Key takeaways
- AI integration services connect AI to systems you already own and build it into a named workflow, rather than selling you a new platform to replace them.
- A typical engagement has six stages: free discovery call, paid audit, proposal, development, integration and training, and optional maintenance.
- You supply access, a named business owner, real examples of the work and people who will test it; the provider cannot invent any of these.
- Cost follows scope: workflows, systems and their APIs, data quality, approval design and what happens after launch.
- You may not need a provider if a tool you already license has the feature switched off, or if one small connection can be built by your own IT team.
Your team already pays for a CRM, an ERP, a shared inbox and somewhere to keep documents. The slow part of the work is the human in the middle: someone reads an email, opens a PDF, retypes three fields into the ERP and pings a colleague for approval. You are now looking for a firm to connect AI to that chain, and the pitches you are getting all sound alike. This guide explains what you are actually buying, what the work looks like, and how to judge a provider before you sign anything.
What do AI integration services include?
AI integration services connect AI to the software and information your business already uses, then build that connection into a specific workflow. The deliverable is not a chatbot on a website. It is a working process: a request arrives, the AI reads or classifies it, the right systems are updated, and a named person approves anything that matters.
The work usually falls into four types, which the AI Integration & Automation service covers in full.
- Workflow automation: intake, routing, reminders, approvals, handoffs and status updates across people and systems.
- AI agents with human approval: software that interprets a request and carries out defined steps using approved tools, with a person signing off on consequential actions. See AI agents with human approval.
- Document intelligence: reading invoices, contracts and forms, checking required fields, and routing anything incomplete or uncertain to a reviewer.
- Company knowledge search: answering questions from approved internal sources, with references so people can check the answer.
Underneath all four sits the same unglamorous work: connecting to an API (the interface one piece of software offers so another can read and write its data), mapping fields between systems, handling the cases where data is missing or wrong, and logging what the AI did. That connective work is what an integration provider is really selling. The AI model is the smaller part. For the technical side, connecting AI to your ERP, CRM and data warehouse goes deeper.
What does a typical AI integration engagement look like?
The stages below describe how Kastling runs an engagement. Other providers vary, but a credible one will be able to tell you where each of these happens in their process.
- Discovery call. A free conversation about the problem, the systems involved and whether there is a fit. It commits you to nothing, and a good call ends with an agreed next step, which may be "this is not for us."
- Paid audit. A separately scoped and billed review of the workflow: interviews with the people who do the work, a look at the systems and data involved, and a list of where time and effort are being lost. It is common for AI and operations work but not mandatory for every project. A narrow, well-defined brief can go straight from the call to a proposal. See what happens in an AI readiness assessment.
- Proposal. Priorities, the proposed solution, scope, dependencies, milestones, cost and how success will be measured, agreed before anything is built.
- Development. The solution is built and tested against real business scenarios, with feedback from the people who will use it. Testing here means more than "it ran once." See how to test AI before production.
- Integration and training. The solution is connected to your live environment, your people are trained for their roles, and ownership of day-to-day running is agreed.
- Optional maintenance. Ongoing support and improvements under a separate agreement that states what is covered and how it is billed.
The audit and the build are different purchases. If a provider quotes one lump sum for an unscoped "AI transformation," ask what is in it. Starting small and measurable is nearly always safer; which processes to automate first helps you pick the first workflow.
Illustrative example: Consider a 60-person wholesale distributor where orders arrive as emailed purchase orders, in PDF and spreadsheet form. A coordinator reads each one, checks it against the price list in the ERP, and keys it in. An integration project would read incoming orders from the shared inbox, extract the fields, compare them with the ERP price list and customer record, and create a draft order. Anything that does not match, such as an unknown product code or a price outside tolerance, goes to the coordinator with the reason attached. The coordinator approves orders before they post. Which fields, which systems and which tolerances apply would all be set during the audit and proposal. This is a scenario, not a delivered project.
What do you need to provide?
An integration project depends on your side as much as the provider's. Weak preparation is one of the most common reasons these projects slip.
| You provide | Why it matters |
|---|---|
| A named business owner | Someone who can decide what "correct" means and who accepts exceptions. Without one, questions stall. |
| Access to systems | Admin or API credentials, permissions and, where needed, IT and security sign off. Slow access slows everything. |
| Real examples of the work | Last quarter's actual emails, invoices or tickets, including the messy ones. Clean samples hide the hard cases. |
| People who do the work | Time for interviews and testing. They know the exceptions that never made it into a process document. |
| A view on data and approvals | What the AI may read, where data may be processed, and which actions need a human to approve. |
| A measure of success | A baseline such as handling time per task or share of incomplete submissions, so improvement can be shown rather than asserted. |
If you cannot fill most of this table yet, a free AI readiness self-assessment can show where the gaps are before you spend on an audit.
What drives the cost of AI integration services?
This guide will not quote a number, because any figure given without seeing your systems would be a guess. What matters is knowing which choices move the cost, so you can compare proposals on what they cover.
- Number of workflows and teams. One workflow with one owner is a smaller project than three workflows across finance, service and operations.
- Number of systems, and how open they are. A modern CRM with a documented API is far easier to connect than an older ERP with no API, where the work may involve exports, database access or screen-level automation.
- Data quality. If the records the AI relies on are inconsistent, duplicated or scattered across inboxes and spreadsheets, cleaning them is real work and often the largest hidden cost.
- Level of autonomy and approval design. A workflow that drafts and a person approves is simpler to design and test than one that acts on its own across several systems.
- Security and governance requirements. Regulated or sensitive data, formal reviews against a framework such as the NIST AI Risk Management Framework, and data residency rules all add effort.
- Infrastructure and usage. AI models and hosting carry their own running costs, billed by usage. Ask whether they sit in your accounts or the provider's, and who pays.
- Training and rollout. Training for each role, a phased rollout and adoption support take time that a pure build quote leaves out.
- Ongoing support. Monitoring, updates and fixes after launch are a separate line, not an assumption inside the build price.
For the audit stage specifically, what drives the cost of an AI readiness audit breaks the same idea down further. Four cost areas should appear separately in any serious proposal: the audit, the implementation, infrastructure and usage, and optional ongoing support.
How can you tell a good provider from a weak one?
You do not need a long vetting questionnaire to see the difference. Most of the signal appears in how the first two or three conversations go. For a structured list to put to every firm, use how to choose an AI implementation partner. The signals below are about behavior you can observe.
Signs of a good provider
- They ask about the work before they mention a model or a vendor.
- They want to see real examples, including the awkward ones.
- They name what they would not automate, and say why.
- Their proposal lists the workflows, systems, interviewees and deliverables by name.
- They explain who approves what, and what happens when the AI is unsure.
- They are open about what they have not verified yet, such as whether your ERP exposes the data they need.
- They put data access, ownership and success measures in writing.
Signs of a weak provider
- The demo is impressive, but runs on their data, not a sample of yours.
- Every problem is answered with the same product or the same tool.
- The proposal talks about "transformation" but never names a workflow.
- They claim they integrate with everything, without checking your systems first.
- Results and savings are promised before anyone has seen your process.
- Nobody on their side can say who owns the code and accounts at the end.
If you plan to put the work out to several firms, a written brief makes the comparison fair. How to write an RFP for an AI implementation project covers what to include and how to score what comes back.
When do you not need an AI integration provider?
Hiring a provider is not always the right call. Skip it, or delay it, in these cases.
- A tool you already license has the feature. Many CRM, helpdesk and accounting products now include AI features that may be switched off. Try those first, in a small test, before paying for custom work.
- One simple connection will do. If the job is moving data between two systems with a standard connector, your own IT team or an administrator may handle it without AI at all.
- The process is not defined. Automating a workflow that nobody agrees on, or that changes every week, builds the confusion into software. Fix the process first.
- The data is not usable. If the records are incomplete or not accessible, a data project comes before an AI project.
- You have no owner or no access. If nobody can approve decisions or grant system access, the project will stall regardless of who you hire.
- You need a new application, not a connection. If the real need is a portal or internal tool, the answer is closer to software development. Build, buy or integrate helps you decide.
Even then, an hour with a provider can be useful to confirm you are right. A good one will tell you when you do not need them.
How does Kastling approach AI integration?
Kastling is an AI solutions and implementation partner for enterprises and growing businesses, often with 20 to 200 people. We start with the work, not the technology: we audit how a process runs today, connect the systems you already use rather than replace them, and build AI into a defined workflow with a named person in control of consequential decisions.
An engagement follows the six stages above: a free discovery call, a separately scoped paid audit where one is needed, a proposal, development tested against real scenarios, integration and training, and optional maintenance under a separate agreement. We agree data access and ownership up front, involve the people who will use the solution, and agree how success will be measured before building.
The team is founder-led, with specialists, including AWS and Azure, brought in as needed. For a view on whether your workflow is a fit, start with a discovery call, or read more about our AI integration and automation services.
Questions
Is AI integration the same as buying an AI tool?
No. Buying a tool gives you a product with its own screens, data and logins. Integration connects AI to the systems you already use, so the work happens where your team already does it. Many projects use a mix: a bought product for a standard job, plus integration to get its output into your CRM or ERP.
Will an integration provider need access to our live systems?
Usually they need some access, but not blanket access. A careful provider asks for the narrowest permissions that let the workflow run, often read access first, and tests against copies or a sandbox before touching live records. Agree who grants access, who can revoke it, and where the data is processed before work starts.
Who owns the integration when the project ends?
It should be written down before work starts. Ask who owns the code, the accounts the integration runs in, the documentation and any prompts or configuration. Your business should be able to hand the work to another team or take it in-house without asking permission.
Can integration work be done in stages?
Yes, and it usually should be. A first release that connects one workflow to one or two systems is easier to test and measure than a broad rollout. Later stages can add systems or workflows once the first one is in daily use and the team trusts it.