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Vehicle Repair — Reliable Valuation Search and Location-Aware Repair-Shop Discovery

We rebuilt valuation filtering and nearby repair-shop search so saved records stay accurate, fast, and properly scoped to the right users.

Vehicle Repair — Reliable Valuation Search and Location-Aware Repair-Shop Discovery

Overview

We partnered with a vehicle-service platform to fix broken valuation listing and search behavior, moving fragile browser-only filtering to a scalable backend and sharpening location-based repair-shop discovery, without rebuilding the underlying valuation engine. The result is filtering, pagination, and map search that users can finally trust.

Challenges

  • The "view all valuations" experience did not work correctly because filtering and pagination ran only in the browser, with no server-side support to back it.
  • Valuation lists sometimes surfaced the wrong subset of records because tenant and user visibility rules were not consistently enforced.
  • Year filters returned wrong results whenever values were compared as numbers in one place and strings in another.
  • Ownership filters behaved unpredictably because empty, missing, null, and zero-like values were all treated ambiguously.
  • Loading valuations was slow and sometimes incomplete because the full record list was pulled into the frontend at once.
  • Search, sort, and filter state in the interface drifted out of sync with what the backend actually returned.
  • Nearby repair-shop results looked inaccurate because the search was anchored only to a postal-code center rather than a precise location.
  • Legitimate repair shops were being excluded by overly strict rating thresholds, while a fixed search radius left poor or missing coverage in many areas.
  • Postal codes broke storage and validation because they were forced into numeric-only handling, and external map and search API failures surfaced as vague, unexplained errors.

Solution highlights

  • We built a server-side valuation listing endpoint driven by query-parameter filters for search text, make, year, ownership state, and related metadata.
  • We enforced tenant and user-scoped valuation visibility so every list reflects exactly what the viewer is allowed to see.
  • We added server-side pagination for "view all" records and switched the app to API-driven filtering instead of loading every record into the browser.
  • We normalized frontend filters for year, ownership, search, sort field, and sort direction, and aligned data types so numeric and string mismatches no longer break results.
  • We delivered nearby repair-shop search with a configurable minimum rating and configurable radius to suit both urban and rural contexts.
  • We added optional address-plus-postal-code geocoding and robust postal-code handling, including text and extended formats, for more accurate location results.
  • We introduced optional keyword-based search to improve repair-category matching and added clear UI messaging that surfaces the active radius, rating threshold, and result limitations.
  • We strengthened validation and error handling across geocoding, places search, and external API failures with clearer failure states for auth, quota, and no-result scenarios.
  • We added integration and regression testing plus acceptance checks covering valuation filters, pagination, and nearby-shop search accuracy.

Outcomes

  • Users can now reliably view, filter, sort, and page through their full valuation history without the list breaking or stalling.
  • Valuation records stay correctly scoped to the intended tenant and user, removing the risk of seeing the wrong subset of data.
  • Year and ownership filtering behave consistently across every value type, so results match what users expect.
  • Large valuation lists load quickly because filtering and pagination now happen on the server instead of in the browser.
  • Repair-shop discovery returns more accurate, location-aware results thanks to address-level geocoding and adjustable radius.
  • Adjustable rating thresholds keep legitimate shops in the results instead of filtering them out too aggressively.
  • Users understand why specific shops appear or do not, because search radius and rating criteria are shown directly in the interface.
  • External map and search failures now produce clear, actionable messages and fallback states instead of silent or confusing errors.
  • Regression risk is lower and changes are safer to ship, with targeted test coverage protecting valuation filtering and location-based search.

Gallery

Vehicle Repair — Reliable Valuation Search and Location-Aware Repair-Shop Discovery screenshot 1
Vehicle Repair — Reliable Valuation Search and Location-Aware Repair-Shop Discovery screenshot 2
Vehicle Repair — Reliable Valuation Search and Location-Aware Repair-Shop Discovery screenshot 3