SYSTEMS SCALE.
PEOPLE DON'T
Every case study below represents a business that hit a ceiling and used our 4-step engineering process to break through it. No duct-tape, no no-code hacks—just reliable, production-grade results.
CASE STUDY 01
AI-Powered Govcon Consulting Platform
Government Contracting Niche
The Void:
Federal Government Advisors was drowning in operational chaos. Their ERP loaded slowly, clients were complaining, contract opportunities were scattered across systems, and bid teams coordinated entirely through email chains. Staff were overworked manually generating capability statements one at a time.
The System:
We rebuilt FGA's entire operational stack from scratch in 4 months — one unified platform that replaced their ERP, onboarding flow, and internal comms. AI-powered opportunity matching with NAICS filters, instant capability statement generation, an AI chatbot for the contract database, daily automated recommendations, and a unified bid team dashboard.
The Outcome:
Capability statement generation dropped from hours to under 5 minutes. Client complaints dropped as the experience improved. Staff stopped firefighting and started focusing on winning bids. FGA now has the infrastructure to scale without proportionally scaling headcount.
The Void:
Federal Government Advisors was drowning in operational chaos. Their ERP loaded slowly, clients were complaining, contract opportunities were scattered across systems, and bid teams coordinated entirely through email chains. Staff were overworked manually generating capability statements one at a time.
The System:
We rebuilt FGA's entire operational stack from scratch in 4 months — one unified platform that replaced their ERP, onboarding flow, and internal comms. AI-powered opportunity matching with NAICS filters, instant capability statement generation, an AI chatbot for the contract database, daily automated recommendations, and a unified bid team dashboard.
The Outcome:
Capability statement generation dropped from hours to under 5 minutes. Client complaints dropped as the experience improved. Staff stopped firefighting and started focusing on winning bids. FGA now has the infrastructure to scale without proportionally scaling headcount.
CASE STUDY 02
AI-Powered Purchase Order Ingestion
Industrial manufacturing Niche
The Void:
Holloway Group was processing dozens of multi-page Purchase Orders daily — each one taking 30 minutes of manual cross-referencing against thousands of SKUs. Senior staff were stuck acting as human bridges between an email inbox and their ERP, burning 40 hours a week on data entry.
The System:
We built an end-to-end AI Ingestion Pipeline using n8n, Gemini Pro OCR, and Vector Search. The system autonomously monitors the inbox, extracts data from unstructured PDFs, matches inconsistent product descriptions to exact database records via semantic similarity, and pushes validated data directly into Unleashed — with a Human-in-the-Loop dashboard for low-confidence flags.
The Outcome:
Processing time dropped from 30 minutes to under 2 minutes per PO. The team now handles 10x their previous volume with zero additional headcount and near-zero manual transcription errors.
The Void:
Holloway Group was processing dozens of multi-page Purchase Orders daily — each one taking 30 minutes of manual cross-referencing against thousands of SKUs. Senior staff were stuck acting as human bridges between an email inbox and their ERP, burning 40 hours a week on data entry.
The System:
We built an end-to-end AI Ingestion Pipeline using n8n, Gemini Pro OCR, and Vector Search. The system autonomously monitors the inbox, extracts data from unstructured PDFs, matches inconsistent product descriptions to exact database records via semantic similarity, and pushes validated data directly into Unleashed — with a Human-in-the-Loop dashboard for low-confidence flags.
The Outcome:
Processing time dropped from 30 minutes to under 2 minutes per PO. The team now handles 10x their previous volume with zero additional headcount and near-zero manual transcription errors.
CASE STUDY 03
AI Matchmaking Engine for Medical School Placement
Education Technology Niche
The Void:
Talearnted Tutors was manually matching medical students to universities using years of proprietary data locked in rigid spreadsheets. Every new student added hours of administrative cross-referencing. The team was trapped in manual mode — scaling was impossible and human error in matching put both students and the agency's reputation at risk.
The System:
We engineered a custom AI agent system built on Gemini Pro 1.5, n8n, and Supabase. The pipeline triggers automatically on Stripe payment, sanitises years of messy matching data into a Hybrid Search & Filter engine that combines strict SQL filters with LLM-powered reasoning, and delivers instant university recommendations. A real-time dashboard lets the client update criteria without engineering intervention.
The Outcome:
Matching time dropped by 95%. The system now handles any volume — 10 students or 1,000 — with identical overhead. AI-generated recommendations consistently hit the expert-level "4–5 university" sweet spot, and the platform is live and generating revenue.
The Void:
Talearnted Tutors was manually matching medical students to universities using years of proprietary data locked in rigid spreadsheets. Every new student added hours of administrative cross-referencing. The team was trapped in manual mode — scaling was impossible and human error in matching put both students and the agency's reputation at risk.
The System:
We engineered a custom AI agent system built on Gemini Pro 1.5, n8n, and Supabase. The pipeline triggers automatically on Stripe payment, sanitises years of messy matching data into a Hybrid Search & Filter engine that combines strict SQL filters with LLM-powered reasoning, and delivers instant university recommendations. A real-time dashboard lets the client update criteria without engineering intervention.
The Outcome:
Matching time dropped by 95%. The system now handles any volume — 10 students or 1,000 — with identical overhead. AI-generated recommendations consistently hit the expert-level "4–5 university" sweet spot, and the platform is live and generating revenue.
Ready to Bridge the Gap?
Stop losing revenue to broken systems. Let's build the infrastructure your growth deserves.
Schedule Your Void Audit