AI development agencies promise the moon—custom models, 90-day launches, AI that'll "transform your business"—but many fold within months or deliver half-baked prototypes. Knowing what's hype and what's real will save you tens of thousands in wasted spend.
Red Flags That Signal Overpromising
Guaranteed timelines without discovery. Any vendor quoting a fixed launch date before understanding your data, infrastructure, and actual requirements is guessing. Legitimate AI projects require 2–4 weeks of discovery alone. If they're saying "8 weeks to production model" before asking about your dataset size, model complexity, or compute constraints, walk away.
Claims of "AI for anything" without specialization. Agencies that sell generative AI, computer vision, NLP, and forecasting equally well often excel at none of them. Ask for case studies in your specific domain. A team strong in recommendation engines might struggle with time-series forecasting or document classification. Request references from similar use cases, not just any AI project.
Vague pricing or "enterprise quotes only." Transparent vendors publish indicative ranges. Early-stage model development typically runs $30K–$80K; production deployment and monitoring add another $20K–$50K annually. If a vendor won't discuss ballpark figures before a sales call, they're hiding either inflated costs or lack of experience. Request a detailed scope breakdown: data engineering, model training, validation, and deployment as separate line items.
What Real AI Development Looks Like
They ask hard questions first. A competent team will want to know: How much training data do you have? What quality is it? Where's it stored? What's your acceptable latency? What's the cost of a false positive vs. a false negative? If they're pitching solutions before drilling into these, they're not solving your problem—they're selling their template.
They include data prep and validation timelines. Most projects spend 60–70% of time on data cleaning, labeling, and validation, not model building. Budget 6–12 weeks minimum for structured data work. If a vendor glosses over this, they're either padding timelines later or building on garbage data. Insist on seeing a data quality audit and labeling strategy upfront.
They commit to measurable metrics, not vague success. "Improve customer satisfaction" is not a metric. "Reduce support ticket resolution time from 48 hours to 12 hours with 95% first-contact accuracy" is. Get specific about baseline performance, target performance, and how it's measured. Ask how they'll validate the model before handing it over (holdout test set, A/B testing, backtesting for time-series).
They plan for handoff and maintenance. Production models drift. A serious vendor will outline monitoring infrastructure, retraining cadence (typically quarterly or when performance drops 5–10%), and your team's role post-launch. If they're not discussing MLOps, model versioning, or performance dashboards, they're building a one-off that'll break in six months.
How to Vet Vendors Practically
- Request a technical lead interview before committing. Ask them to walk through a past project, including failures. How did they handle unexpected data issues? Did they miss a deadline? How did they recover?
- Ask for a proof-of-concept (POC) proposal with fixed scope, timeline (2–4 weeks), and cost ($5K–$15K). A real vendor can scope a POC tightly. If they won't do one, they're not confident.
- Check GitHub or public code samples. Strong ML teams have open-source work, research papers, or shared notebooks. This isn't mandatory, but it's a confidence signal.
- Verify their infrastructure knowledge. Do they understand your stack (AWS, GCP, on-prem)? Can they deploy models efficiently? Vendor lock-in or architectural bloat costs you later.
Platforms like Mercoly help you compare and find trusted AI & Machine Learning Development providers in one place, so you're not vetting vendors alone.
Frequently Asked Questions
Q: How do I know if a vendor is overstating their model accuracy? A: Ask for the test set they used, the baseline they're comparing against, and whether they report accuracy, precision, recall, or F1 score separately. Accuracy alone is meaningless; a model that guesses "negative" every time can still be 95% accurate on imbalanced data. Request the confusion matrix.
Q: What's a realistic timeline for building a custom ML model from scratch? A: Data prep and cleaning typically take 6–12 weeks; model experimentation and tuning add 4–8 weeks; production deployment and monitoring setup add another 2–4 weeks. Total: 3–6 months for a solid, production-ready system. Anything under 12 weeks should raise suspicion unless your data is exceptionally clean and the problem is simple.
Q: Should I pay for "AI consulting" separately from development? A: Yes, if it's a focused engagement (1–2 weeks, $5K–$15K) to define strategy and scope. No, if it's undefined or ongoing "advisory" before committing to a development contract. Many vendors disguise sales calls as consulting.
Use Mercoly to compare AI development providers side-by-side and find one that matches your timeline and budget, not their sales pitch.