Below are leading firms and providers you can evaluate for mobile app development that include strong AI/ML or generative‑AI capabilities (enterprise leaders, specialist engineering shops, and marketplaces). For each I note the main AI strength and a short reason to consider them. I included sources you can use to verify capabilities and recent activity.

Enterprise / global systems integrators and engineering leaders

• Accenture — full‑stack mobile + AI services (AI strategy, generative AI, MLOps, cloud) for large enterprises; strong systems‑integration and industry practices. (epam.com)
• IBM (IBM Watson / Cloud) — mobile backends, Watson AI services (NLP, vision, speech) and enterprise integrations for secure, regulated apps. Good for data‑governance heavy use cases. (IBM.com)
• EPAM Systems — large digital engineering firm with AI/GenAI platforms (DIAL, AI orchestration), cloud partnerships and recognized by Gartner for custom software; often used for AI‑enabled mobile and web applications. (epam.com)
• Cognizant / Infosys / Capgemini / Deloitte — (large consultancies) offer mobile engineering combined with AI/ML practices, cloud integrations, and vertical solutions (finance, healthcare, retail). These firms are typically chosen for scale, compliance, and global delivery. (epam.com)

Mobile & AI specialist engineering firms (strong for product/consumer apps or rapid AI integrations)

• Appinventiv — a focused mobile engineering firm that positions itself as “AI‑native,” embedding ML and generative features into apps; good for end‑to‑end mobile product builds. (Appinventiv.com)
• WillowTree — product design + mobile engineering with growing practice around AI‑enabled experiences (personalization, conversational UI). Good for UX‑forward AI apps. (WillowTree corporate materials and case studies). (startups.epam.com)
• Lightricks — maker of consumer AI‑first mobile apps (image/video generative AI, editing pipelines). Great example of AI applied to mobile UIs and media workflows. (en.wikipedia.org)
• ThoughtWorks / ThoughtBot — smaller than the big integrators but strong in modern engineering, ML/AI pipelines, and product thinking for mobile + AI prototypes and pilots. (ThoughtWorks has published GenAI/ML guidance; ThoughtBot is strong on rapid product engineering.)

Product engineering / platform houses and fast‑growing AI app shops

• EPAM Startups & Appinventiv (already cited above) and other shops such as AppTek, Kittyhawk, and comparable boutique firms that combine mobile UI/UX with ML/AI model integration. EPAM has explicit GenAI platforms and marketplace listings. (epam.com)

Talent marketplaces and freelance/teams for fast, tactical builds

• Toptal / Upwork / Fiverr Pro — access to vetted AI/ML mobile engineers and designers for smaller projects or augmentation (can deliver prototypes that integrate OpenAI, CoreML, TensorFlow Lite, Whisper, etc.). Useful when you need fast hires or specialist contractors. (Fiverr.com)

AI‑native startups and no‑code / low‑code builders (speed / prototyping)

• Emerging “vibe coding” and AI app generators (examples from YC cohorts and new startups) — these let teams rapidly prototype mobile experiences using LLMs and code generation; good for fast experimentation but beware production governance. Examples: Bitrig, Okibi and other YC startups focused on AI/code generation. (businessinsider.com)

How to pick the right partner (short checklist)

• Use case fit: choose consultancies/engineering firms for regulated or large‑scale systems; specialist shops for consumer‑facing, media or UX‑heavy AI apps; marketplaces for short‑term hires. (IBM.com)
• Model & data governance: verify their approach to LLM safety, data residency, PII handling, model‑retraining and MLOps (important for production mobile apps). (epam.com)
• Cloud & partner ecosystem: prefer firms with partnerships to AWS, Google Cloud (Vertex AI), Azure — simplifies deployment, scaling and security. (epam.com)
• Proven examples: ask for case studies with metrics (downloads, engagement lift, latency, model accuracy) and references.

If you want, I can:

• Narrow this to 4–6 firms tailored to your budget, industry and whether you need prototype vs production; or
• Prepare an RFP template & short list questions to evaluate AI capabilities (MLOps, LLM usage, model privacy, latency, costs).

Which would you prefer?