AI development and integration for business systems

AI delivers value when it is connected to a real business process, real data and the people who use it every day. AC is an AI development company in Israel that builds AI systems for businesses: we integrate large language models (LLMs) into the systems you already run, build AI agents and assistants on top of your business data, and develop AI automation that reduces manual work, all with permissions, human review and a full audit trail.

What we build

• AI integration into existing systems: drafting, summarization, classification and smart search inside the screens your team already uses, in your CRM, management system or customer portal.
• AI agents: agents that do not just answer but take actions in the system, such as opening an order, updating a record, generating a document or triggering a workflow, within permissions and with human approval for sensitive steps. More on the AI agents & chatbots page.
• Chatbots and assistants grounded in your business data: an assistant that answers employee or customer questions from your documents, procedures and data, and cites the source of each answer.
• Document and data processing: ingesting files, forms, invoices and messages, extracting the relevant data and entering it into the system, with human review when confidence is low.
• AI automation: workflows that start from an email, a document or a free-text request and end with a structured action in the system.
• AI-operated platforms: systems where business processes, workflows, interfaces and integrations are configured through conversation with AI rather than through settings screens.

LLM integration into existing systems

Most businesses do not need a new system to benefit from AI. The language model connects as a separate service to the existing system, through its API or a dedicated data layer. The service only receives data the user is permitted to see, returns a result, and every call is logged. That way AI can be added to a management system, CRM or ERP without a rewrite, and the model or provider can be swapped later without touching the rest of the system.

From our experience

AC's founder, Adir Cohen, leads a B2B platform that can be fully operated through AI, from setting up business processes to building interfaces and integrations. AC has also built an AI-driven hiring platform in the HR-tech space. The lesson from both is clear: AI works well when it has clear boundaries, every action is logged and a person can review and correct the result.

How we approach an AI project

1. Pick one process with a clear, measurable benefit.
2. Map the data: what goes in, what comes out and who may access what.
3. Deliver a first version with human review and full logging.
4. Measure and improve: quality, time saved and model usage cost.
5. Expand to more processes only once the first one works.

Technology

Large language models from leading providers, semantic search over your documents and data, Python with FastAPI or Node.js on the backend, queues and background jobs for long-running tasks, and usage and cost tracking. AI is integrated as a defined service, so models can be swapped without rewriting the system.

Most AI projects live inside a new or existing business system and rely on integrations with your current tools, for example connecting an LLM to your ERP data. Have a process in mind? Tell us about it.

Frequently asked questions

How much does it cost to develop an AI system?

It depends on scope: how many processes the system covers, which data sources it connects to, how much review and logging is required and how it fits into your existing systems. Beyond development there is an ongoing model usage cost that depends on request volume. After an intro call and an initial discovery phase we provide a detailed estimate, and we usually recommend starting with one process where the benefit is clear.

What is the difference between a chatbot and an AI agent?

A chatbot answers questions: it takes a question, searches the information it was given and returns an answer. An AI agent also takes actions: it can open an order, update a record, generate a document or trigger a workflow, within defined permissions and with every action logged. We usually start with an assistant that answers questions and add actions gradually.

Is our data safe?

We define up front which data is sent to the model, and each user only sees what their permissions in the system allow. Every action is logged, and sensitive actions require human approval. Where needed, usage limits and quotas are set per user or organization, and the provider and terms of use are chosen to match the sensitivity of the data.

Can AI be added to an existing system?

In most cases, yes. A separate service layer connects to the existing system through its API or database, so the system does not need to be replaced. We start with one process and measure before expanding.

What is the difference between regular automation and AI automation?

Regular automation runs a defined process, such as syncing data or generating a document. AI automation adds the ability to handle unstructured input: emails, documents, forms and free-text requests. Much of the value comes from simple, reliable automation, with AI used where it is genuinely needed.