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2025

  • 11 min read

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

  • 7 min read

Why Most AI Projects Fail (And It's Not the Technology)

Three months into a $500K AI project, the CTO of a manufacturing company stared at a dashboard that should have been optimizing their production line. Instead, it was recommending impossible scheduling configurations that violated basic physics constraints of their equipment.

The AI model was technically brilliant. The data pipeline was flawless. The infrastructure was cloud-native and scalable.

The problem? Nobody on the AI team understood how manufacturing actually worked.

This isn't an isolated incident. According to RAND Corporation and Carnegie Mellon research, 80-90% of AI projects ultimately fail—nearly double the failure rate of traditional IT initiatives. The latest Wharton Human-AI Research report (surveying 800+ enterprise decision-makers) reveals that despite 82% of leaders now using AI weekly and 74% seeing positive ROI, persistent barriers threaten to derail the next wave of adoption.

Here's what's actually killing AI projects—and what you need to do differently.

The #1 Silent Killer: Ignoring Domain Knowledge

When I transitioned from drilling engineering to AI engineering, I carried something more valuable than any machine learning certification: 22 years of domain expertise in oil and gas operations.

This matters more than most people realize.

Research from Beyond AI shows that 42% of all organizational knowledge is unique to employees—irreplaceable expertise that exists only in the minds of experienced operators, engineers, and technicians. When AI teams ignore this, they build models that are technically sound but operationally useless.

The Real Cost of the Domain Knowledge Gap

Schneider Electric's Chief Digital Officer puts it bluntly:

"Without domain knowledge, the data scientist will not have other choice than to take all 'potentially significant' features and increase the risk of failure. Even with domain knowledge, in most cases AI practitioners will not be able to bring any kind of explainability."

The consequences are real: - A law firm used ChatGPT to draft legal briefs without proper verification, resulting in fabricated case citations and professional sanctions - A retail AI model missed seasonal demand surges because developers didn't understand merchandising cycles
- A logistics company's route optimization AI increased costs and delays because it lacked expertise in shipping constraints

According to the Wharton report, 49% of enterprise leaders cite recruiting talent with advanced Gen AI technical skills as their biggest challenge. But here's the twist: technical skills without domain knowledge create precisely these failures.

Why Domain Experts Matter More Than Data Scientists

When building AI for technical industries, you need people who can:

  1. Identify the right data sources - Not just what data exists, but what data actually matters for the business problem
  2. Define meaningful features - Understanding which variables affect outcomes based on real-world physics, regulations, or workflows
  3. Validate outputs - Recognizing when AI recommendations violate operational constraints or physical laws
  4. Ensure explainability - Translating model decisions into language that domain practitioners trust

Research from TrendMiner on industrial failure prediction shows that operational experts are critical for: - Ensuring comprehensive and accurate data collection - Providing context for failure patterns and events - Validating that predictions align with practical constraints - Bridging the gap between statistical patterns and operational reality

This is why I focus exclusively on energy and engineering companies. My RAG systems don't just parse technical documents—they understand drilling parameters, wellbore trajectories, and operational constraints because I've lived that reality for two decades.

What the Data Really Shows: The Top 5 AI Failure Patterns

According to the Wharton-GBK 2025 AI Adoption Report, here are the actual barriers enterprise leaders face:

1. Security Risks (#1 Barrier)

The top concern isn't capability—it's trust. When you lack domain experts who understand data sensitivity in your industry, you create security vulnerabilities.

2. Operational Complexity (#2 Barrier)

AI doesn't exist in a vacuum. It needs to integrate with existing workflows, comply with industry regulations, and work within operational constraints. Without domain knowledge, you build beautiful systems that nobody can actually use.

3. Inaccuracy of Results (#3 Barrier)

This is where domain knowledge becomes critical. The Wharton report shows 43% of leaders see risk of declines in skill proficiency as AI becomes more prevalent. When AI produces inaccurate results, it's often because: - Training data doesn't reflect real operational conditions - Model features ignore domain-specific variables - Validation metrics don't align with business outcomes

4. Employee Resistance and Lack of Trust (#8 Barrier)

People resist AI when they don't understand it or when it violates their domain expertise. The Wharton data shows: - 46% cite providing effective training programs as a top challenge - 43% cite maintaining employee morale in roles impacted by Gen AI - Training budgets and confidence are actually declining (-8pp investment in training, -14pp confidence in training as path to fluency)

5. Lack of Training Resources (#10 Barrier - new in 2025)

This isn't just about teaching people to use ChatGPT. It's about building a culture where domain experts and AI practitioners collaborate to create solutions that work in the real world.

The Organizations Getting It Right

The Wharton report reveals what separates winners from losers:

High performers (74% with positive ROI) share three characteristics:

  1. They measure what matters - 72% formally track ROI with metrics tied to actual business outcomes (profitability, throughput, workforce productivity)—not vanity metrics like "AI adoption rate"

  2. They start simple - They don't jump straight to autonomous systems. They begin with targeted use cases where AI augments existing expertise

  3. They invest in people, not just technology - While 30% of Gen AI budgets go to internal R&D (according to IT decision-makers), successful organizations balance this with capability building

The Gartner Framework: A Practical Starting Point

Gartner's AI Maturity Model provides a structured approach to avoiding these failures. The framework assesses readiness across seven pillars:

  1. Strategy - Clear business objectives aligned with organizational goals
  2. Product - Defined use cases with measurable value
  3. Governance - Policies for data security, ethics, and compliance
  4. Engineering - Technical capability and infrastructure
  5. Data - Quality, accessibility, and governance of data assets
  6. Operating Models - How AI integrates with existing workflows
  7. Culture - Organizational readiness and change management

Organizations at higher maturity levels (4.2-4.5 out of 5) achieve dramatically different outcomes: - 45% keep AI projects operational for 3+ years (vs. 20% for low-maturity orgs) - 63% run rigorous financial analysis on AI initiatives - 91% have dedicated AI leaders who prioritize innovation and infrastructure

Gartner's research emphasizes starting with simple, high-value use cases and scaling methodically. Their AI Use Case Insights tool evaluates opportunities based on: - Projected business value (revenue impact, cost savings, efficiency gains) - Implementation complexity (technical difficulty, data requirements, organizational change)

What to Do Before You Start Your Next AI Project

Based on the Wharton data, industry research, and my own experience building AI systems for technical industries, here's your pre-flight checklist:

1. Map Your Domain Knowledge First

Before writing a single line of code: - Identify the domain experts who understand the problem intimately - Document the operational constraints, regulatory requirements, and business rules that must be respected - Determine what success actually looks like in domain-specific terms (not just accuracy scores)

Pro tip: If your AI team doesn't include people who've actually done the job you're trying to automate or augment, stop. You're building on sand.

2. Define a Simple Baseline Use Case

Using Gartner's framework: - Choose ONE specific problem with clear business value - Ensure you have (or can obtain) the necessary data - Identify the domain knowledge required to validate results - Set measurable success criteria that matter to the business - Plan for integration with existing workflows

Start with something like "reduce time to find relevant technical specs in our documentation library" rather than "build an autonomous expert system that replaces our entire engineering team."

3. Build Your Team Around Domain Expertise

The Wharton data shows organizations are split on how to build AI capability: - 48% invest in training programs for existing employees - 46% allow employees to test and innovate - 44% hire consultants or new talent with AI skills

The best approach? Start with your domain experts and add AI capability, not the other way around. A mediocre AI engineer with deep domain knowledge will outperform a brilliant ML PhD who doesn't understand your industry.

4. Establish Success Metrics That Reflect Real Value

Following the Wharton report's findings on successful organizations: - Link AI investments to specific business KPIs (profitability, throughput, productivity) - Measure both efficiency gains (time saved, costs reduced) and effectiveness improvements (better decisions, fewer errors) - Track adoption and trust, not just technical performance - Plan for 2-3 year ROI horizons—80% of enterprise leaders expect positive returns in this timeframe

5. Invest in Change Management From Day One

The human side is not an afterthought. The Wharton data shows: - 89% agree AI enhances employee skills (+18% vs. replaces skills) - But 43% see risk of skill proficiency declines

Successful organizations: - Provide ongoing training and support (not just initial rollout) - Maintain human oversight and validation of AI outputs
- Celebrate wins that demonstrate AI augmenting expertise, not replacing it - Address morale concerns proactively (43% of leaders cite this as a challenge)

The Bottom Line

AI projects don't fail because the technology isn't ready. They fail because organizations treat AI as a technical problem rather than a business transformation that requires deep domain expertise.

The Wharton report makes this crystal clear: while adoption is accelerating (82% weekly usage, 88% planning budget increases), the organizations seeing real returns (74% positive ROI) are those who:

  1. Respect and integrate domain knowledge from the start
  2. Start with simple, well-defined use cases
  3. Measure business outcomes, not technical metrics
  4. Invest in people and processes, not just platforms
  5. Plan for 2-3 year value realization horizons

After 22 years in oil and gas and now building AI systems for technical industries, I've seen both sides. The best AI solutions don't replace expertise—they amplify it. But this only works when domain knowledge is at the center of your strategy, not an afterthought.

Want to avoid these pitfalls in your AI project?

I help energy and engineering companies turn their buried technical documentation into AI assistants that actually understand the domain. No generic chatbots—custom RAG systems built by someone who's spent decades in the trenches of technical operations.

Book a discovery call to discuss how domain-driven AI can deliver real value for your organization.

Sources & Further Reading