Hybrid Address Intelligence: From ~5% to 80%+ Accuracy

The challenge

YourDelivery relied on structured address fields—but in production, input quality was highly unreliable. Customers frequently:

• submitted incomplete or incorrect address details;
• mixed names, phone numbers, and irrelevant text into address fields;
• introduced typos and inconsistent formatting at high frequency.

The operational result:

• the system could only reconstruct roughly 5–6 correct addresses out of 100;
• manual validation became mandatory, including roughly one full-time role dedicated to cleaning addresses;
• delivery workflows slowed and backlogs formed—this was both a cost and a speed problem.

Growth was constrained by data quality and process drag, not by lack of demand for deliveries.

What CVision identified during the audit

The core issue was not “we need more AI.” It was bad input structure combined with over-reliance on naive parsing logic.

• Deterministic-only approaches failed because real-world inputs were too inconsistent.
• A pure LLM approach would introduce guesswork and hallucination risk on high-stakes location data.
• Address reconstruction needed a hybrid system, not a single hammer.
• A reliable geographic truth source—Eircode—was underutilized relative to its value.

The missing layer: separate structured signal from noisy input before processing, then route work based on confidence—not hope.

The system we designed

CVision designed a hybrid address intelligence pipeline—not a single model, not a brittle rules engine pretending to be “done.”

What was built

• A backend API integrated into the client workflow (microservice-style delivery to existing ops systems).
• Batch processing at roughly ~1000 addresses per day at peak throughput needs.
• Hybrid deterministic + LLM processing engine with explicit stage boundaries.
• Confidence scoring and a human-in-the-loop path for edge cases—no silent failures.
• Observability through batch-level accuracy tracking so improvements are measurable over time.

The implementation was meant to be reusable, maintainable, and operable by internal teams—not a one-off script.

Why this was production-grade

This was positioned as infrastructure, not a demo wrapper around an API call.

• Controlled AI usage: LLMs only where necessary—not as the primary decision engine for location.
• Deterministic-first architecture: consistency and reduced hallucination risk on core paths.
• Fallback + human review: explicit handling when confidence is low.
• Confidence scoring: safe automation at scale instead of “always auto.”
• Data integrity focus: Eircode as a ground-truth anchor for geographic correctness.
• Batch scalability: engineered for sustained daily volume—not occasional tests.