Start with a task that matters.
A useful integration starts with an operational question: what should the user be able to do more easily? Model selection comes afterwards. We distinguish tasks that benefit from AI from those a rule or conventional automation can handle more simply.
- Find information in a set of authorized documents.
- Prepare a summary of a record or request.
- Classify a request and suggest a next action.
- Highlight a trend in a dashboard.
- Prepare a draft for review before use.
Defined sources and access.
AI should use information relevant to the task while respecting the user’s permissions. We assess where data lives, its quality, the access needed and the constraints of potential providers.
A plausible answer can be wrong. The design therefore includes evaluation criteria, situations where the system should express uncertainty and a way to trace sources when available.
Support a decision without hiding it.
| Define | Question to answer |
|---|---|
| Agent’s role | Which specific task should it handle? |
| Authorized sources | What information can it consult? |
| Permitted actions | Can it suggest, prepare or execute? |
| Approval | Which actions require human agreement? |
| Evaluation | How is quality assessed on representative cases? |
| Follow-up | How are errors detected and the system revised? |
Illustrative scenario
Turn data into a next action.
Data from a CRM, ERP and forms feeds a dashboard. An unusual change is highlighted. The assistant prepares a summary, presents supporting information and suggests actions. The team can then submit, change or reject the recommendation.
This scenario explains a possible integration. It is neither an analysis of client data nor a Bump agent already offered as a product.
An integration to evaluate in context.
Depending on the engagement, deliverables may include use-case scoping, source connections, an interface, approval rules, a set of test cases and documentation. A pilot helps establish usefulness and limitations before a wider rollout.
Cyber AI, Developer AI and Workflow AI are part of our future vision for specialized systems. They are not presented as available products. Our current services focus on analysis, development and integration within a defined engagement.
How do you check an agent against concrete cases?
For an assistant preparing follow-ups to customer requests, one successful run is not enough. Test situations that should trigger a stop, clarification or human recovery too. This starting checklist should be adapted to the engagement; these cases are illustrative and do not represent client results.
| Test case | Behaviour to check |
|---|---|
| Required information is missing | Ask for clarification instead of inventing data. |
| Two sources disagree | Present the conflict and sources for review. |
| A document is restricted for the user | Respect access rights without disclosing protected content. |
| An action needs approval | Prepare the draft and wait for an explicit decision. |
| The CRM is unavailable | Report the failure and allow recovery without duplicates. |
A little more clarity
Questions worth asking.
How is an integrated AI agent different from a chatbot?
A chatbot exchanges messages. An integration may also consult authorized sources, prepare information or interact with software. Its actual capabilities depend on connections, permissions and the agreed scope.
Can AI act without approval?
That depends on the action and the agreed framework. A recommendation can remain a draft until approved. Sensitive operations need explicit permissions and controls defined before implementation.
Do we need to replace our CRM or ERP?
Not necessarily. We assess integration options for existing tools first. API access, subscriptions and data quality determine what is feasible.
How do we know whether an AI integration is useful?
Compare its results on representative cases against defined criteria: quality, review time, errors, usage cost and usefulness to the team. A pilot supports that decision before a broader rollout.
How do you test an AI agent before launch?
Define representative cases, expected results and errors to detect. Check the sources used, access permissions, approval requests and situations in which the agent must stop. Bump agrees on these criteria with your team; a successful demonstration does not replace validation.