AI Integration Services: How to Connect AI to Your CRM, ERP and Website

An AI model on its own knows nothing about your customers, orders or internal rules, and it cannot act in your systems. AI integration services add those missing pieces:

• Access to your knowledge. Help centre articles, product data, contracts, policies and wikis the AI should answer from.
• Connections to your systems. CRM, ERP, help desk, e-commerce platform, CMS, email and messaging tools, through their APIs.
• Identity and permissions. Making sure the AI sees and changes only what the person or process using it is allowed to.
• Model choice and hosting. Picking hosted models or open-source ones, and where they run.
• The interface where people work. A chat on your website, a panel in your CRM, a bot in Telegram or Teams, or a feature inside your own product.
• Monitoring. Logs, quality checks and cost tracking after launch.

An AI integration company that does this well spends less time on the model and more on everything around it.

Most business projects fall into one of these patterns, or combine two of them.

1. Knowledge assistant

An assistant answers questions from your own documents. The usual technique is retrieval-augmented generation (RAG): the system first finds the relevant passages in your content, then the model writes an answer based on them. The approach was described in a 2020 research paper by Lewis et al. and is now standard for support and internal knowledge assistants. Showing the sources next to each answer makes it easy to check.

2. An AI step inside an existing workflow

The process stays as it is, but one step is done by AI. For example, an incoming email is read, classified and turned into a CRM record, and anything unclear is assigned to a person. This pattern is often the fastest to show value because nobody has to learn a new tool.

3. LLM features inside your product

Search, summaries, drafting or translation added to your software, client portal or CMS. Here the integration work includes prompt design, permissions per customer, testing and keeping model costs predictable.

4. An agent with tool access

An AI agent decides which steps to take and calls tools, such as "look up order" or "create ticket", to reach a goal. Agents are powerful for open-ended tasks but harder to control. Standards like the Model Context Protocol make it easier to expose tools to models in a consistent way. We cover agents in more depth in how to choose an AI agent development company.

5. Chat channels

The same assistant can serve several channels: your website, Telegram, WhatsApp, Slack or Microsoft Teams. Integration here means one knowledge base and one set of rules behind every channel, with a handover to a person that works in each of them.

CRM

Typical CRM integrations create or update contacts and deals from emails and forms, summarise conversation history before a call, or draft follow-ups. The details decide whether it works: which fields the AI may write, how duplicates are detected, and what happens when a required field is missing. Systems like HubSpot and Salesforce have well-documented APIs (see the HubSpot API overview, for example), so the hard part is usually the business rules, not the connection.

ERP and documents

Invoices, delivery notes and order confirmations often arrive as PDFs or emails. AI can extract the fields and pass them to the ERP, with validation against existing records, for example checking that the supplier and purchase order exist, before anything is booked. Low-confidence cases go to a person.

Website and CMS

On the website, the usual integrations are a customer assistant and AI help for editors: drafting summaries, translations or product descriptions for review in the CMS. As a Strapi partner, we can add these features directly to Strapi-based sites, with the editorial team approving every change before it is published.

Most AI integration projects that disappoint have a data problem, not a model problem. Before building, answer four questions:

• Where does the information live? List the sources: help centre, shared drives, wiki, CRM notes, product database.
• Is it current and consistent? Three versions of the same price list will produce three different answers.
• Who owns it? Someone has to update the content when products, prices or policies change.
• Who may see what? If a document is restricted to the finance team, the assistant must respect that for every user.

Cleaning up the most important sources is often the best investment in the whole project. It also helps the people who use those documents today.

Connecting AI to business systems means connecting it to personal and confidential data. Plan for these points from the start:

• Data protection agreements. If a provider processes personal data on your behalf, you need a data processing agreement under GDPR Article 28. This applies to the integrator and to the model provider.
• Hosting location. Some companies require data to stay in the EU or on their own servers. This narrows the choice of models and should be decided early.
• Impact assessment. Processing that is likely to result in a high risk to people may need a data protection impact assessment under GDPR Article 35.
• Transparency. The EU AI Act includes transparency duties for AI systems that interact with people, such as telling users they are talking to an AI. See Article 50.
• Prompt injection and data leakage. Content from emails or web pages can try to manipulate the model. The OWASP Top 10 for LLM Applications describes these risks and how to reduce them.
• Logging. Keep a record of what the AI was asked, which sources it used and what it changed, so you can review and improve it.

The UK Information Commissioner's Office publishes practical guidance on AI and data protection that is useful well beyond the UK.

Hosted models from providers such as OpenAI or Anthropic are quick to start with and strong across many tasks. Open-source models can run on your own infrastructure, which helps with strict data requirements, at the cost of more operational work.

The safer architectural choice is not to depend on a single model. Keep prompts, tools and evaluation tests in your own code, with a thin layer between your application and the model. Then you can switch models when prices, quality or requirements change, and test the new model on the same cases before you do.

1. Audit. Pick the processes with the clearest return and check which systems and data they need.
2. Pilot. Build one use case on real data with success criteria agreed up front, and test it on real examples.
3. Integration. Connect it to the production systems with permissions, error handling, logging and a handover to a person.
4. Rollout and training. Introduce it to the team, collect feedback and adjust.
5. Monitoring and improvement. Track quality and cost, update prompts and models, and add the next use case.

Each stage ends with a decision. If the pilot does not meet its criteria, you stop with a small, known cost.

• Starting with the model instead of the process. The question is which task to improve, not which model is best.
• Integrating everything at once. One working use case teaches you more than five half-built ones.
• No owner. Someone on your side must own the content and the results.
• Giving the AI broad access. Start with read-only access where possible and add write permissions step by step.
• No measurement. Without a test set of real cases, you cannot tell whether a change made things better or worse.
• Forgetting the people. The team that does the work today knows the edge cases. Involve them early.

NewProWeb is an AI integrator for B2B companies. We have spent years building production software, CMS platforms and integrations, and we now connect AI assistants, automation and LLM features to the CRM, ERP, website and documents our clients already use, with access control and logging built in. See our AI automation and integration services, or start with a free AI audit.

AI integration services turn a capable but generic model into something that knows your business and works inside your tools. The model matters less than the data behind it, the systems around it and the rules that govern it. Start with one process, connect it properly, measure it on real cases, and build from there.

For a broader view of the services around integration, read AI automation services: what they include and how to choose a provider.