PartMatch

Find every compatible aftermarket option in seconds.

PartMatch is a parts cross-reference platform for auto parts counters, repair shops, e-commerce teams, and fleet buyers who need to match OEM part numbers to compatible aftermarket alternatives. It helps users quickly find valid replacement options, compare fitment, and reduce manual lookup time and missed sales.

Business Goals

  • Reduce OEM-to-aftermarket lookup time by 70% within 3 months of launch.
  • Increase quote conversion for subscribed customers by 15% within 6 months.
  • Achieve 40% monthly active usage among onboarded accounts within 90 days.
  • Reach 25% of searches resulting in a saved shortlist or quote export within 60 days.
  • Keep search result relevance feedback positive on 85% of searches after 6 months.

User Goals

  • Find compatible aftermarket alternatives for an OEM part number in under 10 seconds.
  • Confirm fitment across make, model, year, engine, trim, and regional variants.
  • Compare multiple brands by price, warranty, availability, and supplier.
  • Export results to share with customers or internal teams.
  • Avoid ordering errors caused by superseded, discontinued, or ambiguous OEM references.

Non-Goals

  • Not a general vehicle repair manual or diagnostic tool.
  • Not a full inventory management system or ERP replacement.
  • Not a marketplace that processes checkout or payments in v1.
  • Not intended to guarantee fitment without user review of vehicle specifics and exceptions.

Counter Sales Rep Jamie, 31 - Works at a busy auto parts counter and handles many part requests per shift. Needs fast, confident answers while the customer is waiting on the phone or in store.

Counter Sales Rep Jamie, 31

  • As a counter sales rep, I want to enter an OEM part number and see compatible aftermarket options, so that I can quote alternatives quickly.
  • As a counter sales rep, I want to compare brands by fitment and price, so that I can recommend the best option for the customer.
  • As a counter sales rep, I want to flag uncertain matches and view notes, so that I can avoid selling the wrong part.

Shop Owner Elena, 48 - Runs a small repair shop and wants to reduce lost time searching catalogs. She values accuracy, repeatability, and the ability to save common lookup patterns.

Shop Owner Elena, 48

  • As a shop owner, I want to search part references by vehicle and OEM number, so that my team can standardize lookups.
  • As a shop owner, I want to save frequent searches and share them with staff, so that we work faster across shifts.
  • As a shop owner, I want exportable match reports, so that I can keep records for customer approval.

E-commerce Catalog Manager Priya, 39 - Maintains online product listings for an auto parts retailer. She needs clean cross-reference data and bulk workflows to improve catalog coverage.

E-commerce Catalog Manager Priya, 39

  • As an e-commerce manager, I want to bulk upload OEM numbers and receive mapped aftermarket equivalents, so that I can enrich product listings at scale.
  • As an e-commerce manager, I want confidence scores and source references, so that I can review uncertain matches before publishing.
  • As an e-commerce manager, I want API access, so that I can sync part matching into our catalog pipeline.

OEM-to-Aftermarket Search · High priority

  • Users can search by OEM part number, brand, vehicle attributes, or partial reference and receive ranked compatible aftermarket options.
  • Accept exact, partial, and normalized part numbers including hyphens, spaces, and leading zeros.
  • Return results within 3 seconds for 95% of queries on the standard catalog index.
  • Group results by match type such as direct replacement, supersession, and equivalent alternative.
  • Show source evidence for each match, including catalog origin and confidence score.
  • Handle no-result searches with suggested close matches and normalization hints.

Fitment Validation · High priority

  • The system checks vehicle applicability and known constraints so users can judge whether a part is truly compatible.
  • Support make, model, year, engine, trim, drivetrain, and region filters.
  • Warn when fitment is universal only, conditional, or dependent on production date.
  • Surface superseded OEM references and discontinued parts with current replacements.
  • Highlight conflicts when one aftermarket option fits only a subset of vehicles.
  • Allow users to mark a result as verified, questionable, or not compatible.

Comparison and Shortlist · Medium priority

  • Users can compare multiple part options side by side and save them for later use or customer quoting.
  • Compare up to 5 parts at once with price, brand, warranty, stock status, and fitment notes.
  • Save shortlists to named lists tied to customers, jobs, or SKU projects.
  • Export comparison views to PDF and CSV.
  • Persist shortlist history for returning users.
  • Support notes and internal tags on saved items.

Bulk Lookup and API · Medium priority

  • Power users can process many OEM references at once and integrate part matching into external systems.
  • Upload CSV with up to 10,000 rows per job.
  • Return row-level match status, confidence, and error messages.
  • Provide REST API endpoints for search, match, and export.
  • Rate limit API keys and show usage dashboards.
  • Support webhook notifications when bulk jobs complete.

Admin Data Management · Low priority

  • Internal admins can manage catalogs, mappings, quality flags, and ingestion workflows to keep results fresh.
  • Import catalog feeds from distributors and normalize vendor formats.
  • Review and resolve duplicate part mappings.
  • Track stale records and source refresh dates.
  • Audit edits by user, timestamp, and change reason.
  • Allow manual overrides with approval workflow.

Fast Lookup Onboarding

  • Sign up with email, Google, or company SSO.
  • Choose role: counter sales, shop owner, or catalog manager.
  • Import an initial OEM list, upload a CSV, or try a sample search.
  • Run first lookup in under 2 minutes with guided input examples.
  • See an instant comparison panel with confidence labels and fitment warnings.
  • Save results or export a report as the first success action.

1. Enter OEM reference

  • The user pastes or types an OEM part number or uploads a list.
  • Normalize punctuation and whitespace automatically.
  • Validate empty or obviously malformed inputs with inline correction hints.
  • Support batch paste and CSV import from the same entry point.

2. Refine fitment context

  • The user adds vehicle or application details when available to narrow results.
  • Offer make, model, year, engine, trim, and region filters.
  • Default to broad search if no vehicle context is provided.
  • Warn when missing context may reduce confidence.

3. Review ranked matches

  • The system displays matched aftermarket options with confidence and evidence.
  • Show direct replacements first, then equivalent alternatives, then conditional matches.
  • Display source catalogs, supersession lineage, and fitment notes.
  • Flag low-confidence or conflicting entries with clear labels.

4. Compare and shortlist

  • The user compares options and saves the best candidates for quoting or sourcing.
  • Side-by-side view highlights differences in price, brand, warranty, and availability.
  • Let users attach notes and tags to shortlist items.
  • Allow removal of suspected mismatches without losing search context.

5. Export or share

  • The user sends the final result to a customer, teammate, or downstream system.
  • Export PDF and CSV with branding and timestamp.
  • Share a secure link with optional expiration.
  • Track export status and retry if file generation fails.

Power Features and Edge Cases

  • Bulk CSV lookup with error row reporting and retry.
  • API access for catalog enrichment and ecommerce integration.
  • Saved search templates for common vehicle programs or part families.
  • Duplicate resolution and supersession tracking for legacy OEM numbers.
  • Admin override workflow for disputed matches.
  • Localization support for regional part numbering and metric/imperial conventions.

Clear, Trustworthy, Fast

  • Results use strong visual hierarchy with confidence badges and fitment warnings above the fold.
  • Accessible keyboard navigation and screen-reader labels for comparison tables and filters.
  • Performance-first interface with optimistic loading states and progressive result streaming.
  • Color is never the only signal; warnings and match states use icons and text.
  • Mobile-friendly search-first layout for counter staff using tablets or phones.

Jamie is at a busy auto parts counter when a customer calls with an OEM reference from a repair invoice. In the past, Jamie would jump between catalogs, supplier portals, and spreadsheets, hoping to find a compatible aftermarket option before the call ended.

With PartMatch, Jamie pastes the OEM number, adds the vehicle year and engine, and gets a ranked list of compatible alternatives in seconds. The confidence labels, fitment notes, and source references help Jamie choose the right part quickly, which shortens call time, increases quote conversion, and reduces costly misorders.

A week later, Jamie’s manager reviews saved searches and exports from PartMatch to see which brands and part families are being requested most. That turns lookup activity into actionable catalog insight and helps the business stock better-selling alternatives.

User-Centric Metrics

  • 95% of single-part searches return a usable answer within 10 seconds.
  • 70% reduction in average lookup time versus manual catalog search.
  • At least 85% of search results are rated helpful by users.
  • 40% of users save or export a result within the first 30 days.
  • Less than 2% of searches end in an incorrect-fitment complaint.

Business Metrics

  • 15% increase in subscription conversion from trial to paid within 6 months.
  • 30% increase in monthly active accounts within 90 days of launch.
  • 25% of active accounts using the product weekly after 6 months.
  • 20% of users upgrading to bulk/API plans within 9 months.

Technical Metrics

  • 99.9% monthly uptime for the search service.
  • Under 300 ms p95 response time for cached searches and under 3 seconds for uncached lookups.
  • Zero critical security findings in quarterly scans.
  • Bulk job completion success rate above 98%.

Tracking Plan

  • search_submitted with query type, input length, and channel.
  • search_results_rendered with latency, result count, and confidence distribution.
  • fitment_filter_applied with vehicle fields selected.
  • result_shortlisted with part IDs and match type.
  • export_generated with format, record count, and destination.
  • bulk_upload_completed with row count, error count, and processing time.
  • match_feedback_submitted with helpful, incorrect, or uncertain classification.

Technical Needs

  • Frontend in React with TypeScript for fast search and comparison workflows.
  • Backend API in Node.js or Python FastAPI for search, matching, and export services.
  • Search index using Elasticsearch or OpenSearch for normalized part-number retrieval.
  • Relational database in PostgreSQL for users, saved searches, shortlist data, and audit logs.
  • Object storage such as S3 for imports, exports, and catalog feed snapshots.
  • Background jobs using queue infrastructure such as SQS, BullMQ, or Celery for bulk processing.
  • Observability stack with OpenTelemetry, Sentry, and Prometheus/Grafana for tracing and monitoring.

Integration Points

  • OAuth and SSO via Google Workspace or Microsoft Entra ID.
  • Distributor or catalog feed imports from supplier CSV or API endpoints.
  • Vehicle data source such as TecDoc, AutoCare, or a licensed fitment dataset.
  • Email delivery through SendGrid or Postmark for exports and notifications.
  • Optional webhooks and REST API for ERP, PIM, or ecommerce systems.

Data Storage & Privacy

  • Store only necessary user and company data, with separate tenant isolation for enterprise customers.
  • Encrypt data at rest and in transit using industry-standard TLS and managed database encryption.
  • Provide GDPR and CCPA support for data export, deletion, and access requests.
  • Keep audit logs for manual overrides and admin changes for at least 12 months.
  • Avoid storing sensitive payment data in scope for v1; use a third-party billing provider instead.

Scalability & Performance

  • Use indexed part-number normalization to handle high-volume exact and fuzzy searches.
  • Cache frequent lookups and common vehicle combinations to reduce repeated query load.
  • Process bulk uploads asynchronously so large files do not block interactive search.
  • Design for catalog ingestion growth to tens of millions of cross-reference records.

Potential Challenges

  • Incomplete or conflicting catalog data can produce false positives; mitigate with confidence scoring, source ranking, and manual review tools.
  • Part numbering conventions vary by brand and region; mitigate with normalization rules and reference dictionaries.
  • Fitment can depend on production date or sub-variants; mitigate with explicit warning states and mandatory context fields where needed.
  • Users may overtrust results; mitigate with clear disclaimers, evidence links, and feedback/reporting tools.
  • Catalog updates can stale quickly; mitigate with scheduled refreshes, source timestamps, and stale-data alerts.

Team & resourcing - Small product team: 2 backend engineers, 1 frontend engineer, 1 product designer, part-time PM, and shared data/catalog specialist.

Phase 1: MVP Search · Weeks 1–5

  • Single OEM search input with normalization
  • Basic cross-reference results list
  • Confidence labels and source references
  • User authentication and tenant setup
  • Simple export to CSV

Phase 2: Fitment and Compare · Weeks 6–10

  • Vehicle filter workflow
  • Side-by-side comparison view
  • Saved shortlist and notes
  • PDF export
  • Feedback capture on match quality

Phase 3: Bulk and Admin Tools · Weeks 11–16

  • CSV bulk upload and processing jobs
  • Admin catalog ingestion and override tools
  • Audit logs and source freshness indicators
  • Webhook notifications and usage dashboards
  • API endpoints for search and export

Phase 4: Enterprise Hardening · Weeks 17–24

  • SSO integration
  • Role-based permissions
  • Performance tuning and caching
  • Monitoring, alerts, and SLA reporting
  • Compliance workflows for data access and deletion

Paste this into Cursor, Bolt, Lovable, or v0 to start building.

Build a SaaS web app called PartMatch for cross-checking OEM auto parts against aftermarket options.

Product summary:
Users paste an OEM part number or upload a CSV, optionally add vehicle context, and get ranked compatible aftermarket alternatives with confidence scores, fitment notes, source references, and comparison tools. Target users are parts counter reps, repair shop owners, and ecommerce catalog managers.

Core features:
1. Search by OEM part number, partial number, or brand reference with normalization for hyphens, spaces, and leading zeros.
2. Show ranked results: direct replacements, equivalent alternatives, and conditional matches.
3. Fitment filters for make, model, year, engine, trim, drivetrain, and region.
4. Side-by-side comparison table with price, brand, warranty, stock, and fitment notes.
5. Saved shortlists, notes, tags, PDF/CSV export, shareable links.
6. Bulk CSV upload with async job processing and row-level error reporting.
7. Admin tools for catalog imports, source freshness, duplicate resolution, and manual overrides.
8. Basic analytics dashboard and feedback capture.

Primary screens/flows:
Search home, results page, compare drawer, shortlist page, bulk upload page, admin catalog page, login/signup, and account settings.
Flow: sign in, search or upload, refine fitment, review ranked matches, compare, save/export, submit feedback.

Data model:
User, Organization, Role, OEMSearch, SearchResult, Part, VehicleFitment, SourceCatalog, Shortlist, ShortlistItem, BulkJob, BulkJobRow, AuditLog, Feedback.
Each Part should store part number, brand, description, source, supersession links, fitment references, confidence score, last updated, and tenant ID.

Tech stack:
Frontend: Next.js 14, React, TypeScript, Tailwind CSS, shadcn/ui.
Backend: Node.js with NestJS or FastAPI, REST API, PostgreSQL, Redis, Elasticsearch or OpenSearch, S3-compatible storage, background jobs with BullMQ or SQS, OpenTelemetry, Sentry.
Auth: Google/Microsoft OAuth and optional SSO.
Deploy on AWS with Docker.

Implementation requirements:
Make the UI fast, search-first, mobile-friendly, accessible, and trustworthy. Use clear confidence badges, warning states, and source evidence. Add tenant isolation, role-based access control, export generation, and audit logging. Seed the app with mock data and provide clear placeholders where a licensed vehicle fitment dataset or supplier catalogs would integrate later.

Build the MVP end-to-end with realistic dummy data, polished UI, and a production-ready project structure.

Business Idea

Aftermarket part reference checker to cross check OEM parts against to find all options available for customer

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