Product 

Embed Custom AI Agents into your product quickly and easily, without AI expertise or intense infrastructure setup.

From Idea to Production

Build Custom AI Agents to automate repetitive analysis, provide deep insights, and recommend ways to optimize your marketing campaigns based on data

Without Cimba

AI Feature Scoping

The product team scopes the AI feature they want to add to the platform.

LLM Selection

The team begins the process of selecting suitable LLMs based on performance metrics, compatibility with existing systems, and potential integration benefits.

Evaluation and Comparison

Further evaluation of shortlisted LLM candidates involves analyzing their capabilities and considering factors like language proficiency, response accuracy, and computational efficiency.

Prompt Engineering

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.

Iterative Refinement

Continued refinement of prompts involves iterative testing and adjustments to optimize conversational flow and enhance user engagement.

Prompt Finalization

Final adjustments and validations ensure that the prompts are finely tuned and aligned with user expectations and platform objectives.

Strategy Development

Strategic planning and development of prompt chaining methodologies aim to sequence interactions logically, maintaining context and coherence throughout user interactions.

Prompt Chaining Implementation

Execution of the planned prompt chaining strategy involves integrating sequential prompts seamlessly, ensuring smooth transitions and effective continuation of conversations.

Validation and Optimization

Validation through testing and optimization procedures refines prompt chaining techniques, addressing any usability issues and enhancing overall user experience.

RAG Engine

The team selects the vector database they are going to use, creates a RAG engine.

Solution Testing

The team tests this solution and refines it with reinforcement learning through human feedback.

Model Optimization

Upon testing, a decision is made to fine-tune the model for better performance. 

Agent Integration

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.

Feature Launch

The feature is released.

With Cimba

AI Feature Scoping

The product team scopes the AI feature they want to add to the platform.

Automated Model Selection

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's Integration

Cimba is connected to all of the required data sources and systems, including data warehouses, document stores, knowledge bases, and the product backend.

Automated Database

Cimba automatically creates a vector database and RAG engine based on the user input and desired use case.

Agent Training

The product team can test and train their custom Agent through the Cimba platform and provide context and reinforcement learning through the user interface.

Platform Integration

The product team integrates their custom agent into their platform using the Cimba API.

Action Approval

Cimba automatically creates a vector database and RAG engine based on the user input and desired use case.

Feature Launch

The feature is released.

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Answering a “not-so-simple” question

“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

Automate playbooks 
Use your operation playbooks to train the agents without knowledge of large language models, prompt engineering, or programming languages. Let agents help you solve complex business challenges
Example use cases
Solve complex business challenges with adaptive AI i.e. “Help me optimize my marketing campaign”, “Suggest appropriate segmentation to a specific campaign”, etc.
Get recommendation & trigger action
AI can recommend the next appropriate actions based on your insights and internal knowledge
Contact us today and let us know what you need
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