How to Connect AI to Your CRM and Internal Tools

Tg Apps · Published October 7, 2026 · Sources checked October 7, 2026

Connect your business to the AI assistant your team uses. Get an on-demand view of sales, projects, and pending work, ask questions, and manage everyday tasks remotely. Start with the information and workflows that would make your operation easier to follow.

Start with a question your team already asks

A good first workflow has a clear business result: identify pending requests, summarize a customer history, or explain which projects need attention. Choose the system holding those records and the person who should be allowed to use them.

Sales: review a lead and its recent activities before a call.
Support: summarize a customer's requests and open the source records.
Operations: list overdue tasks or compare project progress with agreed milestones.

These are examples of workflows we can build. Define which records and fields the answer needs, how current they must be, and how the user will check the result. A concise answer with record references is more useful than a broad chatbot with no connection to the operation.

MCP, an API integration, or RAG?

These approaches solve different parts of the integration and can work together:

An API integration connects an AI feature directly to your app or backend. It is useful when the assistant belongs inside your existing CRM, dashboard, or customer product.
MCP lets a compatible assistant discover and call defined tools, such as listing pending requests. The MCP server can reuse your existing APIs and business services.
RAG retrieves relevant documents or records before generating an answer. It is useful for company knowledge, support material, and answers that point to their sources. It can sit behind an API or an MCP tool.

Choose the approach around the workflow and the software you already operate. A system with a usable API may need a small adapter, not a rebuild. Document search and an action such as creating a task have different requirements, even when both appear in the same conversation.

Connect the assistant your team uses

ChatGPT supports custom MCP connections under account and workspace policies. OpenAI Docs describes connection and tool testing. Claude documents custom connectors using remote MCP. Gemini-powered agents can use compatible clients such as Gemini CLI.

Select the actual assistant client, account, authentication method, and supported tools before implementation. A model name alone does not identify the connection your team will use. Test discovery and a real query in that client, then test each approved update flow.

How a request becomes a controlled business operation

The user asks a question, such as which requests are waiting for follow-up.
The assistant selects a defined tool and supplies its parameters.
The server verifies the user, the company account, the permitted records, and the requested operation.
Existing business services retrieve the necessary fields or validate the specific update. Changes that need review follow the agreed approval step.
The tool returns a structured result. The assistant presents it, and the user can inspect the relevant records.

Your business remains in control of available tools, permissions, and access revocation. The server enforces these rules. For updates, define what happens if a request is repeated or the record changes between preparation and execution. Activity records should capture the operation and result with proportionate data retention.

Also agree which fields may reach the AI provider and how its data policy fits your requirements. Authentication and tool permissions govern access to the system; provider configuration governs how information sent to the model is handled.

What to verify before choosing an integration partner

Ask for a demonstration tied to your first workflow, not only a list of model names. Verify a permitted query, a denied request, access revocation, source records, and the behavior of an approved update. For multi-company systems, include a test proving that one company cannot access another's records.

Agree who owns the code and accounts, who publishes and monitors the integration, and how changes to tools or providers are tested. Compare development capacity, release responsibility, ongoing support, model usage, and hosting rather than only an initial setup price.

For a concrete implementation example, read our MCP case with a Go backend and Flutter management interface. It explains the adapter, delegated access, and the checks performed.

MCP development within your Tg Apps monthly plan

At Tg Apps, MCP development is optional and included in your monthly plan, within agreed priorities and capacity. It can share the engagement with your app, CRM, backend, and other integrations. Model usage, hosting, and third-party subscriptions are aligned separately before kickoff.

Starter is USD 1,500/month, Growth is USD 2,000/month and is the most selected plan, and Embedded starts at USD 3,500/month. The monthly amount is fixed in your agreed billing currency before kickoff. Meetings and delivery cycles follow the selected plan, and production releases follow the agreed release plan.

We follow the agreed client plan, flag tradeoffs, and share implementation insights when useful. Contract and NDA come before kickoff, with no upfront payment. TG APPLICATIONS DESENVOLVIMENTO LTDA is identified by D-U-N-S 651029828. Start with a defined working result under the First Milestone Guarantee, then expand the workflows your team finds useful.

Explore AI and MCP integrations for CRM and internal tools and tell us which task you want to simplify first.

Technical sources and service details

Platform documentation describes supported connections. The workflows in this guide are planning examples; each integration is tested with the client and account chosen for the project.