Flexible. Customizable. Scalable
Custom DK AI Agents: Trained Solutions for Smarter Operations
Scalable to Meet Your Unique Business Needs
Build Your Own Intelligent Taskmaster
Custom DK AI Agent Development
A bespoke DK AI agent built to suit your growing business needs is a game-changer. From figuring out your company’s objectives to deploying a scalable solution, our organized workflow ensures precision, improves operational efficiency and customer satisfaction. Regardless of whether it is automating tasks or managing data, our AI-driven solution imparts a seamless, intuitive experience for today and the future.
How Do We Create a Custom DK AI Agent For You?
Workflow for Developing a Custom AI agent for a Client
1
Initial Consultation
- Collating Data: To understand your specific business needs and goals.
- Identifying Key Issues: Discuss the issues that can be addressed through automation (e.g. data Scraping, data deduplication, data management).
- Defining AI Agent Scope: Define the type of functionality and/or capabilities the AI agent requires. (e.g. workbots, task automation, data processing).
2
Set Goals & Objectives
- Establish KPIs: Set measurable goals for the AI agent (e.g. reduce internal work, task time management, cost management).
- Recognize Target Users: Understand the potential users of the AI agent (e.g. customers, employees, partners).
- Choose Key Integrations: Select the precise systems (CRM, ERP, internal apps, etc.) that require integration.
3
Design & Groundwork
- Task Map: Develop a flowchart of the customer or employee experience, with the AI agent’s steps in case of different scenarios.
- Customization Features: Finalize on the features, such as browsing, data capture, and so on.
4
Prototype Build
- AI Agent Framework Development: Build the architectural framework of the AI agent according to the requirements and relevant features.
- Prototype Development: Develop a beta version of the AI agent to perform an initial internal test.
- System Integration: Integrate the systems using third-party (CRM, database, APIs, etc.).
5
Client Assessment and Feedback
- Prototype Presentation: Conduct a demo for the client and a prototype evaluation.
- Collect Feedback: Gather feedback on the user experience, performance and other areas, which can be used for improvement.
- Modifications: Based on the feedback, make necessary improvements (e.g. adjust the flow, adapt according to the responses and add/delete features).
6
Testing & QA
- Internal Testing: Conduct a round of internal testing for integration stability, functionality and performance.
- User Acceptance Testing (UAT): Let the client or the team test the AI agent to find any bugs, errors, or potential issues.
- Fix Issues: If any issues are identified, it will be fixed during this phase.
7
Deployment
- Deployment: The product will be deployed into the application, website or other environment as finalized with the client.
- Track Performance: Monitor the performance of the AI agent against predefined KPIs.
- Necessary Fine-tunings: Real-time adjustments are made, based on performance expectations and feedback.
8
Continued Support and Optimization
- Regular Updates: In accordance with the Service Level Agreement, updates will be incorporated into the AI agent for new features or improved performance.
- Monitor User Engagement: Depending on the SLA, performance will be tracked and inputs from users will be utilized to improve AI agent responses and workflows.
- Customer Support: We will provide support after the launch, for troubleshooting and/or fixing any issues.
9
Scalability and Growth
- Remodel for Business: Enhance performance and functionalities according to business growth. Build new features or use cases based on evolving business needs (e.g. adding multilingual support or expanding to new platforms).
- Management of Efficiency: Work continually to improve the workflow of the AI agent, to improve customer satisfaction and enhance operational efficiency.