AI Integration Services for the Systems You Already Use
AI is more useful when it can work with the information your team already has. We connect a defined AI task to your business systems, with checks on what it reads, produces, and changes.
For teams that need AI inside a CRM, ERP, support desk, document flow, or internal application.
A useful result needs a place to go
A standalone AI tool can help with a single prompt. An integration has to work with real records, permissions, and the steps your team follows after the result appears.
AI sits outside the work
Someone copies a customer message into a separate tool, then pastes the answer back into the support desk. The useful step never becomes part of the team's process.
The right data is hard to reach
A model needs a contract, product record, or account history to help with a task. That information lives in systems with different access rules.
Outputs stop at a draft
AI can classify a request or extract a field, but a person still has to move the result into the CRM or ERP. That handoff needs validation and ownership.
A demo has no operating plan
The prototype works on clean inputs. Real use brings missing fields, timeouts, changing APIs, and decisions that need a human reviewer.
Our article on the cost of a bad integration explains why these details matter early.
Specific jobs for connected AI
These are examples of work we can scope. They are not client case studies, and the right design depends on your systems and data.
Support request triage
Use approved ticket context to suggest a category and draft a response. A team member reviews the result before it reaches the customer.
Support desk + knowledge base
Document intake
Extract fields from incoming documents, check them against required values, and queue uncertain items for review before any system update.
Documents + internal records
CRM record assistance
Summarize a lead enquiry and propose the next action inside the CRM. Keep the source message available so the owner can check the suggestion.
Inbox + CRM
From document to approved record
This is an illustrative document intake flow, not a record of a client engagement. The review step and destination would be set for your process.
- 01 / 05
Receive
A document arrives in an approved intake folder.
- 02 / 05
Extract
An AI step reads the fields needed for the task.
- 03 / 05
Check
Rules flag missing values or a mismatch with existing records.
- 04 / 05
Review
A person confirms any item that needs judgment.
- 05 / 05
Write back
Approved values reach the destination system and the action is logged.
An integration your team can operate
The scope sets the deliverables. We make the data flow and review decisions explicit so the result can be tested before launch.
A scoped integration design
We identify the data source, the AI step, the destination, access needs, and the point where a person must review the result.
Connections that fit your stack
We use available APIs, webhooks, and supported platform features. Feasibility is confirmed against your tools before it enters the build scope.
Validation and failure paths
Outputs are checked before use. Missing data, failed requests, and uncertain results have a defined route instead of a silent failure.
Testing and handover
We test with agreed examples and edge cases, document the integration, and explain how your team can review or maintain it.
Decide what AI can access and change
Permissions and human review belong in the design, before the system touches live records.
A boundary for every action
We document the data an AI step needs, the output it may produce, and the actions it may take. A draft reply, a suggested CRM update, and an approved change to a financial record have different review needs.
The scope also covers invalid inputs, failed connections, and how your team can trace an important action. Read more about data security when an AI agent touches your systems.
- Which records can the integration read?
- What can it change without approval?
- Who handles an uncertain result or failed run?
A path from use case to live system
- 01 / 04
Map the task
We review the current process, systems, data, and decision that AI would help with.
- 02 / 04
Agree on scope
We define permitted actions, review points, acceptance criteria, and the systems we can access.
- 03 / 04
Build and test
We connect the agreed components and test normal inputs, errors, and uncertain outputs.
- 04 / 04
Launch and hand over
We deploy the approved integration with documentation and an agreed plan for support.
Start with one defined integration
$799
Starting price in USD
We quote the systems, not a label
A focused AI step with straightforward access may be a small engagement. Multi-system work, sensitive data, complex permissions, and production support need a broader scope. We review the systems and requirements before giving you a written price and timeline.
Model usage, software subscriptions, hosting, and maintenance may add costs. We identify the likely ongoing charges during scoping.
Before we build
Can you add AI to our existing CRM or ERP?
Possibly. It depends on the product, your account permissions, available APIs, and what the AI needs to read or change. We check those details during scoping before promising a specific integration.
Do we need to replace our current software?
Usually not. The aim is to place a useful AI step inside the systems and processes your team already uses. If a tool does not offer a suitable connection, we will explain the limitation and the available options.
Who reviews AI output before it changes a record?
That is set in the project scope. A draft can go straight to a person. A low-risk field may pass an agreed validation rule. Actions with a meaningful cost or customer impact can require explicit approval.
How do you limit access to business data?
We identify the fields and actions needed for the task, then request access within boundaries your team approves. The design includes a way to trace important actions and handle results that cannot be trusted.
Can you process invoices or other documents?
Document extraction can be part of an integration when the format, volume, and review process are understood. We test against representative documents and define what happens when a field is missing or unclear.
What happens after the integration launches?
Your team receives the agreed documentation and handover. Eligible projects include 30 days of post-launch bug-fix support for defects within the agreed delivered scope. New features, integrations, maintenance, and scope changes are quoted separately.
Tell us where the AI needs to work
Describe the task, the systems involved, and what should happen with the result. We will assess a practical first scope.
If the main task is moving work between tools under clear rules, see our workflow automation services. If you are weighing a project against manual effort, try the manual work cost calculator.