Dimension Labs

Use case

Accelerated analysis for agencies

Upload the client's verbatims, tickets or transcripts. Get the coded dataset and the finding in an afternoon, not three weeks.

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Ask Code these 40,000 verbatims and tell me what's driving the score.

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

[01] The problem

Every project starts with 40,000 records and a deadline.

Your analysts hand-code verbatims into a taxonomy built from scratch for every client. It is the least billable work you do, and the slowest.

Right now

01Analysts hand-code verbatims for weeks
02A new taxonomy gets built from scratch on every single project
03Margin disappears into hours you cannot bill for
04Turnaround caps how many projects you can take
05You deliver themes when the client wanted proof

Clients do not pay for coding hours. They pay for the finding. Everything before it is cost.

[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

We booked a suite for our anniversary and arrived to hair in the sink and a stained carpet. When I asked to move, the manager was dismissive and made us feel like the problem. Then a $39 resort fee we were never told about. We will not be back.

Google review · 1 star · property joined

Dimensions written to the record

Facility & Cleanlinessbroadly dirty
Primary Frictioncleanliness
Staff Interaction Qualitydismissive · named role
Service Recoveryrefused
Fee & Premium Changeundisclosed fee · $39
Price–Value Perceptionpoor
Loyalty Signalwill not return

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.

Drop in one client's file and every record comes back coded: topic, root cause, sentiment, effort, intent. Each client gets a separate workspace, so data never crosses between accounts. Backfill three years of history in the same run.

Run every client in a separate, segregated workspace
Code a full back catalogue, not a sample
Build the client's own taxonomy in plain English
Have the agent build context on your client automatically

dimension labs · blend

3.2 · Prove the cause

Ask why. Get proof.

Ask in plain English. The agent runs the analysis, controls for what would confound it, and returns the finding with an effect size and confidence interval. Defensible when the client's own analytics team pushes back.

Take a proven finding into the room, not a theme cloud
Rank drivers by impact, not by mention volume
Join the client's CRM or sales data when they share it
Show the exact verbatims behind every finding

dimension labs · ask

Example finding

Code these 40,000 verbatims and tell me what's driving the score.

Coded dataset, ranked drivers with effect sizes, and the verbatims behind each finding, in one run.

One client file · separate workspace

3.3 · Deliver

Ask for the report. Get the report.

Say what the deliverable needs to contain and the agent builds it: executive summary, findings, charts, recommendations. Rerun it monthly on fresh data and a one-off project becomes a retainer. Export as HTML, PDF or slides.

Produce the client deck without building it
Turn one-off projects into monthly retainers
Run the same methodology across every client
Export to PDF, slides or their 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