Case 02 — AI Local Search Optimization

Sinal

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

Role

Product Design — end-to-end (0→1)

Scope

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

Year

2025

$1M

ARR within 90 days of launch

71

Enterprise accounts actively adopted

+130%

Local adoption vs. accounts without the workflow

● Play with a live prototype of this project

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

Context

Sinal is the anonymized version of a 0→1 AI-powered local search optimization product designed for a B2B SaaS platform for multi-location brands.

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.

Insight

Trust — not accuracy — was the real barrier to AI adoption.

Teams didn't hesitate because the AI's suggestions were wrong; they hesitated because the changes touched public business data and they couldn't see what would go live. That reframed the work from "make smart recommendations" to "make every recommendation explainable, reviewable, and reversible."

Trade-offs

  1. 01
    Human-in-the-loop over full automationAuto-publishing would have felt faster and more magical, but a single wrong change to a public profile erodes trust permanently. I chose review, edit, approve, and reject states — less automated in feel, far safer to adopt at enterprise scale.
  2. 02
    Grouped bulk approval over per-location reviewReviewing each location was the most precise option but couldn't scale across hundreds of profiles. Grouping identical issues into one decision traded some granularity for the scale enterprise teams actually needed.
  3. 03
    Explainability over a leaner UIShowing what's wrong, why it matters, and current-vs-proposed values added visual weight — but without it, teams wouldn't trust a change enough to approve it.

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
  • ↑ Prototype above

Result

  1. 01
    Explainable recommendations teams could trustEvery recommendation showed what was wrong, why it mattered, and exactly which fields would change — so enterprise teams understood each fix before it went public.
  2. 02
    Human control at every stepEdit, approve, and reject states kept AI under human review instead of auto-publishing changes to live business profiles.
  3. 03
    One decision, hundreds of locationsGrouped opportunities and bulk approval let teams fix identical issues across a whole location group without approving the same change hundreds of times.
  4. 04
    Workflows for centralized and local teamsAccount-level and local-level flows supported corporate oversight and distributed on-the-ground teams from the same product.
  5. 05
    Delivered a major AI product milestoneThe 0→1 product reached $1M ARR within 90 days of launch, with +130% local adoption among accounts using the workflow versus those without.

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