Precisely StreetPro Discover Makes Street Data AI-Ready

Why It Matters

StreetPro Discover bridges the gap between raw GIS data and conversational AI, accelerating time‑to‑insight for high‑stakes operational decisions. It gives enterprises a scalable way to leverage location data without costly data‑engineering overhead.

Key Takeaways

• Transforms street codes into readable text for LLMs
• Enables plain‑language queries on street-level data
• Cuts manual preprocessing time for AI projects
• Improves data consistency across delivery, risk, urban planning
• Supports agentic‑ready data strategy for enterprise AI

Pulse Analysis

Street-level data has long been a stumbling block for organizations that want to embed geographic intelligence into conversational AI. Traditional street datasets are riddled with cryptic numeric codes, ambiguous attribute fields, and hierarchical structures that require specialist spatial expertise to interpret. As large language models become the front‑end for enterprise analytics, the mismatch between human‑readable queries and machine‑ready formats creates latency and error risk. Precisely’s new StreetPro Discover tackles this gap by converting those opaque attributes into clear, searchable text, aligning the data layer with the natural‑language expectations of modern LLMs.

StreetPro Discover delivers a richly interlinked set of street attributes, each rendered as plain English descriptors such as “two‑lane residential street with on‑street parking” instead of obscure GIS codes. This human‑readable layer enables developers to feed the dataset directly into prompt‑engineered LLM workflows, eliminating the need for custom parsers or manual data cleaning. Early adopters report up to a 40 % reduction in time spent on data preparation, allowing analytics teams to focus on model tuning and insight generation. The product also embeds Precisely’s data‑quality guarantees, ensuring that the AI consumes consistent, up‑to‑date information.

The launch of StreetPro Discover signals a broader shift toward “agentic‑ready” data, where the underlying information is pre‑conditioned for autonomous AI agents rather than human analysts alone. Enterprises that integrate this dataset can accelerate use cases such as real‑time delivery routing, risk exposure mapping, and smart‑city planning, all powered by conversational interfaces. By lowering the technical barrier to entry, Precisely positions itself as a strategic data partner in the AI stack, potentially influencing market standards for geographic data preparation. As AI adoption scales, datasets that speak the language of LLMs will become a competitive differentiator.