NimbleBrain approached TRooTech to architect an end-to-end AI platform—DeepAgent, that empowers users to launch sophisticated AI agents without any technical expertise. The vision was to abstract complex model building behind conversational AI, enabling clients to simply describe their needs in natural language and deploy fully functioning AI solutions within minutes.

TRooTech engineered a powerful, modular, and user-friendly platform that eliminates traditional AI implementation barriers, including cost, time, and technical dependency. DeepAgent leverages LLMs, no-code interaction, and data-native integration to make AI truly accessible.

Resources Utilized

Engagement Period

Tech Stack Deployed

About Client

Our Strategies in Action:

Unstructured Prompts Created Chaos:
Early-stage users struggled with ambiguity in describing their needs. This led to vague inputs that risked generating irrelevant AI agents. The lack of structure in user prompts made it difficult to ensure precision in agent behavior.

Inconsistencies Across Multiple LLMs:
The platform’s flexibility in supporting multiple LLMs introduced its own complexities. Each model behaved differently in response generation, requiring extra layers of normalization, fallback logic, and performance tuning to maintain consistency.

Integration with Legacy Systems Was Painful:
Several clients operated on outdated or closed enterprise systems, making real-time data syncing and AI agent embedding unexpectedly difficult. Building secure, bi-directional integrations added development overhead and complexity.

Scalability Concerns During Concurrent Requests:
As early adopters began testing the platform at scale, the backend showed signs of latency under concurrent agent generation loads. This exposed the need for early infrastructure optimization and asynchronous task handling.

Client Data Privacy Gaps Surfaced:
Working with client data—often confidential and unstructured—highlighted risks around compliance and access control. NimbleBrain had not initially planned for role-based restrictions, which delayed rollout to certain enterprise users.

Bottlenecks We Encountered:

Enabled natural language agent creation via a guided conversational interface

Integrated multi-LLM orchestration for adaptive model selection

Built secure data connectors for seamless integration with client systems

Developed a microservices-based backend for high scalability and performance

Introduced a real-time agent tuning panel for non-technical users

Ensured API-first architecture to support broad tool interoperability

Embedded clear pricing visibility within the platform interface

Solutions Implemented By Us

Code-free AI agent builder powered by conversational AI

Support for OpenAI, Anthropic, and custom LLMs

Dynamic integration with CRM, ERP, and document repositories

AI-driven reporting with exportable summaries and visual insights

DALLE-3 powered custom image generation based on text prompts

User-controlled tuning for agent tone, logic, and scope

Transparent pricing engine with no hidden costs

Core Features and Functionalities:

 Users:

If you're ready to deploy custom agents, accelerate outcomes, and streamline operations without technical friction, TRooTech can make it happen.

AI doesn’t need to be complex—DeepAgent makes intelligence accessible in minutes.

Cross-Industry

AI Agent Platform

Python

OpenAI

Langchain

Anthropic LLMs

Pinecone Vector Database