Pipeline Multi-Agent
Agentic AI for document generation
Seedium was responsible for building a multi-agent system that enables users to generate business documentation 2–5× faster compared to traditional manual processes.
Project:
AI Agent
Industry:
Business collaboration, SaaS
Client:
USA startup (NDA)
Services:
AI development
Cooperation Model:
End-to-end development
Timeframe:
Ongoing project
Product overview
The product is an intelligent document-generation system that uses specialized AI agents to create business documents from user input. It streamlines the creation of product, strategy, and financial documentation, reducing time spent on manual work while sharing context and iterative refinement.
The story behind
The client approached Seedium with a proof of concept and the objective of transforming it into a scalable, production-ready multi-agent system. As a solo founder, they required full-stack expertise to design scalable architecture, implement robust agent orchestration, and establish reliable validation and iteration workflows.
Work in numbers
Seedium solutions
Seedium was responsible for turning an early prototype into a secure, maintainable system, including backend infrastructure, integrations, and deployment pipelines.
We introduced a system of role-based AI agents, supported by smart task routing that ensures each task is handled by the most appropriate agent. Each agent operates independently but within a shared execution framework, enabling consistent coordination across the system.
- Instead of generating information from scratch, the system evolves a shared knowledge base.
- Generated documents are stored, vectorized, and reused, while agents retrieve only relevant context before execution using a RAG-based approach.
- The memory is continuously updated after validation, ensuring that knowledge improves over time.
Each agent incorporates a reflection step, enabling it to assess and refine the quality of its output, identify missing or weak parts, and feed improvements back into the execution loop for iterative refinement.
Every agent output passes through a strict control loop designed to ensure reliability and consistency across the system:
- An agent generates output based on the assigned task and the retrieved context
- The system validates the result against predefined rules and structured schemas
- A dedicated review agent evaluates and improves the output, correcting errors, inconsistencies, or missing elements
- If validation fails, the system triggers up to 2 retry attempts with exponential backoff, allowing the agent to refine its response using updated context and feedback
This layered process ensures quality control at every step, reducing hallucinations and enforcing structured outputs across all agents.
The system integrates with collaborative tools such as Discord and GitHub to enable seamless document management within existing workflows. Users can upload, edit, and update documents directly through shared channels, keeping collaboration lightweight and accessible.
All changes are synchronized in real time, ensuring that the latest version of each document is always available across the system.
Project highlights
Features Implemented
Multi-agent AI system with role-based architecture
Document generation pipeline
Secure storage and versioning in GCG
Agent reflection mechanism
Structured validation using rules and schemas
Dedicated review agent for output refinement
Integration with Discord and GitHub
Back-end infrastructure setup
Tech Stack
Next.js
Express
TypeScript
PostgreSQL
Google Cloud
OpenAI
The outcomes and recognition
The multi-agent system was successfully built and deployed within the planned deadlines. It demonstrated a >90% task completion rate after validation and retry mechanisms, with the potential to accelerate document generation by 2–5× compared to manual processes.
The system is now in the final phase before launch, undergoing last-stage testing and approval to ensure production readiness.
“In this project, the client’s domain knowledge combined with Seedium’s AI expertise enabled the creation of a reliable solution that addresses one of the most time-consuming challenges faced by business leaders. This shows the real potential of AI agents to reduce costs and increase ROI for both enterprises and SMBs.”
Dmytro Machulianskyi
Engineer working on the project
Mariana Dzhus
Business Development Manager
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