Dimension Labs

Industry

Guest feedback analytics for restaurants

Read every review and survey across every store. Find whether speed, accuracy, staff or the operator is really moving your rating, location by location.

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Ask Which issue is costing each store its rating?

We work with enterprises such as NBA, Travelers, Mars and Concentrix.

[01] The problem

Same menu, same training. The difference is somewhere in the comments.

Restaurant dashboards report wait time and star averages by store. They don't tell you why two stores with the same menu are a full star apart.

Right now

01Store rankings stop at the star average
02Drive-thru, delivery and dine-in feedback are read separately
03Accuracy issues are hidden in free text
04Operator performance is judged by anecdote
05Menu launches are judged by sales alone

Your guests describe every visit. The pattern across thousands of stores is already there.

[02] EnrichOne record, structured

Language in. Structured data out.

Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for restaurants and food service.

enrichment · one record

Raw text

Waited 25 minutes in the drive-thru and they still forgot the kids' meals. The shift lead at the window was great about it, but this is the third time at this location.

Google review · 2 stars · store joined

Dimensions written to the record

Service Speed & Wait25 min · drive-thru
Transaction / Visit Typedrive-thru
Order & Service Accuracyitems missing
Staff Interaction Qualitypositive · named role
Service Recoveryattempted
First-Time vs. Repeatrepeat · 3rd incident
Location Referencedthis store

Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side.

[03] The product

3.1 · Read everything

Every record, enriched.

Start with one export of Google reviews, surveys and delivery-app feedback, or connect every channel. Every record comes back with typed Dimensions like service speed and wait, order and service accuracy, staff interaction quality, transaction type and location, written the same way every run, then joined to store, format, daypart and sales data.

Read every record, not a sample
Join each record to store, format, daypart and sales data
Add your own Dimensions in plain English
Backfill years of history in the same run

dimension labs · blend

3.2 · Prove the cause

Ask why. Get proof.

Ask in plain English. The agent writes the queries, controls for what would confound the answer, and ranks each issue by its measured effect on rating, revisit and same-store sales, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal.

Which issue is costing each store its rating
Separate drive-thru, delivery and dine-in drivers
See what top operators do differently
Measure how guests received a new menu item

dimension labs · ask

Example finding

Which issue is costing each store its rating?

See the Panera store performance diagnostic: 27 Dimensions across public reviews, compared store by store.

Published artifact · public reviews

3.3 · Deliver

Ask for the report. Get the report.

Ask for the readout your operators and franchise leads needs and the agent builds it: summary, ranked findings, charts and recommendations, each figure checked. Schedule it weekly or monthly on fresh data, or publish it as a live monitor.

Produce the monthly readout without building it
Publish a live monitor leadership can open and question
Track whether the fix you shipped moved the number
Export to PDF, slides or your BI tool

dimension labs · report

[04] How the platform worksRuns daily

From raw conversations to a proven answer.

Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works.

Language · 37 sources

ZendeskIntercomSalesforceLiveChatDriftSlackQualtricsMedalliaSurveyMonkeyQuestionProGenesysAmazon ConnectAvaya Cloud8x8TwilioSMSAdaKore.aiYellow.aiCognigyRasaMicrosoft Bot FrameworkAmazon LexAmazon AlexaOracle Digital AssistantBotcopyCapacityFacebook MessengerKikX (Twitter)OpenAIEmailCall recordingsApp store reviewsGoogle reviewsOpen-ended surveysCommunity forums

Business + world data

SnowflakeDatabricksBigQueryAmazon S3Salesforce CRMCSV + ExcelREST APIRevenueOrdersRenewalsUsage eventsLoyaltyStore + location dataWeatherSeasonalityMarket events

Dimension Labs pipeline · Runs daily

Ingest + normalizeEvery channel and language into one schema

EnrichDozens of Dimensions written onto every record

Map + governValidated for drift, versioned, the same fields every run

Analyze + proveJoined to outcomes and tested for cause

Where it goes

Dimension Labs agentAsk, report, schedule

Your warehouseSnowflake, Databricks, BigQuery

BI toolsTableau, Power BI, Hex

Claude and ChatGPTvia MCP · coming soon

[05] ArtifactsBuilt by the agent