Dimension Labs

Industry

Product voice analytics for consumer brands

Read every product review, care contact and social post. Find which defects and experiences actually move ratings and returns, SKU by SKU.

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Ask Which defect is costing us ratings and returns?

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

[01] The problem

Your retail partners read your reviews. Your product team should too.

Consumer feedback is spread across retailer reviews, care lines and social. Each source is read separately, so a defect pattern can run for months before it's named.

Right now

01Retailer reviews are read one listing at a time
02Care-line contacts are coded inconsistently
03Defect patterns take months to surface
04Launches are judged on sales alone
05Claims and safety signals are found by chance

A product's real spec sheet is written by the people who use it.

[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 consumer products and cpg.

enrichment · one record

Raw text

The sensor drops connection every night around 2am, which is the whole point of buying it. Support sent a replacement that does the same thing. Returning it.

Product review · 1 star · SKU joined

Dimensions written to the record

Reliability & Defect Typeconnectivity · nightly
Product / Module Referencedsensor
Core Quality Verdictfails core job
Service Recoveryreplacement · same defect
Return & Refund Signalreturning

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 retailer reviews, care-line contacts and social posts, or connect every channel. Every record comes back with typed Dimensions like reliability and defect type, core quality verdict, return and refund signal, product referenced and safety-critical event, written the same way every run, then joined to SKU, batch, retailer and sales data.

Read every record, not a sample
Join each record to SKU, batch, retailer 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, returns and repeat purchase, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal.

Which defect is costing us ratings and returns
Compare every SKU on the same Dimensions
See how a launch landed in the first month
Flag safety and claims signals on every record

dimension labs · ask

Example finding

Which defect is costing us ratings and returns?

See the Owlet product voice intelligence: 34 Dimensions across product reviews and public filings.

Published artifact · public sources

3.3 · Deliver

Ask for the report. Get the report.

Ask for the readout your brand and quality 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