MyExec: How Appinventiv Built an AI
Consultant for Data-Driven
Decision Intelligence

The cost of developing a business consultant bot like MyExec varies based on features, LLM integrations, document handling needs, and personalization layers. Typically, a platform like this ranges between $50,000 to $300,000. For a tailored estimate, get in touch with our experts.

Also Read: How Much does It Cost to Build an AI Product?

A minimum viable version could be developed in 5 to 7 months, while a fully featured, multi-agent version with advanced orchestration may take 9 to 14 months, depending on complexity. Reach out for a detailed project roadmap.

A standard chatbot typically answers from a pre-defined script or a single knowledge base. A Multi-Agent RAG system is far more dynamic. "RAG" allows it to retrieve factual data from specific documents to ensure answers are accurate. The "Multi-Agent" part means different specialized AIs work together, like a team of experts, to analyze a problem from multiple angles before providing a comprehensive, well-reasoned answer.

Also Read: Unveiling Practical Applications of RAG in AI Development

This is achieved through the Personalization Engine and the RAG pipeline. The RAG system grounds the AI's responses in the user's own business data, making the insights highly specific. The Personalization Engine then adapts the strategic frameworks and recommendations based on the user's stated goals, industry, and business size, ensuring the advice is always relevant and context-aware.

Security is paramount. All data, especially user-uploaded documents, is handled within a secure, isolated environment. We implement strict data access controls and encryption protocols. The RAG system is designed to only use the data to formulate a response for that specific user session, and it is not used for training general models, ensuring complete data privacy and confidentiality.

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A scalable architecture means the system is designed to handle growth without a decline in performance. Our modular, agent-based design allows for this in two ways: we can add more computational resources to handle more users, and we can easily add new "agents" or capabilities (e.g., a new agent for marketing analysis) without having to rebuild the entire system.

Building MyExec required a collaborative, iterative approach designed to balance technical innovation with real-world usability. Here's how we approached each phase:

Discovery & Ideation: We began by conducting detailed workshops with the MyExec founders to understand their vision, map key user personas, and identify core pain points SMBs face in business decision-making. This helped define the AI assistant’s core objectives—document interpretation, strategic guidance, and real-time insights.

Design & Prototyping: Our UX team created a simple, chat-first interface built around natural language interactions. We mapped out the conversational flow, defined document upload and response triggers, and built wireframes that reflected ease-of-use for non-technical users. The design was continuously refined based on feedback from early user demos.

AI Development: We built a modular backend powered by RAG pipelines and the ReACT framework. This included training a multi-agent system using GPT-4o to parse business documents and route relevant queries through a context-aware reasoning layer. We ensured each AI response was not just accurate, but contextually personalized to the business.

[Also Read: How to Build an AI App?]

Integration & Testing: We integrated FastAPI for scalable endpoints, MongoDB for memory and session persistence, and LangSmith for observability and prompt monitoring. The platform was deployed using Docker and Amazon ECS, enabling autoscaling and robust performance. Rigorous testing cycles were conducted to validate LLM responses, context retention, and session accuracy.

Post-Launch Optimization: After go-live, we continued monitoring user interactions and model performance. We rolled out personalization enhancements, added fallback logic for ambiguous queries, and fine-tuned prompt engineering to improve business-specific output quality. Feature extensions were prioritized based on live user feedback and performance metrics.

Getting started with Appinventiv is simple and collaborative. Here’s how the onboarding process works:

• Contact us to schedule an initial consultation: We begin with a discussion to understand your idea and vision.
• Share your requirements and use cases: Our team will gather all relevant details to define the project scope.
• Receive a tailored proposal with approach, cost, and timeline: Based on the discussion, we create a proposal that aligns with your business goals.
• Once agreed, we assign a dedicated team and project manager to begin development: Your project is executed with full transparency, regular updates, and end-to-end collaboration.