The Result
BridgewingTech, an innovative legal-tech and business-intelligence startup, partnered with our team to develop a cloud-native AI platform that automates the full data lifecycle for litigation-risk assessment, document classification, and business analytics.
The result is a modular, secure, and AI-driven system that streamlines data ingestion, cleansing, and AI evaluation, helping legal teams and enterprise clients transform unstructured data into actionable insights.
The platform provides transparent AI monitoring, robust data governance, and seamless integration with external services, enabling BridgewingTech to deliver high-value intelligence for law firms, corporate legal departments, and compliance teams.
The Challenge
BridgewingTech needed a scalable, auditable, and AI-enhanced solution capable of processing diverse legal and business data at scale.
The key challenges were:
- Complex Data Sources: Legal documents, contracts, and structured business records across multiple repositories and APIs.
- AI Transparency: Need to configure, test, and monitor AI models used for document classification and risk scoring with full traceability.
- Data Governance: Database versioning, schema evolution, and reproducibility across environments.
- Integrations: Real-time connections with Google Cloud services (Gmail, Drive, BigQuery, Pub/Sub), external APIs, and internal document stores.
- Automation: Continuous pipelines for ingestion, cleansing, and enrichment.
- Compliance & Security: Isolated execution, strict access control, and audit logging.
The Solution
We designed and built a Flask-based AI data management platform supporting end-to-end automation, observability, and integration:
Core Components:
- Data Pipeline Engine – Ingests, deduplicates, and normalizes documents from cloud and enterprise sources.
- Schema Versioning – Managed with Alembic, enabling controlled database evolution and rollback.
- AI Model Hub – Configures and monitors AI models via Langfuse, allowing traceable experimentation, evaluation, and debugging.
- Integration Layer – Syncs with Google Drive, Gmail, and BigQuery for seamless data exchange.
- API Gateway – Central entry point for internal tools and partner integrations.
- Containerized Infrastructure – Deployed via Docker and Docker Compose for reproducibility and scalability.
Each service runs in an isolated container with configurable access levels and secure tokens, ensuring compliance and multi-tenant security.
Technology Stack
- Backend: Python 3.11, Flask
- Database: PostgreSQL 12+, Alembic migrations
- AI Observability: Langfuse
- Containerization: Docker, Docker Compose
- Orchestration: Uvicorn / Gunicorn
- Integrations: Google Cloud (Drive, Gmail, BigQuery, Pub/Sub)
- Deployment: Shell scripts for CI/CD automation
Key Benefits
✅ End-to-End Automation — From ingestion to AI-based classification and analytics
🔍 Full AI Traceability — Transparent monitoring of model behavior through Langfuse
🧱 Reproducible Architecture — Versioned database migrations with Alembic
☁️ Cloud-Native Scalability — Containerized deployment for fast iteration and scaling
🔗 Seamless Integrations — Real-time sync with Google ecosystem and external data sources
🔐 Enterprise-Grade Security — Isolated environments and access controls
Outcome
With the new platform, BridgewingTech can onboard new data sources quickly, run experiments with multiple AI models, and provide clients with reliable, explainable insights.
The startup is now positioned to scale its litigation-risk intelligence services globally, delivering faster analysis, better compliance, and data-driven decision-making for legal professionals.
About Mellivora Software
Mellivora Software helps with Staff Augmentation for Enterprises & SMEs. Our core expertise is focused around:
- Big Data and Data Management
- NLP/Machine Learning technologies
- DevOps and Cloud technologies