"Engineered an automated data-cleansing framework to audit, normalize, and restructure high-error transactional records. By resolving systemic data anomalies—including string irregularities, duplicate entity entries, schema mismatches, and null variances—this pipeline elevated raw, unreliable logs into clean, decision-ready assets primed for business intelligence modeling."

Multi-Model Social Media Agent — Automated Content Workflows

Built by QuantEve Technologies | AI Automation & Full-Stack Engineering

Designed and developed an AI-powered social media workflow that streamlines content research, drafting, evaluation, approval, publishing, and performance analysis.

Rather than relying on a single model for every task, the system uses a multi-model workflow to organize content operations into manageable stages, with persistent state and human review where needed.

Core Capabilities

Content Research: Organizes topics and research inputs into a structured content workflow.

AI-Assisted Drafting: Generates social media content for platforms such as X and LinkedIn.

Multi-Model Processing: Supports model-provider alternatives and fallback handling for more resilient workflows.

Draft Evaluation: Adds an evaluation stage to review and improve content before it moves forward.

Human Approval: Keeps content review separate from publishing so drafts can be checked before external actions.

Workflow Persistence: Uses SQLite-backed queues and stored state to track content as it moves through the system.

Performance Feedback: Organizes publishing analytics and feedback to inform future content decisions.

Engineering Focus

The system brings AI model orchestration, application logic, persistent workflow state, and social media operations together in one maintainable automation pipeline.

Technology Stack

Python / Node.js (depending on the deployed component) · LLM APIs · REST APIs · SQLite · Workflow Automation · Social Platform Integrations

Built by QuantEve Technologies to demonstrate how AI can automate repetitive content operations while keeping the workflow organized, reviewable, and controllable.

Keeping content review separate from publishing, with state in SQLite queues, is a sensible setup. When a model provider fails mid-run and the fallback kicks in, does the queue retry just that stage with the next model, or does the draft restart from research?