Make the connection fit the task
An AI integration can be a behind-the-scenes step in a process, a search box over approved documents, or an assistant inside a product your team already uses. The hard part is often not calling an AI service. It is deciding which information to send, how to identify the right user, how to handle a poor response, and what should happen if a connected tool is unavailable.
We inspect the current workflow and the documented access options for the software involved. If a service cannot provide the needed data or permissions, we explain that constraint before treating it as a build task. We also consider whether a simple automation, a built-in feature, or a custom connection is the more sensible answer.
Three integration patterns
The right pattern depends on the source systems and the decisions your team wants to improve. Each example is an illustrative design, not a client case study.
- Enquiry context: summarize a request and its previous interactions inside a team’s existing review process, with a link back to the source record.
- Document search: let staff ask questions about an approved set of internal guides and receive an answer that points to the material it used.
- Structured extraction: read information from an incoming document, map it to fields in a business system, and pause for a person to confirm uncertain values.
What we deliver
A project can include a short integration design, the connection between selected services, and the interface or workflow that makes the AI result useful. We agree what information flows in each direction and how a user or system is allowed to trigger the feature. The build can also include checks for incomplete responses, a human review step, and notes on provider limits and operating costs.
- A map of the systems, information, and permissions the feature depends on.
- A working connection using the access methods supported by those systems.
- A clear way to review, accept, or correct AI-generated output.
- Setup notes, test examples, and a handover describing ongoing ownership.
Agree on data access before the build
You identify the system owner, explain what information may be used, and arrange access using the providers’ approved authorization flow. We do not need passwords in a brief. Your team also decides which outputs may be saved, which must remain temporary, and whether any category of information is off limits for the chosen service.
We confirm the design with representative examples and build the integration in reviewable pieces. Your team checks that the feature uses the right source and returns information in a form that fits the work. A person can approve extracted data or a generated message before it is stored or shared. At handover, we explain the connections, the settings your team controls, and the failure states that need attention.
What shapes cost and timing
Integration work is shaped by the number and condition of the systems involved, the authentication and permissions they support, and the quality of the data they expose. Existing APIs, provider limits, custom interfaces, document formats, and the need to preserve a history of decisions can change the design. Building a review experience also takes more work than returning a result to an existing screen or notification.
We review these dependencies before agreeing on scope. Provider fees and model usage are separate considerations, and account owners may need to approve access or enable a feature. The estimate reflects the connection, testing, and handover that are actually required; we do not use a generic price or timeline without seeing those details.
Questions about this service
Can you connect AI to the software we already use?
Often, if that software provides a suitable way to access the needed information and your account permits it. We check the provider’s available interfaces and permissions during discovery.
Do we need to replace our current tools?
Usually the goal is to make an existing task work better with the tools you already have. If a tool blocks the required connection, we explain the options before proposing a change.
Where does our data go?
That depends on the chosen model and the connected services. Before building, we map the information flow and discuss provider settings, account terms, and your organization’s data rules.
Can a person approve the AI result?
Yes. We can put review into the flow so someone checks a draft or extracted value before it is sent, stored, or used for a consequential decision.