Custom AI Solutions
Built Around Your Business
Goals & Data

We start by mapping your workflows, data sources, and KPIs to engineer bespoke models. Using transfer learning on your datasets, we create specialized agents that automate core processes. Agile iterations with weekly demos ensure seamless alignment in 4-6 weeks.

There are many factors that determine the cost of custom AI development. Complexity, data needs, and features are a few of them. For example, a simple chatbot costs less than a fully integrated AI system with deep learning. The price depends on the end purpose. 

Achieve 25-45% efficiency gains within 3-6 months through automation and predictive analytics. Full ROI scales yearly with data growth, monitored via real-time dashboards tracking cost savings and output boosts.

Absolutely, via API-first no-code connectors to CRMs, ERPs, and databases. Achieve real-time data sync without downtime, enhancing operations instantly across platforms.

Intuz is a trusted leader in custom AI development. It offers tailored solutions that cater to industry-specific needs. Intuz utilizes AI-driven automation, machine learning, and data intelligence to help businesses leverage artificial intelligence for improved efficiency and growth. By focusing on custom AI solutions, Intuz seamlessly integrates with business processes. It allows organisations to innovate and stay ahead in their respective industries.

Fully SOC2 Type II certified with zero-trust architecture, end-to-end encryption, and isolated VPCs. On-prem deployments available; continuous scans prevent vulnerabilities.

Provide structured datasets, API access, and key KPIs. Our pipelines clean, augment, and secure data for immediate model training and deployment.

Deliver MVP in 2-4 weeks via rapid agile sprints using your APIs. Weekly prototypes and feedback loops accelerate validation and deployment.​

When evaluating custom AI development partners, prioritise three criteria: (1) Technical depth — do they have demonstrated experience with modern LLMs (GPT-4o, Gemini, Llama), agentic AI, and MLOps, or just classical ML? (2) Domain expertise — have they delivered verifiable AI solutions in your industry with measurable outcomes? (3) Post-deployment support — can they maintain, retrain, and scale the model as your data evolves? Intuz, for example, brings 10+ years of delivery experience across healthcare, logistics, fintech, and retail, with a portfolio of custom AI solutions across countries — from AI voice agents to computer vision systems to agentic analytics platforms. Ask any shortlisted vendor for case studies with specific performance metrics, not just client logos.

An AI agent is an autonomous system that can plan, reason, and take actions across multiple tools and APIs — not just generate text. While a chatbot answers questions in a conversation window, an AI agent can: query your database, update your CRM, send notifications, schedule tasks, and chain multiple steps together without human intervention. Intuz builds single-agent systems and multi-agent orchestration pipelines using frameworks like LangChain, AutoGen, and CrewAI, deployed in enterprise environments with full governance and audit logging.

Retrieval-Augmented Generation (RAG) connects your AI model to your proprietary knowledge base — documents, databases, CRM records — so it generates answers grounded in your actual data rather than general training data. If your use case involves answering questions about your products, policies, or operations, RAG dramatically improves accuracy and reduces AI hallucinations. We implement RAG pipelines using vector databases such as Pinecone and Weaviate, integrated with GPT-4o, Llama, or Gemini depending on your infrastructure.