Appinventiv provides end‑to‑end digital product engineering and AI services that help enterprises and high‑growth companies design, develop, and deploy production‑ready, secure, and scalable AI‑driven solutions. Their offering includes AI strategy assessments, custom model and generative AI development, MLOps/AIOps pipelines, and seamless integration with existing enterprise systems while ensuring compliance and ongoing lifecycle management.
Funding
$100M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Enterprises and high‑growth companies often lack the in‑house expertise, scalable infrastructure, and governance frameworks required to design, develop, and deploy AI‑driven digital products at production scale. This results in prolonged time‑to‑market, security and compliance risks, and difficulty integrating intelligent capabilities into existing systems.
Solution
Appinventiv offers end‑to‑end digital product engineering and AI services that turn business ideas into production‑ready, secure, and scalable solutions. The firm conducts AI strategy and readiness assessments, defines roadmaps, and builds custom models, generative AI, computer‑vision, and RPA components tailored to each client’s data and workflow. Delivered architectures are cloud‑native, incorporate MLOps/AIOps pipelines for continuous monitoring, and integrate via APIs or micro‑services into legacy ERP, CRM, and other enterprise platforms. Throughout the engagement, Appinventiv ensures regulatory compliance, robust security, and long‑term support, enabling clients to operationalize AI without maintaining extensive internal AI teams.
Target Audience
Primary customers are large enterprises, Fortune 500 corporations, and fast‑growing startups across regulated and high‑impact industries that require AI‑enabled digital transformation and scalable product development.
Features
- AI strategy, readiness assessment, and roadmap creation aligned with business KPIs
- Custom model development, fine‑tuned LLMs, generative AI, computer‑vision, and ML pipelines
- Scalable MLOps/AIOps infrastructure for automated training, deployment, and monitoring
- Secure, compliant AI architecture with data governance, privacy, and audit capabilities
- Seamless integration via API, micro‑services, and containerized deployments into existing enterprise systems
- End‑to‑end product engineering covering design, development, testing, deployment, and post‑launch optimization
- Ongoing lifecycle management, model retraining, and performance optimization services