AI Integration Services

Most AI that never ships is not broken. It runs in a notebook, or a chat window, and nothing it produces lands in the system where the work actually happens. We wire it in: into the CRM, the ERP, the claims platform, the ticket queue, with the permissions scoped, the writes logged, and a person on the cases that need one. The deliverable is a workflow your team uses, not a demo they liked.

AI integration into the systems you already run: CRM, ERP, claims and ticketing. Scoped permissions, logged writes, a human on the exceptions, and a workflow your team actually uses.

AI integration services are the work of connecting a model or agent to the systems a business already runs, so its output lands in the system of record rather than in a chat window. The work covers authentication and permissions, reading and writing against existing APIs, handling failures and rate limits, logging every action, and routing the cases a model should not decide to a person.

AI integration means wiring a model into the CRM, ERP or service platform where work already happens, with scoped permissions, logged writes and a human on the exceptions. The model is rarely the hard part. The integration is, because it has to handle authentication, failures, rate limits and the cases the model should not decide. Thinklytics scopes the data and the permission model before building, and the deliverable is a workflow in production rather than a demo.

A connection into the system that owns the record, so the output is acted on rather than copied by hand into something else.

A permission set written down before the build: what the AI may read, what it may write, and what it must ask a person about.

Failure handling that assumes the model and the API will both be unavailable sometimes, because they will be.

A log of every action taken, with enough context that someone can reconstruct a decision months later.

Where the Model Context Protocol fits, and where a direct integration is the better answer, decided per capability rather than as a standard.

A chatbot bolted onto a website. That is a different piece of work and usually a smaller one.

A rip and replace. The point of integration is that you keep the systems you have and the AI works inside them.

A one-way pipe. An integration that reads but never writes has not removed any work, it has added a screen to check.

Finished at launch. Model versions change, APIs deprecate, and an integration nobody monitors fails quietly.

A map of the systems in scope, what each one owns, and which is authoritative where two disagree.

The permission model: read scopes, write scopes, and the actions that require human approval.

The integration itself, built against the existing API rather than around it, with retries, back-off and idempotent writes.

An audit log of actions and the evidence behind them, with a retention decision made rather than defaulted.

The review queue for anything the model declines to decide, and a route for a reviewer to act quickly.

Monitoring on the integration itself, so an expired token or a deprecated endpoint surfaces as an alert and not as a silence.

Prior authorisation review, once the model was wired into the review workflow rather than run alongside it. 18,000 more cases a month, no extra staff.

False positives after a fraud model was integrated into the review queue, worth $2.9M a year in review effort.

Downtime surfaced once tracking was automated and fed into the systems the plants already used, across 6 sites.

It lives somewhere other than the system where the work happens, so using it means switching tools and re-entering data. People route around that every time.

It writes to the wrong record occasionally and we only hear about it later.

No identity resolution before the write, and no log that makes the error findable. Both are integration decisions rather than model problems.

They are being asked to approve broad access with no written permission set and no audit trail. That is usually the entire objection and it is cheaper to design than to argue.

An expired credential or a deprecated endpoint, with no monitoring on the integration itself. A silent integration looks identical to a working one.

The work of connecting a model or agent to the systems a business already runs, so its output lands where the work happens. In practice that means authentication and scoped permissions, reading and writing against existing APIs, handling failures and rate limits, logging every action, and routing the cases the model should not decide to a person. The model is usually the smallest part of the effort.

Because the model only has to be right on average and the integration has to be right every time. It has to handle an API that is down, a credential that expired, a record that exists twice, a rate limit mid-batch, and a write that must not happen twice if the first attempt timed out. None of that is interesting and all of it is where these projects fail.

Can you integrate AI into Salesforce, our ERP or our claims platform?

Yes, and the approach is the same shape in each case: work out which system owns the record, build against its API rather than around it, scope what the AI may write, and log what it did. Where a platform has a credible native option, we will say so, because an embedded feature you already pay for usually beats a custom integration for a workflow that lives entirely inside that platform.

Normally three things, and they are cheaper to design up front than to retrofit under pressure. A written permission set covering what the AI reads, writes and must ask about. An audit log with a retention decision. And a kill switch with a named owner in the business rather than in engineering. Most objections we see are not objections to AI, they are objections to being asked to approve something undefined.

Whichever the workflow justifies, and the honest answer usually depends on how many systems are involved. One system with a decent native capability rarely needs custom work. Several systems, a specific permission model, or a workflow that has to survive an audit tend to need something built. We scope that in the first stage on your actual systems rather than deciding it in advance.

A single workflow into one system of record usually runs six to ten weeks including the permission model and the logging. The variable is rarely the model and almost always the state of the data underneath, particularly whether the same entity resolves to one record. If it does not, that is its own piece of work and should be costed separately.

Both will. Model versions get deprecated and APIs change contracts, usually with notice that nobody is watching for. The integration needs monitoring on itself, not only on the business outcome, so a changed response shape or an expired token raises an alert. Without that, the failure mode is silence, which is the expensive one.

Yes, and we will tell you when it is the wrong tool. MCP describes a capability so any compliant agent can discover and use it, which is powerful when several agents need the same system and overhead when one agent needs one thing for one purpose. The design work moves from the connection to the permission boundary, because you are publishing what is possible rather than wiring what is intended. Narrow servers scoped to one capability, reads separated from writes, and the tool call logged with its arguments. That is what gets through a security review.

Workflow automation is about the sequence of steps and who does what. AI integration is specifically about getting a model's output into the systems those steps run in, safely. They overlap and are often bought together, but a workflow can be automated with no AI in it at all, and an integration can serve a workflow somebody else designed.