Evaluating AI development companies requires assessing five core dimensions: technical AI depth (not just general software skills), production track record (deployed products, not demos), full-cycle team structure (PM, design, dev, QA with AI-specific expertise), communication maturity, and cost transparency including ongoing inference expenses. Companies that score well across all five dimensions deliver AI products 40-60% more reliably than those selected primarily on cost or timeline promises. Use a weighted scorecard approach, allocating 30% to technical depth, 25% to production track record, 20% to team structure, 15% to process maturity, and 10% to pricing clarity.
Introduction
Selecting an AI development company is fundamentally different from hiring a traditional software vendor. AI projects carry unique risks: model performance is probabilistic, inference costs scale unpredictably, and user expectations for AI features differ from deterministic software. Yet 68% of companies evaluating AI vendors use the same procurement criteria they apply to standard software projects.
The result: 54% of AI projects fail to reach production, according to industry analysis from 2025. The primary cause is not technical failure but misaligned vendor selection. Companies choose partners optimized for speed or cost rather than AI-specific competence.
This framework provides a structured, repeatable approach to evaluating AI development companies. It addresses the unique dimensions of AI project delivery that traditional vendor assessments miss entirely.
Why Traditional Vendor Evaluation Fails for AI Projects
Software Development Is Not AI Development
Traditional software development produces deterministic outputs: given the same input, the system produces the same output every time. AI development produces probabilistic outputs that vary based on model behavior, prompt design, and training data. This fundamental difference invalidates several standard evaluation criteria.
A company with 500 completed software projects and zero AI deployments is not qualified for your AI product. Conversely, a smaller team with 20 production AI deployments understands challenges that larger, non-AI-focused shops have never encountered.
Demo Culture Distorts Reality
AI demos are uniquely misleading. A language model responding perfectly in a controlled presentation may hallucinate, produce biased outputs, or fail entirely with real-world inputs. Traditional software demos show actual functionality. AI demos show best-case behavior in controlled conditions.
Evaluators must look beyond demos to production metrics: error rates, fallback trigger frequency, user satisfaction scores, and cost-per-interaction data from live deployments.
Hidden Cost Structures
Traditional software costs are front-loaded in development. AI products carry significant ongoing costs: API inference charges, model hosting, monitoring, and continuous prompt optimization. A vendor quoting $100,000 for development who builds a product consuming $15,000/month in API calls has created a financial liability, not an asset.
The Five-Dimension Evaluation Framework
This framework assigns weighted scores across five dimensions. Each dimension includes specific evaluation questions, scoring criteria, and red flags.
Dimension | Weight | What It Measures | Key Question |
|---|---|---|---|
Technical AI Depth | 30% | AI-specific knowledge and capabilities | Can they explain trade-offs between AI approaches? |
Production Track Record | 25% | Real-world deployment experience | Can they show live, deployed AI products? |
Team Structure | 20% | Full-cycle AI expertise across roles | Do non-developers have AI-specific skills? |
Process Maturity | 15% | AI-adapted development methodology | Do they include discovery and AI testing phases? |
Cost Transparency | 10% | Honest, complete pricing including ongoing costs | Do they estimate inference costs at scale? |
Dimension 1: Technical AI Depth
Weight: 30%
What to Evaluate
Technical AI depth goes beyond knowing which API to call. It includes understanding when to use LLMs versus traditional ML, how to architect systems for probabilistic outputs, and how to optimize inference costs without sacrificing quality.
Evaluation Questions
Model Selection: Ask the company to explain when they would use a large language model versus a smaller, fine-tuned model versus a traditional algorithm. Strong answers include specific trade-offs around cost, latency, accuracy, and data privacy. Weak answers default to "we use GPT-4 for everything."
Architecture Knowledge: Request a high-level architecture diagram for a RAG (Retrieval-Augmented Generation) system. Evaluate whether they address chunking strategies, embedding model selection, vector database choice, and retrieval optimization. Companies without RAG experience will provide generic diagrams missing these details.
Prompt Engineering: Ask about their approach to prompt engineering and testing. Competent teams maintain prompt libraries, version-control prompts, and test outputs systematically across scenarios. Teams that "just write prompts" lack the rigor needed for production AI.
Cost Optimization: Ask how they reduce inference costs without degrading output quality. Strong answers include prompt caching, model routing (using cheaper models for simple queries), output length optimization, and batch processing strategies.
Scoring Guide
Score | Criteria |
|---|---|
5 - Excellent | Explains trade-offs clearly, demonstrates multiple AI architecture patterns, shows prompt testing frameworks |
4 - Strong | Solid understanding of AI architectures, can discuss alternatives, has production optimization experience |
3 - Adequate | Knows major AI providers and basic integration patterns, limited optimization experience |
2 - Weak | Limited to single-provider experience, no systematic prompt engineering, vague on architecture |
1 - Insufficient | Cannot explain AI architecture decisions, no production AI experience |
Dimension 2: Production Track Record
Weight: 25%
What to Evaluate
Production AI experience reveals challenges invisible in demos: handling API outages, managing model version changes, optimizing costs at scale, and maintaining output quality over time. Companies without production deployments lack this critical knowledge.
Evaluation Questions
Live Products: Request URLs or app store links to AI products they built and currently maintain. Check user reviews specifically mentioning AI features. Products with 4+ star ratings and positive AI-specific feedback demonstrate production competence.
Failure Handling: Ask what happens when their AI features fail in production. Strong answers describe specific fallback mechanisms, error recovery procedures, and user communication strategies. Vague answers ("it doesn't fail") indicate inexperience.
Scale Experience: Ask about the largest user base they have supported with AI features. Understand their experience with inference cost management at scale. A product serving 100 users has different challenges than one serving 100,000.
Maintenance: Ask how they handle model updates, prompt drift, and quality degradation over time. Production AI requires ongoing monitoring and optimization that traditional software does not.
Scoring Guide
Score | Criteria |
|---|---|
5 - Excellent | Multiple live AI products, strong user ratings, documented failure handling, scale experience |
4 - Strong | 2-3 live AI products, good user feedback, clear maintenance processes |
3 - Adequate | 1-2 live AI products, basic monitoring, limited scale experience |
2 - Weak | AI projects completed but not in active production, no metrics available |
1 - Insufficient | No production AI deployments, only demos or prototypes |
Dimension 3: Team Structure and AI Expertise
Weight: 20%
What to Evaluate
AI products require AI-specific expertise across all roles, not just development. Product managers must understand AI feasibility. Designers must handle probabilistic outputs. QA specialists need AI-specific testing methodologies.
Role-Specific Questions
Product Managers: How do you decide whether a feature should use AI or a traditional approach? Strong PMs describe feasibility assessments, user research around AI expectations, and frameworks for evaluating AI ROI.
Designers: How do you design interfaces for features where outputs are not 100% deterministic? Strong designers discuss confidence indicators, regeneration options, feedback mechanisms, and progressive disclosure of AI capabilities.
Developers: How do you handle AI API failures in production? Strong developers describe circuit breakers, fallback mechanisms, retry strategies, and graceful degradation patterns.
QA Specialists: How do you test AI features? Strong QA professionals describe systematic prompt testing across scenarios, bias detection methodologies, performance benchmarking, and regression testing for prompt changes.
Scoring Guide
Score | Criteria |
|---|---|
5 - Excellent | All four roles demonstrate AI-specific expertise with concrete examples |
4 - Strong | Three roles show AI expertise, fourth role shows awareness |
3 - Adequate | Developers show AI expertise, other roles have general knowledge |
2 - Weak | Only developers involved, no PM/design/QA with AI experience |
1 - Insufficient | No AI-specific expertise demonstrated in any role |
Dimension 4: Process Maturity
Weight: 15%
What to Evaluate
AI development requires adapted processes including discovery phases, iterative prompt engineering, AI-specific testing, and production monitoring. Companies applying standard software development processes to AI projects miss critical steps.
Key Process Elements
Discovery Phase: Companies that start with a 1-2 week discovery phase validate AI feasibility before committing to full development. This includes testing APIs with representative data, measuring response times, estimating costs, and identifying technical risks. Projects without discovery phases encounter 40% more mid-project pivots.
Sprint Structure: AI sprints should include time for prompt engineering iteration, model comparison, and output quality assessment alongside standard development tasks. Sprint demos should show actual AI outputs, not mockups.
Testing Methodology: AI testing requires scenario-based evaluation across hundreds of input variations, bias testing, performance benchmarking, and regression testing when prompts change. Standard unit and integration tests are insufficient.
Monitoring and Feedback: Production AI needs monitoring for output quality, cost tracking, latency measurement, and user feedback collection. Companies should describe specific monitoring tools and alert thresholds.
Scoring Guide
Score | Criteria |
|---|---|
5 - Excellent | Documented AI-specific processes across all phases with metrics |
4 - Strong | Clear discovery, adapted sprints, AI testing methodology |
3 - Adequate | Some AI adaptations, basic testing, limited monitoring plans |
2 - Weak | Standard software processes with minimal AI adaptation |
1 - Insufficient | No AI-specific process adaptations |
Dimension 5: Cost Transparency
Weight: 10%
What to Evaluate
AI project costs include development fees plus ongoing inference, hosting, and maintenance expenses. Companies that quote only development costs create budget surprises that can make products financially unviable.
Cost Components to Request
Cost Category | What to Ask For | Red Flag |
|---|---|---|
Development | Itemized by phase (discovery, development, testing, deployment) | Single lump-sum quote with no breakdown |
Infrastructure | Cloud hosting, vector databases, model hosting estimates | No mention of infrastructure costs |
API/Inference | Monthly cost estimates at different user volumes | No discussion of ongoing API costs |
Monitoring | Production monitoring and alerting setup costs | Monitoring not included in proposal |
Maintenance | Monthly support, prompt optimization, model updates | No post-launch support offered |
Scoring Guide
Score | Criteria |
|---|---|
5 - Excellent | Itemized costs across all categories with volume-based projections |
4 - Strong | Development and infrastructure costs detailed, inference estimates provided |
3 - Adequate | Development costs broken down, mentions ongoing costs without specifics |
2 - Weak | Single development cost estimate, no ongoing cost discussion |
1 - Insufficient | Vague pricing, no breakdown, no ongoing cost awareness |
Evaluation Scorecard Template
Use this scorecard to compare AI development companies systematically.
Dimension | Weight | Company A | Company B | Company C |
|---|---|---|---|---|
Technical AI Depth | 30% | _/5 | _/5 | _/5 |
Production Track Record | 25% | _/5 | _/5 | _/5 |
Team Structure | 20% | _/5 | _/5 | _/5 |
Process Maturity | 15% | _/5 | _/5 | _/5 |
Cost Transparency | 10% | _/5 | _/5 | _/5 |
Weighted Total | 100% | _/5 | _/5 | _/5 |
Interpretation:
4.0-5.0: Strong candidate for AI-first product development
3.0-3.9: Acceptable for less complex AI projects with oversight
2.0-2.9: Significant gaps that risk project success
Below 2.0: Not recommended for AI product development
How Appolica Approaches Client Evaluation Conversations
At Appolica, we encourage prospective clients to evaluate us rigorously. Transparency during the evaluation phase builds the trust required for successful AI projects that involve uncertainty and iteration.
Our full-cycle development team includes:
Product Managers who walk clients through our AI feasibility assessment process, showing examples where we recommended against AI when traditional approaches served users better. In one project, our PM saved a client three months of development by identifying that rule-based notifications outperformed AI-generated suggestions for their specific use case.
Designers who demonstrate our AI-specific pattern library covering confidence indicators, regeneration flows, and feedback mechanisms. We share before-and-after examples showing how our AI UX patterns reduce user confusion by 60%.
Developers who present architecture diagrams from previous AI projects, explain trade-offs between approaches, and discuss specific challenges they encountered and resolved in production.
QA Specialists who describe our AI testing methodology that validates outputs across 100+ scenarios and catches 85% of potential issues before user testing.
We provide references from previous AI projects, access to live deployed products, and transparent cost breakdowns including ongoing inference estimates at your projected scale. Our 2-week discovery phase is designed as a low-risk entry point. Clients validate our technical capabilities with a defined scope and budget before committing to full development.
Ready to see how our team scores on your evaluation framework? Schedule a consultation to discuss your AI product vision.
Red Flags That Disqualify AI Development Companies
Immediate Disqualifiers
No production AI deployments. Companies without live, user-facing AI products lack the production experience required to build reliable systems. Prototypes and demos do not count.
Promises of 100% accuracy. AI outputs are probabilistic. Companies claiming perfect accuracy either misunderstand AI fundamentals or are being dishonest. Both are disqualifying.
No discovery or validation phase. Jumping directly into development without validating AI feasibility indicates a process that generates expensive failures. Discovery phases reduce mid-project pivots by 40%.
Single-person AI "team." One developer wrapping an API is not an AI development team. Production AI requires product management, design, development, and QA working together.
Serious Concerns
Reluctance to show live products. If a company cannot demonstrate deployed AI products, their experience is theoretical. Request specific URLs, app store links, or client-verified case studies.
No discussion of ongoing costs. Companies that ignore inference costs either lack production experience or are hiding total cost of ownership.
Fixed scope with no iteration flexibility. AI development requires iteration as model behavior reveals unexpected patterns. Fixed-scope contracts create adversarial dynamics when changes are needed.
Aggressive timelines. AI MVPs delivered in under 8 weeks almost certainly skip discovery, proper testing, and production hardening. The minimum realistic timeline for a quality AI MVP is 3-4 months.
