Start with a useful job to do
A good AI product needs a clear purpose. That might be helping a support team search approved documentation, extracting information from incoming documents, or turning a customer request into a structured next step. We identify the user, the source information, and what the product should do when it cannot find a reliable answer.
Agentic AI adds the ability to take connected actions through tools and integrations. We define which actions an agent may take, where a person must approve a step, and how the system hands work back when something needs judgment.
An example: from inquiry to follow-up
Imagine an inquiry arriving through a website or shared inbox. An agent could extract the request, check relevant customer information, suggest an owner, and prepare a follow-up. Sending the message or changing a business record can remain an approval step.
This is an illustrative workflow, not a claim about a deployed customer system. Its design would depend on the available integrations, data permissions, and how your team handles exceptions.
What we build with you
- A focused product scope, user journey, and success criteria.
- A working interface and connections to the agreed data sources or tools.
- Permissions, approval steps, and clear fallback behavior.
- Evaluation examples covering normal requests, unclear inputs, and failure cases.
- Deployment and handover details appropriate to the agreed scope.
We begin with representative tasks and a small working version. Together, we review answer quality, latency, cost, and usability before expanding the workflow. Testing continues as prompts, models, source material, or business rules change.
Choose the right amount of AI
Some tasks need language understanding; others are better served by straightforward rules. We look at both before choosing an approach. If the source information is incomplete or a required system has no suitable integration, we make that limitation part of the plan.
For broader operational changes, explore our business process automation service. If the experience belongs in customers’ pockets, we can connect the AI capability to a mobile application.
Common questions
Do we need our own AI model?
Not necessarily. The choice depends on the task, data requirements, expected usage, and operating constraints. We assess those before selecting models or deciding whether an existing model is sufficient.
Can an agent work with our existing tools?
Where suitable APIs or supported integrations are available. We review access, permissions, and system limits first, then define what the agent can read, change, or submit for approval.
How do we get started?
Email Tech Ed with the task you want to improve. A short description of the users, current process, and available data is enough to begin the conversation.