scalability & Operational Requirements

A technically brilliant solution is worthless if it can't integrate with your existing systems or scale to meet your needs.

Critical integration questions:

  • What APIs do you provide? REST, GraphQL, webhooks? What's the documentation quality?
  • How does this integrate with our existing stack? Can it connect to our document management system, ERP, CRM?
  • What's the implementation timeline? Realistically, how long from contract to production?
  • What data formats do you support? PDFs, Word docs, scanned images, CAD files, proprietary formats?
  • How customizable is the solution? Can we adjust parameters, tune for our domain, configure workflows?

Scalability assessment:

  • What are your SLA commitments? Uptime, latency, error resolution times?
  • How does pricing scale? Is it per user, per document, per API call? What happens if usage spikes 10x?
  • Can the system handle our data volume? Not just today's 10,000 documents, but next year's 100,000?
  • What's your disaster recovery plan? Backup frequency, recovery time objectives?

The Wharton report shows operational complexity as the #2 barrier to AI adoption. A vendor that underestimates integration complexity is setting you up for failure.

4. Training, Support & Implementation Partnership

Fisher Phillips research emphasizes that no matter how powerful the AI system, it's only valuable if your team can use it effectively.

Support structure questions:

  • What does your implementation process look like? Do you provide dedicated implementation support?
  • What training do you offer? For technical teams? For end users? Is it included or extra cost?
  • What's your support model?
  • Tier 1: General questions, account issues
  • Tier 2: Technical issues, configuration problems
  • Tier 3: Complex problems, bugs, feature requests
  • What are your response time commitments? For critical production issues vs. general questions?
  • Do you provide clear usage guidelines? How the model should (and shouldn't) be used?

Ongoing partnership questions:

  • How often do you update the models? Will we get automatic updates or do we control versioning?
  • What's your product roadmap? Are new features aligned with our industry needs?
  • Can we influence feature development? Do you have a customer advisory board?
  • What happens if we outgrow your solution? Data portability, export options, migration support?

From the Wharton data, 46% of enterprises cite "providing effective training programs" as a top challenge, while 43% cite "maintaining employee morale" in AI-impacted roles. A vendor partner should help address these, not add to them.

5. Business Stability, ROI & Exit Strategy

Financial assessment:

  • What's the total cost of ownership? Not just subscription fees, but implementation, training, ongoing maintenance, customization?
  • How do you help measure ROI? Do you provide analytics on usage, time saved, errors prevented?
  • What metrics do successful customers track? Time to answer, accuracy rates, user satisfaction?
  • Can you provide customer references? Ideally in similar industries, at similar scale?

Stability and flexibility:

  • How long have you been in business? What's your funding situation?
  • Who are your major customers? Are they similar to us in size and industry?
  • What's your vendor lock-in situation? Can we export our data? Our customizations?
  • What are the contract terms? Termination clauses, data deletion policies, service level guarantees?

Research from Panorama Consulting emphasizes that credible AI vendors connect their technology to enterprise-relevant use cases with measurable outcomes, not just cutting-edge research.

Red Flags: When to Walk Away

Based on multiple industry sources and personal experience, here are the warning signs that should make you reconsider:

🚩 Technical Red Flags:

  1. Vague or evasive answers about their technology stack - If they can't or won't explain which models they use and why, they either don't know or are hiding something
  2. No clear hallucination prevention strategy - "We use the latest AI" isn't a strategy
  3. Proprietary "black box" solutions with zero explainability - You can't trust what you can't audit
  4. No discussion of model limitations - Every AI has limitations; vendors who don't discuss them are being dishonest
  5. Demo only works with cherry-picked examples - Ask to test with YOUR documents in the sales process

🚩 Data & Security Red Flags:

  1. Unclear data usage policies - If they can't clearly state whether your data trains their models, assume it does
  2. No compliance certifications - SOC 2, ISO 27001, etc. aren't nice-to-haves for enterprise AI
  3. Data stored in unspecified locations - "The cloud" isn't an answer for data residency
  4. No written governance framework - According to Bitsight research, this should be table stakes in 2025

🚩 Business Red Flags:

  1. Pushy sales tactics or pressure to sign quickly - Good vendors know implementation takes time; they don't rush decisions
  2. No customer references in your industry - They might be brilliant, but you'll be the guinea pig
  3. Unclear pricing or "custom quote only" - Transparency in pricing correlates with transparency elsewhere
  4. No trial or pilot program offered - Confident vendors let their product prove itself
  5. Founder/team has no domain expertise - Building AI for energy companies while never having worked in energy? Risky

🚩 Support & Partnership Red Flags:

  1. Implementation timeline seems unrealistic - If they promise production in 2 weeks, they're underestimating complexity
  2. No dedicated implementation support - You'll be left reading docs and submitting support tickets
  3. "Set it and forget it" mentality - AI systems need ongoing monitoring, tuning, and improvement
  4. Resistance to discussing limitations or customization needs - Either inflexible or overselling

The Build vs. Buy Decision Framework

Sometimes the right answer isn't choosing a vendor—it's building in-house. Here's when each makes sense.

McKinsey analysis and HP Tech research provide frameworks, but here's a practical scoring system:

Score Your Situation (1-5 scale):

1. Strategic Importance

  • 1 point: AI is a commodity tool (e.g., basic document search)
  • 5 points: AI is core competitive advantage (e.g., proprietary algorithm for your industry)

2. Data Sensitivity

  • 1 point: General business data, no compliance concerns
  • 5 points: Highly sensitive (PHI, PII, trade secrets, regulated data)

3. Customization Needs

  • 1 point: Standard use case, off-the-shelf solutions exist
  • 5 points: Highly specialized domain, unique workflows, proprietary processes

4. Internal Capability

  • 1 point: No AI/ML team, limited technical expertise
  • 5 points: Strong ML team with LLM experience, proven track record

5. Timeline Pressure

  • 1 point: Need production solution in <3 months
  • 5 points: Can invest 12-18 months in development

6. Budget Flexibility

  • 1 point: Limited budget, must minimize upfront investment
  • 5 points: Significant budget available, can invest in long-term capability

Interpretation:

  • 6-15 points: BUY - Vendor solution is likely more cost-effective and faster to value
  • 16-23 points: HYBRID - Buy core platform, build customizations and integrations
  • 24-30 points: BUILD - Custom solution justified by strategic importance, sensitivity, or unique requirements

The Hybrid Approach (Most Common)

Research from MarkTechPost shows that most Fortune 500 firms use a blended approach:

  • Buy: Vendor platforms for governance, audit trails, multi-model routing, compliance
  • Build: Custom retrieval layers, domain-specific evaluations, specialized integrations, proprietary IP

This balances scale with control over sensitive IP and satisfies board-level oversight requirements.

Build vs. Buy: Real-World Scenarios

Scenario A - Manufacturing Quality Control: - Strategic importance: Medium (3/5) - Important but not core differentiator - Data sensitivity: Low (2/5) - Production data, some competitive sensitivity - Customization: High (4/5) - Industry-specific defect patterns - Internal capability: Low (2/5) - No AI team - Timeline: Urgent (1/5) - Competitors moving fast - Budget: Moderate (2/5) - Total: 14/30 → BUY

Scenario B - Financial Services Risk Modeling: - Strategic importance: Critical (5/5) - Core competitive advantage - Data sensitivity: Extreme (5/5) - Regulated financial data, proprietary signals - Customization: Extreme (5/5) - Unique risk models, proprietary data sources - Internal capability: Strong (4/5) - Established quant team - Timeline: Flexible (4/5) - Can invest in long-term capability - Budget: Significant (5/5) - Total: 28/30 → BUILD (with vendor infrastructure for governance/compliance)

Scenario C - Technical Documentation RAG: - Strategic importance: Medium (3/5) - Improves efficiency, not core business - Data sensitivity: High (4/5) - Proprietary engineering documentation - Customization: High (4/5) - Domain-specific terminology, custom workflows - Internal capability: Moderate (3/5) - Strong engineering, limited AI expertise - Timeline: Moderate (3/5) - 6-month horizon acceptable - Budget: Limited (2/5) - Total: 19/30 → HYBRID - Use vendor RAG platform, build custom connectors and domain tuning

My Framework in Action: Real-World Vendor Evaluation

When I evaluate vendors for clients or recommend solutions, here's my actual process:

Phase 1: Requirements Definition (Week 1-2)

  1. Document the specific business problem and success metrics
  2. Identify data sources, formats, and sensitivity levels
  3. Map integration requirements with existing systems
  4. Assess internal team capabilities honestly
  5. Define budget constraints and ROI expectations

Phase 2: Vendor Research (Week 3-4)

  1. Create shortlist of 3-5 vendors based on capability, industry fit, and reputation
  2. Request technical documentation, architecture diagrams, security attestations
  3. Schedule technical deep-dive calls (not just sales demos)
  4. Collect customer references in similar industries

Phase 3: Technical Evaluation (Week 5-6)

  1. Conduct hands-on testing with real documents from your environment
  2. Measure accuracy, latency, and error rates with your data
  3. Evaluate integration complexity and API quality
  4. Review security certifications and compliance documentation
  5. Assess vendor's domain knowledge in your industry

Phase 4: Business Assessment (Week 7-8)

  1. Model total cost of ownership over 3 years
  2. Evaluate implementation timeline and resource requirements
  3. Review contract terms, SLAs, and exit clauses
  4. Check customer references and case studies
  5. Assess vendor stability and product roadmap

Phase 5: Pilot Program (Week 9-12)

  1. Negotiate a limited pilot with clear success criteria
  2. Test with subset of real users and use cases
  3. Measure against defined KPIs (time saved, accuracy, user satisfaction)
  4. Identify gaps and customization needs
  5. Make go/no-go decision based on pilot results

The Bottom Line: Choose Partners, Not Just Products

The Wharton 2025 report is clear: 74% of enterprises see positive ROI from AI, but success depends on more than just the technology.

The organizations winning with AI vendors are those who:

  1. Evaluate based on technical depth, not sales polish - Demand architecture transparency and domain expertise
  2. Prioritize data security and governance from day one - It's the #1 barrier for a reason
  3. Test with their own data during evaluation - Demos with vendor data prove nothing
  4. Plan for integration complexity - It's the #2 barrier and consistently underestimated
  5. Choose vendors who invest in your success - Training, support, and partnership matter

The decision between build and buy isn't binary. Most successful implementations use a hybrid approach: vendor platforms for foundational capabilities and compliance, custom development for domain-specific IP and competitive differentiation.

After 22 years in oil and gas and now building AI systems for engineering companies, I've learned that the best vendors feel like partners, not vendors. They understand your domain, respect your constraints, ask hard questions about your requirements, and are honest about their limitations.

They're also the ones who, when you ask technical questions, give you technical answers—not sales pitches.

Want Help Evaluating AI Vendors for Your Engineering Organization?

I help energy and engineering companies cut through the AI hype and make data-driven vendor decisions. With domain expertise in technical operations and hands-on experience building production RAG systems, I can help you:

  • Evaluate vendors using a structured technical framework
  • Conduct proof-of-concept testing with your actual documents
  • Assess build vs. buy trade-offs for your specific situation
  • Avoid expensive mistakes by spotting red flags early

No vendor relationships, no commissions—just independent technical evaluation from someone who understands both the technology and your industry.

Book a consultation to discuss your AI vendor evaluation.

Key Takeaways: Your Vendor Evaluation Checklist

✅ Technical Capabilities: - Clear explanation of models, architecture, and approach - Transparent hallucination prevention and accuracy validation - Domain-specific customization capabilities - Model flexibility and update strategy

✅ Data Security & Governance: - Explicit "we don't train on your data" commitment - SOC 2, ISO 27001, industry-specific compliance - Data residency, encryption, and access controls - Documented governance and ethics frameworks

✅ Integration & Operations: - Quality APIs and technical documentation - Realistic implementation timeline and support - Clear SLAs for uptime, latency, and error resolution - Scalability plan for data volume and user growth

✅ Training & Support: - Structured implementation program - Training for technical and business users - Tiered support with clear response times - Product roadmap aligned with your industry

✅ Business Fundamentals: - Customer references in similar industries - Transparent pricing and TCO modeling - Clear contract terms and exit clauses - Vendor stability and market position

Sources & Further Reading