Data and useful actions
Choose the records the assistant can read, the tools it can call and the outcome each workflow should deliver.
AI integration services for SaaS products
An assistant inside
the product you
already built.
Your customers have a task to finish. Connect an AI assistant to the data and actions that help them finish it, without sending them out of your app.
COGNI.BUILD helps founders and product teams scope and implement that first useful workflow.
Illustrative workflow · a project management product
From a question
to a reviewed action.
“Which projects need attention before Friday?”
The assistant helps the user move from finding the problem to preparing the next step. Your product remains the place where the work happens.
Read the right data
Retrieve deadlines, project status and relevant notes within the signed-in user’s access.
Show the evidence
Explain which projects need attention, link to the source records and identify missing information.
Prepare an action
Draft updates or proposed assignments using the product’s existing tools.
Ask before committing
Show the proposed changes and require approval before writing to the product.
Scope the whole feature.
A generative AI integration involves more than a model call. We work through the boundaries that make the assistant useful in your product.
Identity and permissions
Define user and tenant boundaries, limited tool access, approval requirements and behavior when access is denied.
Evaluation and recovery
Define representative tasks, check answers and actions, and plan for missing context, errors and handoff to a person.
Deployment and ownership
Agree on monitoring, operating costs, maintenance responsibilities and how changes will be evaluated after launch.
Start with one workflow worth building.
Bring an existing product, a recurring task and a view of the data or tools involved. We can use those to discuss a bounded first implementation and the work it depends on.
Still deciding whether to build?
Compare a packaged assistant, a custom integration and a hybrid approach in our build-versus-buy decision guide.
Inspect the retrieval and evaluation decisions.
Our RAG example walkthrough uses synthetic labs to show how source access and evaluation affect an assistant. Explore the behavior and the production work it leaves open.
Questions before an integration.
Can this work with our existing software?
Start by reviewing your application architecture, authentication and available APIs. The first scope should identify the integration points and any changes your team must make. Compatibility depends on those details.
What data would the assistant see?
That is a design decision to make explicitly. List the approved sources, user permissions, tenant boundaries and information that must stay out of model requests. Review provider and retention requirements before implementation.
How much will it cost?
Separate implementation from monthly operation. Integrations, interface work, permissions and evaluation affect implementation; usage, model calls and supporting infrastructure affect ongoing costs. The worked cost example shows how those assumptions add up. It is not a project quote.
What would you need from our team?
A product walkthrough, one candidate workflow, an engineering contact and representative examples that are appropriate to share. Scope access to development environments and data before granting it.
Who maintains it after launch?
Decide ownership during scoping: deployment, monitoring, model changes, evaluation, support and incident response. The engagement should spell out which responsibilities stay with your team and which are included.
Show us the task.
Let’s scope the assistant.
Tell us what your product does, where users get stuck and when you want to address it. We’ll use those details to start a useful conversation about scope.
Discuss an assistant for your product