What should I look for in a top AI development company?

Prioritize proven production experience (case studies, client testimonials), technical depth in your specific AI domain (LLMs, RAG, agents, computer vision), flexible engagement models (project, staff aug, managed services), industry expertise if regulated, transparent pricing, and strong team stability. Avoid firms claiming expertise across all AI domains; specialize firms typically deliver better outcomes.

How much does AI development cost in 2026?

Cost varies by engagement model: MVP/PoC projects range $50K–$200K; custom AI applications $200K–$1M+; staff augmentation $8K–$15K/month per engineer; managed services $5K–$30K/month based on scope. Nearshore (India, Eastern Europe) offers 30–50% cost savings vs. onshore. Enterprise transformations with Accenture, IBM, or Infosys start at $1M+. Budget accordingly for data preparation, model training, and production monitoring.

What's the difference between AI consulting and AI development companies?

AI consulting firms (Accenture, McKinsey) focus on strategy, roadmap, and organizational change; AI development companies (AgileEngine, Azumo, EPAM) build production systems. Choose consulting for enterprise transformation; choose development firms for custom application building. Many firms offer both, but their core strength differs. Clarify desired outcomes before engagement.

Should I work with a mega-firm or a boutique AI development company?

Mega-firms (Accenture, Infosys, IBM) excel at large-scale enterprise transformation, compliance, and organizational change. Boutique firms (AgileEngine, Azumo, LeewayHertz) deliver faster innovation, lower overhead, and production focus. Choose mega-firms for org-wide change; choose boutique for rapid, cost-effective AI development. Consider hybrid: boutique for custom development + mega-firm for enterprise integration.

How do I evaluate an AI development company's portfolio?

Request 3–5 detailed case studies matching your industry and AI domain (LLM, RAG, agents). Ask for client references and permission to contact them directly. Look for published metrics (accuracy improvements, latency, cost savings). Assess team stability (senior engineer tenure, ML leadership). Review code quality practices (testing, evaluation, monitoring). Avoid firms unable to share concrete project outcomes.

What's the typical timeline for AI development projects in 2026?

MVP/PoC: 6–12 weeks. Custom LLM/RAG application: 3–6 months. Production agentic AI system: 4–9 months. Enterprise AI transformation: 12–24+ months. Timelines depend on data readiness, team availability, and scope clarity. Include buffer for data preparation (often 30–40% of project time), model evaluation, and production hardening. Agile delivery with 2-week sprints enables faster feedback and course correction.

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