AI Business Agent — Intelligent Decision-Support SaaS

Overview

Project context

AI decision-support project combining RAG retrieval, LLM orchestration, simulation workflows, and web interfaces with FastAPI, PostgreSQL/pgvector, OpenSearch, MinIO, Redis, and Docker.

Problem

What needed to be solved

Business users needed traceable assistant responses across mixed structured and semi-structured data while maintaining deterministic retrieval and decision support transparency.

Impact

Delivery outcomes

• Shipped end-to-end architecture with backend orchestration and web delivery surfaces.
• Enabled scenario-oriented workflows for operational decision support experiments.
• Provided measurable traceability through structured response and retrieval boundaries.

Architecture

Execution flow

Step-by-step implementation path used to deliver this project.

1. Step 1

   FastAPI orchestration services

2. Step 2

   PostgreSQL + pgvector factual/semantic retrieval

3. Step 3

   OpenSearch and MinIO contextual storage

4. Step 4

   Simulation and routing workflows

5. Step 5

   Structured response composition UI

Features

Capabilities shipped

Media

Screenshots and recordings

AI Business Agent — Intelligent Decision-Support SaaS screenshot 1

AI Business Agent — Intelligent Decision-Support SaaS screenshot 1AI Business Agent — Intelligent Decision-Support SaaS screenshot 2

Limitations

Scope boundaries

• Prototype framing with advisory output posture.
• Evaluation outcomes are context-dependent and require workflow-specific validation.
• Operational decisions require human review.

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