AI inside real workflows
Develop capabilities such as document extraction, search, summarization, and assisted drafting where they help users complete a defined task.
AI and automation
Ventive develops AI features and workflow automation inside custom software, connecting your business data and systems with defined permissions, evaluation criteria, and human review where the work requires it.
AI featuresWorkflow automationBusiness data
Custom software. Connected systems.
Make AI useful in the software your team actually uses.
Automation across applications
Connect triggers, business rules, and system actions to reduce repetitive handoffs. Use conventional automation where predictable rules are enough.
Controls around data and decisions
Design permissions, evaluation examples, review steps, and failure handling around the information involved and the consequences of an incorrect output.
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01
Select a specific use case
Define the task, current effort, available data, acceptable output, and decisions that need a person. Check whether AI is useful for the problem.
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02
Prototype and evaluate
Build a focused proof of concept using representative inputs. Compare its behavior against agreed examples, constraints, and operating costs.
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03
Integrate with oversight
Connect the approved approach to your application, implement access and review controls, and establish how performance and exceptions will be monitored.
Define success together
What the engagement can include.
We agree on scope, priorities, and acceptance criteria around your software and the people who depend on it.
- A defined use case with data requirements and evaluation criteria.
- A prototype or integrated feature for the agreed workflow.
- Documented controls, review steps, and ongoing evaluation needs.
Frequently asked questions
Do we need AI, or would ordinary automation work?
The answer depends on the task. Predictable rules and structured inputs often suit conventional automation. AI may help with language, documents, or less structured information, but its output needs appropriate evaluation and controls.
Can an AI feature use our existing business data?
Potentially, after reviewing access permissions, data quality, sensitivity, and the selected provider or deployment approach. The design should specify which information the feature can access and how that information is handled.
How do you handle incorrect AI outputs?
The workflow should account for uncertainty through representative evaluation, validation, review, and fallback behavior. Higher-impact actions can require a person to approve the result before it changes a business system.
AI and automation
Which task would you like to improve?
Describe the workflow, available data, and where people need to stay involved.
We will review your context and follow up to discuss the scope, constraints, and a practical next step.
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