AI integration services that work inside the software your team already runs

Who are AI integration services for?

Businesses where someone reads in one system and types into another. An order arrives by email and gets keyed into the order system. A lead fills in a form, and someone copies it into the CRM. An invoice lands as a PDF, and its lines go into the accounts by hand. An AI integration connects a language model to those systems so that step stops being done by hand. What it takes depends on the business:

• Established local businesses: one inbox, a CRM or job tool and the accounting software, and an owner who decides. Usually one connection, one task and one person who checks what it holds back.
• Multi-location and regional businesses: the same task arriving at several sites, sometimes in different setups of the same system. The integration has to know which site each item belongs to, and who answers for it there.
• National businesses: many systems, narrow permissions, a record of every change, and sign-off from IT, security or legal before anything connects. Your legal or compliance team has the final word on whether a use fits your industry’s rules.

We build AI integrations as part of our AI automation work. When a connection is one piece of a bigger change across several systems or teams, an AI implementation plan sets the whole of it out first, and when you’re weighing several uses of AI, an AI strategy and roadmap puts them in order. Everything else we do with AI is on our AI consulting page.

What can a model do inside the software you already use?

Five kinds of work, mostly:

• Read: pull the details out of an email, a PDF, a scan or a photo: names, dates, addresses, line items.
• Sort: decide what something is (an order, a complaint, a change of address, a new lead) and send it to the right place.
• Match: work out which customer, account or job an item belongs to when nobody wrote the account number down.
• Draft: write a reply, a note on a record or a summary for a person to check and send.
• Answer: look up a record and tell a staff member what it says, in plain words.

Ordinary code does the arithmetic: totals, dates and account numbers are checked against your records. The OWASP GenAI Security Project, which publishes security guidance for applications built on language models, recommends the same split: have the model give its output in a set format, then check that format with deterministic code. So most of an integration is the code around the model: the connection, the checks, the rules about what it may write, and the record of what it did.

How do I connect AI to my CRM?

Check what your CRM already does first. Many CRMs now come with AI features of their own, and if one of them covers your task, switching it on may be all you need. When they don’t fit, AI CRM integration services connect a model to the CRM through its API, the part of it built for other software to read and write records. Through that connection, a model can:

• Turn messages into records: read an email or a web form and create or update the contact, the company and the deal.
• Keep records current: spot a new phone number or address in an email signature and update the contact.
• Sort new leads: read what someone asked for and route it to the right person or pipeline.
• Draft the next step: write a follow-up for a salesperson to check and send.

Two decisions come before the choice of model. First, the integration checks before it writes: it looks for an existing contact before creating one, so the CRM doesn’t fill with duplicates. Second, you decide which records and fields it may change, and which it may only read. And if the CRM itself is the problem, the fix is a different job: our pages on custom CRM builds and CRM implementation cover replacing one or setting one up properly.

How do I add ChatGPT to my existing business software?

Through OpenAI’s API. ChatGPT is the app people type into; software reaches the same company’s models through its API, and that’s what ChatGPT integration services connect to. Model names change often, so the setup comes down to three things:

• What it’s told: the instructions, the examples and the records it gets for each task.
• What it’s checked against: your own data, so a reading that doesn’t match a real customer or order is caught before it’s saved.
• What happens to your data: OpenAI’s platform documentation says data sent to its API isn’t used to train or improve its models unless you opt in, and that its abuse-monitoring logs are kept for up to 30 days, unless longer retention is required by law or reasonably needed to protect its services or others from harm. Eligible customers can apply for zero data retention, which needs OpenAI’s prior approval and comes with extra requirements.

Every provider sets its own terms, and terms change, so we would check the current ones for your use before choosing. Other providers sell API access to their models too, and the one behind your integration should be picked for the job.

How do I integrate LLMs into workflow automation?

Treat the model as one step in the workflow. A run usually has four parts: a trigger (a new email, a form, a record that changes), the model step (read, sort or draft), a check in code (does the result match your records?) and an action (create, update or hold). Zapier lists ChatGPT (OpenAI) integrations, Make documents an OpenAI (ChatGPT, Whisper) app, and n8n’s documentation describes AI workflows that connect LLM providers such as OpenAI, Anthropic and Google, so a first version can often be built on a tool you may already pay for. Our guide to Zapier, Make, n8n and custom code compares them.

LLM integration services earn their fee when the workflow has to hold up under real volume, day after day. That takes:

• Checks: every reading tested against your records before anything is written.
• Retries and a fallback: when the provider doesn’t answer, the item is tried again, then handed to a person.
• Limits: a cap on spend and on how much it handles in an hour.
• A log: what came in, what the model returned and what was written, kept for the day someone asks why.

When those matter, we deploy the step as code of our own, connect it through your systems’ APIs and run it. If the task needs no reading at all, it needs no model either: fixed rules in Zapier, Make or n8n will do it, and we’ll tell you so.

Is it safe to connect an AI model to company data?

It can be, if the integration is built on the assumption that the model can be fooled. The OWASP Top 10 for LLM applications, 2025 edition, puts prompt injection first: text in an email, a web page or a file that tries to give the model new instructions. OWASP calls that the indirect kind and says it is unclear whether prompt injection can be fully prevented. Sixth on its list is excessive agency: a model with more functions, permissions or freedom than its job needs. What we would build follows five rules from that guidance:

• The least access the task needs: read-only wherever it only reads, with its API keys kept in code, out of the model’s reach.
• Outside content kept separate: an email’s text goes to the model apart from its instructions, clearly marked as untrusted.
• A person approves high-impact actions: a payment, a cancellation or anything hard to undo waits for a confirmation.
• Permissions checked in your own systems: the CRM or ERP decides what the integration’s account may do.
• A record of every change: OWASP notes that logging and monitoring limit the damage, though they don’t prevent it.

The data side comes down to the provider’s terms: what it keeps, for how long, and whether it trains on it. And whether your industry’s rules allow a given use of company data is a call for your own compliance review.

What happens when a connection breaks or the model misreads?

Something will go wrong eventually, so the design question is what happens when it does. The failures to plan for:

• The model misreads: OWASP lists misinformation, false or misleading information that appears credible, among its top risks. The check against your records is there to catch it, and a caught item is held for a person.
• The provider is down: the item is retried, then passed to your team to handle by hand.
• A connected system changes: an upgrade or a new version of an API can break a connection that worked yesterday, so the integration has to notice and say so.
• Access lapses: a key expires or a password changes, and every write fails until it’s renewed.
• Too much arrives at once: a cap on spend and volume stops a runaway bill.

A health check that reports failures loudly matters as much as any of these: a connection that fails quietly does more harm than one that fails often. The integration also needs a switch that stops it writing while the reading carries on, so new work keeps arriving where your team can see it. No one can promise a model that never misreads. The aim is narrower: when it’s wrong, the mistake should surface as a hold for a person to answer.

What does a working AI integration look like?

Alvy Logistics and DSI Transportation share an operations system we built and run with them, AlvyOne, and its order intake is an integration of exactly this kind. Alvy is owned by our co-founder Noah Alvarez, and DSI is an Alvy company. Every new email that reaches the two companies’ order inboxes gets read, and the account is worked out from three clues: who sent it, the reply-to address and the signature. Since October 23, 2026, a dispatcher can confirm that account in the team’s chat. Next comes a check of the address and of whether the order already exists, and only then is the order written into the dispatch system over that system’s API. Switched on June 29, 2026, it has built orders since June 30, posting each one to the dispatch team’s chat as it goes in. When something is missing, it holds the order with a note saying what, for a dispatcher to answer. No driver is assigned automatically: the order is where it stops. Alvy’s staff also have a dispatch assistant in their team chat, in use since July 3, 2026 and able to take requests with several steps since October 10; it asks a dispatcher to confirm before any risky change. The AlvyOne case study has the full story.

When is a plain API integration enough?

When the data already arrives in fixed fields. If a web form, a payment system or a booking tool hands over the same fields every time, moving them into another system is a job for rules alone: a direct API integration, with no model and no model costs. A model earns its place when a person has to read something before they can type it: free text, attachments, documents that come in many layouts, or a judgment about which account an item belongs to.

Many integrations are both. The fixed fields move by rules, and the model takes the part that needs reading. We’ll tell you which parts of your task need a model and which don’t, and if none of it does, you’ll hear that from us, along with the plain version.

What should you ask an AI integration company?

Whoever builds it, these questions show whether an integration will hold up once it’s live:

• Which permissions does it need, and why? A good answer is a short list tied to the task.
• What can it write, and what can it only read? Anything it writes should be checked first.
• What happens when one of our systems changes? Ask who notices, and how you’ll hear about it.
• Where does a held item show up, and who answers it? It should land where your team already works.
• How is it switched off, and what happens to work that arrives meanwhile? Nothing should be lost while it’s off.

The proposal sets out our own answers to those questions: what we would build, what it may read and write, how it runs and what it costs. Once it’s live, looking after it is monthly work we take on, on terms the proposal sets. Our AI consulting cost page has the setup and monthly ranges US agency price pages and guides show.

If someone on your team spends the day copying what they read into another system, book a 15-minute call. Name the systems and the task, and we’ll say whether the job calls for a model, a plain integration or neither.