Embed Custom AI Agents into your product quickly and easily, without AI expertise or intense infrastructure setup.
From Idea to Production
The product team scopes the AI feature they want to add to the platform.
The team begins the process of selecting suitable LLMs based on performance metrics, compatibility with existing systems, and potential integration benefits.
Further evaluation of shortlisted LLM candidates involves analyzing their capabilities and considering factors like language proficiency, response accuracy, and computational efficiency.
Substantial effort is dedicated to designing and refining prompts that effectively elicit desired responses from the selected LLM, focusing on clarity, relevance, and user interaction flow.
Continued refinement of prompts involves iterative testing and adjustments to optimize conversational flow and enhance user engagement.
Final adjustments and validations ensure that the prompts are finely tuned and aligned with user expectations and platform objectives.
Strategic planning and development of prompt chaining methodologies aim to sequence interactions logically, maintaining context and coherence throughout user interactions.
Execution of the planned prompt chaining strategy involves integrating sequential prompts seamlessly, ensuring smooth transitions and effective continuation of conversations.
Validation through testing and optimization procedures refines prompt chaining techniques, addressing any usability issues and enhancing overall user experience.
The team selects the vector database they are going to use, creates a RAG engine.
The team tests this solution and refines it with reinforcement learning through human feedback.
Upon testing, a decision is made to fine-tune the model for better performance.
Now that the Agent is production-ready, the team integrates it with other tools and creates a UI within the platform for this new AI Agent.
The feature is released.
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The product team scopes the AI feature they want to add to the platform.
There is no need to select an LLM, Cimbas proprietary MoE (mixture of experts) pipeline will select the right model depending on the task, allowing for easy multi-LLM agent creation.
Cimba is connected to all of the required data sources and systems, including data warehouses, document stores, knowledge bases, and the product backend.
Cimba automatically creates a vector database and RAG engine based on the user input and desired use case.
The product team can test and train their custom Agent through the Cimba platform and provide context and reinforcement learning through the user interface.
The product team integrates their custom agent into their platform using the Cimba API.
Cimba automatically creates a vector database and RAG engine based on the user input and desired use case.
The feature is released.
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“How do I optimize my Marketing Spend?’’ Without Cimba AI
Campaign Manager Logs Into Dashboard Navigates multiple dashboards, viewing different insights to create a mental picture of how the campaigns are doing Sees something that is interesting, sends a slack message to a data analyst about it Data Analyst creates a ticket in Jira Data Analyst creates a ticket in Jira Data analyst manually writes SQL or Python code, returns answer (code disappears into the ether forever)Campaign Manager, seeing this, has another questionCycle repeats
Now that the campaign manager has all of the information, they plan to move budget from campaign A on Facebook, to campaign C on TikTok. Campaign manager opens facebook ad manager, navigates the UI, and adjusts the budget accordingly. Campaign manager opens TikTok ads, and adjusts the budget accordingly executes the appropriate task to make the change.
Metrics:
Time Spent: 11 Days
People Involved: 2
UIs Navigated: 6
“How do I optimize my Marketing Spend?’’ With Cimba AI
Campaign Manager Logs into CimbaAsks Cimba “how can I optimize my marketing spend?”Cimba automatically kicks off a workflow to anaylze key metrics from all advertising spend sources. Cimba returns these metrics to the user with visualizations and highlights. Cimba automatically summarizes its finding into a simple, easy to digest reportsCimba recommends actions to the campaign manager “you should move budget from Campaign A on Facebook to Campaign C on TikTok”.Campaign Manager asks Cimba “how would that impact my ROI, CPC, CTR, etc.”Cimba creates projections on these metrics and returns them to the campaign managerCampaign manager approves this action, and Cimba
Metrics:
Time Spent: 15 minutes
People Involved: 1
UIs Navigated: 1
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