AI Integration Services

AI integration means connecting artificial intelligence capabilities, such as large language models, machine learning models or AI agents, to the software, data and workflows a business already uses. Instead of working in a separate AI tool, people get AI features inside their CRM, ERP, website, app or internal systems.

An AI integration usually has four parts: a connection to an AI model or service (often via API), a data layer that gives the model the right information with the right permissions, the application changes that show AI results to users or trigger actions, and monitoring for quality, security and cost. We design, build and test each part, then roll the integration out in stages.

The cost of AI integration depends on the number of systems involved, how accessible your data and APIs are, whether you use a hosted model API or a self-hosted model, security and compliance requirements, and expected usage volume, which drives ongoing API and hosting costs. A single AI feature in one application costs far less than AI connected across several platforms. We provide a written estimate after reviewing your systems.

A focused integration, such as adding an AI assistant or document extraction to one application, can often be delivered in a few weeks. Integrations spanning several systems, legacy software or strict compliance reviews typically take a few months. API availability and data readiness have the biggest impact.

Yes. We add AI features such as smart search, recommendations, chat assistants, content generation and image or voice capabilities to existing websites, web apps and iOS/Android apps, working within your current codebase and design. For customer-facing chat specifically, see our AI chatbot development services; for assistants that help users inside your product, our AI copilot development services.

We integrate OpenAI (ChatGPT/GPT models), Anthropic Claude, Google Gemini, cloud AI platforms such as AWS Bedrock, Azure OpenAI and Vertex AI, and open-weight models such as Llama and Mistral that can be self-hosted. We also integrate your own custom machine learning models. We recommend the option that fits your accuracy, cost, latency and data-residency needs.

Usually not. Most AI integration projects add AI alongside existing systems through APIs and middleware. For older software without APIs, we can build an integration layer around it, and, if it makes sense, plan a gradual legacy application modernisation.

We give AI only the minimum data it needs, enforce your existing user permissions on retrieved content, encrypt data in transit and at rest, mask personal data where possible, use provider settings that do not train on your data, defend against prompt injection and log every AI action. Private or regional deployments are available when data must not leave your environment.

Agentic AI integration connects AI agents to your systems so they can take actions, not just generate text: creating tickets, updating records, sending notifications or running multi-step workflows. We expose carefully scoped tools, often through Model Context Protocol (MCP) servers, add approval steps for sensitive actions and keep an audit log of everything an agent does. To have the agents themselves designed and built, see our AI agent development services.

CodeBase Coders provides end-to-end AI integration services: readiness assessment, integration architecture, LLM and agentic AI integration, CRM/ERP and app integration, data pipelines, model deployment and ongoing support. We also offer broader software integration services, AI development services for new AI builds and AI consulting if you are still deciding where to start. Contact us to plan your integration.