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Dimension Labs/Analytics agent for customer language 1Bn+ interactions analyzed

Dimension Labs, the analytics agent for customer language. Mapping the universe of customer data.

Every conversation, review and survey holds critical, high-context information. Dimension Labs reads all of them, turns each one into dozens of measurable dimensions, and blends them with your revenue and operational data. The result is a true omni-channel picture that shows what to act on and measures the impact as you go.

What you’re looking atClick, or scroll to travel ↓

Every point is one conversation: a support ticket, a call, a chat, a review, a survey answer. Billions of them, and on their own, noise.

Each conversation is read in full and measured on dozens of axes: intent, effort, churn risk, whether a promise was kept.

The result is rows of typed data, joined to revenue and outcomes, so you can see what to act on and measure the impact.

Trusted by innovators who refuse to operate on assumptions

NBATravelersMarsDisneyConcentrixIntuitSeattle MarinersDropboxAmerican Red CrossResideoNetflixThomson ReutersAsicsStakeEasygoFleishmanHillard
[01] How you use itComplex underneath, simple on top

The data is messy. That’s our problem, not yours.

You ask in plain English and get the answer back as a chart, report, deck or dashboard. The product is complex. Using it is simple.Underneath, Dimension Labs ingests every channel, enriches every record, joins it to your business data, runs the causal tests and keeps your reports and dashboards current.

Dimension Labs · Project: Zendesk Revenue Intelligence
[01.1] The agent at workRead · Enrich · Blend · Prove
The agent at work Live reading · 04 Prove

Know why your numbers move.

Dimension Labs reads every conversation, review and survey, extracts dozens of signals from each one, blends them with your revenue and operational data, and uses causal statistics to prove what drives your metrics. If you can type a question, you can run it.

› What were customers saying before they left? Which signals actually predicted churn?
  1. 01Read
  2. 02Enrich
  3. 03Blend
  4. 04Prove

Language · unstructuredDimensions · the structure we addBusiness data · structured

Working…

Finding 91% of accounts that wrote “we’re evaluating alternatives” left. 14% of the rest did.

80 accounts · 782 messages · 8 channels

From $299 a month. No demo, no sales call, no contract on monthly plans.

One conversation, measured on many axes Hundreds of Dimensions, written in plain English

Dimensions. Language has more axes than sentiment.

Sentiment scores a conversation on one axis: positive or negative. A single support ticket carries dozens more. What the customer wanted. How hard it was. Whether a promise was kept. Whether a competitor came up. Dimension Labs measures each one as its own axis on every record, so you can compare, trend and test them like any other metric.

[02] BlendStructured + unstructured

Blend. What customers said, joined to what they did.

Tickets, calls, chats, surveys and reviews sit next to revenue, renewals, orders and usage, row by row. Add the outside world too: weather, seasonality and market events. It isn’t a complete view if half of it is missing.

  • 1.1Every channel normalized and comparable, in every language
  • 1.2Joined at the account, customer, location or order level
  • 1.3Outside data added only when it can explain the outcome
blend
[03] EnrichOne record, by industry

Enrich. Dozens of signals from every record, from day one.

Each conversation is read in full and written back as typed fields we call Dimensions: intent, effort, churn risk, promise kept, competitor mentioned and more. Some customers run 50 to 100 on every record. You don’t design them from scratch: we start you with the set that works for your industry and your question.

Browse example Dimensions ›
enrichment · one record
Raw text

We're running a 60-day parallel evaluation of another vendor before we sign the renewal. Honestly, we were promised SSO in Q2 and it still isn't here, and this ticket has been open for three weeks.

Support ticket · enterprise account · ARR joined
Dimensions written to the record
  • Competitor Mentionedyes · evaluation underway
  • Churn & Cancellation Riskhigh
  • Lifecycle Stagerenewal
  • Urgency & Deadline60 days
  • Expectation Gaproadmap promise missed
  • Feature RequestSSO
  • Resolution Statusopen · 3 weeks

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

[03.1] PipelineDaily enrichment

Pipeline. Enrichment at scale, every day.

A chat window can summarize a few thousand rows once. Our pipeline enriches millions of conversations in parallel, validates every field, and runs again on new data each day with the same schema. Results write back to your own warehouse.

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
01
Ingest + normalizeEvery channel and language into one schema
02
EnrichDozens of Dimensions written onto every record
03
Map + governValidated for drift, versioned, the same fields every run
04
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
[04] Prove8,504 rated public reviews, Orlando

Prove. Dashboards rank what’s loud. Causal analysis finds what’s costly.

We rebuilt a hotel group’s guest dashboard from public reviews, then asked a different question: not what guests mention, but what actually moves a one-star stay. Front desk and check-in, the dashboard’s number-two priority, causes almost nothing. Staff attitude, mentioned in under 4% of reviews, is the biggest driver of all.

Backdoor adjustmentEffects with confidence intervalsEffects by segmentPlacebo + refutation tests
The dashboard’s priority list by mention volume + correlation The causal ranking effect on a one-star stay Cleanliness 10.0% of comments 1 Front desk + check-in 27.2% of comments · flagged #2 2 Room maintenance 7.9% of comments 3 Staff attitude 3.6% of comments 4 Fees + billing 5.2% of comments 5 1 Staff attitude +44 pts 2 Cleanliness +34 pts 3 Room maintenance +21 pts 4 Fees + billing +19 pts 5 Front desk + check-in +2 pts · not significant The mirage
Exhibit from “What your CX tools can’t tell you”. Left: the standard CX-analytics ranking. Right: a DoWhy causal model on 8,504 rated public reviews, holding hotel, tier, language, review length and reviewer fixed. Every effect survives placebo, random-cause and subset tests.
[05] Act and measure Every channel, one picture

One omni-channel picture. Find what to act on, then measure the impact as it lands.

Most of what customers tell you never reaches the numbers, because it sits in a different tool from the numbers. Dimension Labs brings every channel and your business data into one picture, finds the action that moves the metric, and keeps measuring after you take it.

[06] DeliverOutput from the agent

Deliver. Years of client work, packaged into templates for every use case and vertical.

We’ve delivered this work for insurers, sports leagues, contact centers and consumer brands. Everything we learned is packaged into templates: the right Dimensions, the right analysis and the right report for each use case and industry. Your first answer already looks like the work of a senior analyst.

Reports · Live monitors · Apps · Decks · Charts See every artifact ›
[+] ResearchPublished methods

Research. Years of work on language as data.

Our methods are published, so you can check them. The research covers how to score experience from text, why sentiment misses most of what matters, and how much signal a piece of language can actually carry.

5Published papers
1Bn+Interactions analyzed
100sDimensions created
Signal in the noise

Every enterprise has a dashboard of what happened. None of them have a model of why.

[07] ComparisonAI assistants vs Dimension Labs

“Can’t I just upload this to an AI assistant?” You can. Here’s where it stops.

Export the tickets, drop them into a general AI assistant, ask what’s driving churn, and you’ll get a fluent answer. It reads what fits in one prompt, summarizes it differently every time, and can’t test the answer against your revenue. Dimension Labs is the system underneath: every record structured, joined to your data and proven.

A general AI assistant, file uploadedDimension Labs
01What it reads

As much of the file as fits in one prompt

Every record, enriched by a parallel pipeline

02What it produces

A summary that changes every time you ask

Typed Dimensions on every record, the same schema every run

03How it ranks issues

By how often they're mentioned

By measured effect on the metric, with significance tests

04Business data

Whatever you paste in

Joined to revenue, renewals, orders, and outside data like weather

05Over time

Starts from zero in every chat

Runs daily, so this month compares cleanly to last year

06Where you start

A blank prompt

Templates and report formats built for your use case and vertical

[08] EcosystemIn development

Coming soon. Your Dimensions, inside Claude and ChatGPT.

We’re building MCP servers for the major AI assistants. Your team can ask in the assistant they already use and get answers built on the same enrichment, the same governed data and the same causal tests. Or use our agent. The layer underneath is the same.

Your AI assistant · previewIn development
Which customer signals actually predicted churn last year? Use our Dimension Labs data.
tool calldimension_labs.find_drivers
{ "outcome": "churned", "population": "enterprise accounts",
  "controls": ["tier", "tenure", "ticket_volume"] }
returned · governed Dimensions, tested against the outcome
  • alt_vendor_evaluation6.5×p<0.001
  • promise_not_delivered4.7×p<0.001
  • repeat_issue2.6×significant
Accounts that wrote “we’re evaluating alternatives” were 6.5 times more likely to leave. It’s the earliest warning in your data: for most of the accounts that left, support heard it before any survey did.
[09] CustomersTravelers · Mars · Stake.com · Seattle Mariners
“Dimension Labs gives us that 50,000-foot view we needed to figure out what's going on out there.”
TravelersData Science Business Partner
Read the Travelers story ›
$3.13Min savings delivered to Travelers
600%ROI within 18 months
4Mcall transcripts joined to renewal records at Travelers
1Bn+interactions analyzed
“Structured, reusable conversational data is foundational to our future strategy.”
Mars
“We can move from question to insight fast enough that it actually changes what teams do that week.”
Stake
“Ultimately, it allows us to quantify the qualitative and give context and reasoning to KPI movements.”
Seattle MarinersSeattle Mariners
[10] Who it’s forQuestions you can ask today

Who it’s for. Everyone who was told the data team would get back to them.

›Why is NPS dropping, and for whom?›Which ticket themes predict cancellation?›Which accounts are at risk right now, and why?›What separates top agents from the rest?›Did the change we shipped last month work?›Which locations are underperforming, and what's different about them?›What did customers say before refunds spiked?›Which complaints are loud but cost nothing?›Where does the bot fail, and what does it cost?

Enterprise grade security

Built to handle Fortune 500 customer data.

With the controls enterprise security teams ask for, on every plan.

Security at Dimension Labs
  • SOC 2 Type IIAudited controls over security, availability and confidentiality.
  • ISO 27001Certified information security management.
  • GDPR and CCPACompliant, with data processing agreements for customers.
  • PII redactionPersonal data redacted at ingest, before enrichment.
  • Single or multi-tenantCloud hosted, with single-tenant and on-premise options for enterprise.
  • SSO and rolesSingle sign-on and role-based permissions, plus full data deletion on request.
Dimension Labs/Start with one file From $299 a month

Stop reporting. Start understanding.

Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans.