Dimension Labs

Use case

Review intelligence and competitive benchmarking

Read every public review of you and your competitors. Compare on the same Dimensions, and find which differences actually move ratings.

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Ask Where do we lose to competitors, and why?

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

[01] The problem

Your competitors' customers are telling you how to win. Publicly.

Reputation tools count stars and reply to reviews. They don't read your competitors' reviews on the same scale as yours, so you can't see where you actually lose.

Right now

01Benchmarks stop at star averages
02Competitor reviews are read anecdotally, if at all
03Every source rates on a different scale
04Response teams triage, nobody analyzes
05Market shifts show up a quarter late

Every review site is a free, continuous survey of your whole market.

[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 this use case.

enrichment · one record

Raw text

The car was delivered with a check engine light on. Registration still hasn't arrived after 60 days, and every call goes to a different advisor. Wouldn't recommend them to anyone.

Review · vehicle purchase · deal joined

Dimensions written to the record

Reliability & Defect Typecheck engine · at delivery
Expectation Gapcondition on delivery
Urgency & Deadlineregistration · 60 days
Effort Levelhigh · no single owner
Recommendation Signaldetractor
Predicted NPS Banddetractor

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, Trustpilot, app store and marketplace reviews, or connect every channel. Every record comes back with typed Dimensions like competitor mentioned, core quality verdict, price-value perception, primary friction and recommendation signal, written the same way every run, then joined to location, brand, date and market data.

Read every record, not a sample
Join each record to location, brand, date and market 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 and recommendation, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal.

Where do we lose to competitors, and why
Compare every brand on the same Dimensions
See which differences actually move ratings
Watch for market shifts every week

dimension labs · ask

Example finding

Where do we lose to competitors, and why?

See the Carvana multi-source review analysis: 25 Dimensions across five public review sources.

Published artifact · public reviews

3.3 · Deliver

Ask for the report. Get the report.

Ask for the readout your brand and strategy teams 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