How we build custom AI agents
1. AI Agent consultation & discovery
We start by understanding your business challenges and defining the tasks the AI agent will perform.
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This process includes:
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Assessing current workflows and identifying automation opportunities
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Engaging stakeholders to capture business expectations
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Ensuring technical feasibility within your existing ecosystem
2. Use case definition & architecture design
We define the specific use cases the AI Agent will handle and design the system architecture.
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This stage focuses on:
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Finalizing use cases based on business impact and complexity
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Prioritizing tasks and defining success metrics
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Designing a scalable and adaptable architecture for future growth
3. Data collection & preparation
We gather and prepare the relevant data to ensure the AI agent performs effectively in your specific domain.
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This process covers:
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Collecting structured and unstructured data from internal and external sources
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Cleaning, anonymizing, and ensuring the privacy of sensitive data
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Preprocessing the data to align with model training needs
4. Model selection & fine-tuning
We carefully choose the best model based on your industry and fine-tune it using your proprietary data. For highly specialized tasks, we can train SLMs (Small Language Models)
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Our approach includes:
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Choosing the optimal LLM or SLM model based on your specific needs
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Fine-tuning the model with domain-specific data to ensure maximum accuracy
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Optimizing the model for real-time, task-specific performance
5. AI Agent development & testing
We develop the AI Agent’s interface and backend components to ensure interaction with your internal systems. We test its performance through iterative trials.
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The development phase involves:
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Building both the backend systems and user-facing components (e.g., chat interfaces)
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Integrating the AI agent with your existing software infrastructure
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Testing the agent’s functionality and accuracy in a controlled environment
We implement security measures to ensure data protection throughout the AI agent’s lifecycle. We also embed compliance with regulations like GDPR and HIPAA, ensuring the AI Agent behaves ethically and within legal frameworks.
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This step covers:
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Encrypting sensitive data and securing API interactions
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Ensuring compliance with industry regulations like GDPR and HIPAA
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Installing moderation filters and setting ethical guardrails to prevent biased or harmful behavior
7. AI Agent deployment & optimization
We deploy AI Agent in a self-hosted environment, ensuring full control over your data and infrastructure. Then, we monitor its performance, making improvements as needed.
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The deployment phase includes:
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Rolling out the AI Agent in a pilot environment for initial feedback
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Deploying fully across all your business units
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Monitoring in real time and optimizing the model to adapt to business changes
8. Post deployment support
We provide ongoing support and maintenance to ensure your AI agent evolves with your business and continues to perform at its best.
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Post-deployment support includes:
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Monitoring system uptime and fixing bugs as they arise
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Offering regular updates to add new features or improve performance
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Retraining the model as your business needs change or new data becomes available
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