Case 02 — AI Local Search Optimization

Sinal

Local search fixes humans can trust, review, and scale.

Product Designer · 0→1 AI product for multi-location search optimization

Role

Product Design

Scope

0→1 product design · AI recommendation UX · Review and approval flows · Multi-location workflows

Year

2025

Context

Sinal is the anonymized version of a 0→1 AI-powered local search optimization product I designed for SOCi.

Enterprise brands manage hundreds or thousands of locations across search, listings, reputation, social, and local marketing. Corporate teams needed visibility across every location, while local teams needed simple, actionable guidance without becoming SEO experts.

Problem

Local search optimization was high-value, but too manual, fragmented, and technical to scale.

Teams had to find issues across many locations, understand which business fields needed to change, and manually update details like hours, services, categories, and attributes. The design challenge was turning AI recommendations into clear, trustworthy actions that enterprise teams could review, approve, and scale.

Process

  1. 01
    Made AI explainableEvery recommendation had to show what was wrong, why it mattered, which locations were affected, and exactly what would change before anything went live.
  2. 02
    Kept humans in controlBecause Sinal affected public business information, the workflow supported review, edit, approve, and reject states instead of treating AI as an auto-publisher.
  3. 03
    Designed for multi-location scaleThe product had to work for one location, a regional group, or an account-level fix without forcing users to approve the same change hundreds of times.
  4. 04
    Positioned it as a workflow, not a reportRecommendations moved from insight to action through grouped opportunities, affected-location context, editable proposed changes, and scalable approval patterns.

Proposal

AI finds the local search gap; people decide what goes public

  • Opportunity list
  • Affected locations
  • Current vs proposed fields
  • Edit / approve / reject
  • Bulk approval
  • Live below ↓

● Live prototype — this is not an image

Filter recommendations, select location groups, approve or reject profile updates.

app.sinal.ai/search

Search optimization

Recommendations

Needs review

6

Current view

6

Approved locations

0

RecommendationPotential impactAffectedStatusActions

Add curbside pickup to store profiles

Search demand is rising in nearby markets, but the service is missing from affected profiles.

High

Improves match for pickup intent across high-volume branded searches.

128locations
Needs review

Align holiday hours for Memorial Day

Public listings show mixed holiday schedules across locations in the same operating group.

High

Reduces closed-store visits and prevents inconsistent holiday search results.

84locations
Needs review

Use a more specific primary category

Competitor profiles ranking above these locations use a category closer to the searched service.

Medium

May improve relevance for non-branded category searches.

37locations
Needs review

Confirm accessibility attributes

Location pages mention accessible entrances, but public profile attributes are incomplete.

Medium

Makes key visit-planning information visible before customers arrive.

61locations
Needs review

Add same-day appointment availability

Landing pages advertise same-day appointments, but the matching profile service is absent.

Low

Adds clarity for urgent searches, with lower volume than pickup-related terms.

42locations
Needs review

Fix Sunday hours mismatch

Website hours and public profile hours disagree for locations in the southeast region.

Medium

Prevents avoidable no-visits when customers search during weekend hours.

19locations
Needs review

Result

  1. 01
    Designed core parts of the 0→1 product experience, including AI recommendation UX, local-level and account-level workflows, editable states, and approval patterns
  2. 02
    Helped translate complex local search optimization into a guided enterprise workflow customers could understand and act on
  3. 03
    Supported a major AI product milestone: the product reached $1M MRR in less than 30 days

Next case

Pulso

Money that answers before you ask