Document & Data Pipelines
Case Study Summary
Client: 8-figure SaaS company Industry: Technology / SaaS
Impact Metrics:
- $8.4M+ in time-sensitive revenue opportunities unlocked
- Decision-making went from days to near-instant
- 10,000+ contracts and invoices processed per month
- 6 core data sources centralized into BigQuery
8-figure SaaS unlocked $8.4M+ in time-sensitive revenue opportunities by giving teams real-time access to data — enabling faster decisions and negotiations powered by live usage insights.
Challenge
Teams were making decisions based on stale, week-old data. Any new analytical view needed up to a month to setup, and urgent requests relied on individuals being available to respond at speed. It was a costly, chaotic setup for a company that ran asynchronously.
Our Approach
We built a centralized data infrastructure connecting 6 core data sources into BigQuery, with additional sources connected via MCP. On top of this we layered a self-service analytics platform that pushes live metrics directly into Slack and Google Sheets where the team already worked. On the document side, we built an ingestion pipeline handling contracts and invoices at scale, using OCR and LLM extraction to pull key fields automatically.
Results & Impact
- Decision-making across the company went from days to near-instant with full auditability
- Sales and engineering teams now self-serve their own data without waiting on anyone
- Document pipeline processes 10,000+ contracts and invoices per month, extracting key fields automatically — eliminating hours of manual review
Tech Stack
- BigQuery, VertexAI
- Custom connectors to local Postgres
- Slack API integration, Google Cloud Functions
- Python MCP, FastAPI for RESTful endpoints
- Docker containerization, GitHub Actions for CI/CD pipeline
Team Expertise
We delivered in 3 months with a team of two specialists — an AI Engineer and a Technical Business Analyst. We worked closely with Ops Management and heads of each team to understand their workflows and data flow requirements. Our team continues to monitor and optimise the platform, iterating on data models, pipeline reliability, and reporting accuracy as the business evolves.
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