Dimension Labs

Industry

Conversation analytics for contact centers and BPOs

Read every call, chat and bot conversation across every client program. Prove which agent and process changes moved resolution and CSAT.

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Ask Which changes moved resolution for this client?

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

[01] The problem

Your clients want proof the program is working. QA samples won't give it to them.

BPOs run dozens of programs, each with its own taxonomy and scorecard. Proving value at the QBR means hand-built decks from sampled data.

Right now

01QA reviews a fraction of interactions
02Every client program codes differently
03QBR decks are built by hand
04Bot and human channels are measured separately
05Process fixes ship without measuring the effect

You already handle every conversation. The analysis should cover every one too.

[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 contact centers and bpos.

enrichment · one record

Raw text

This is my fourth call about the same bill. The last agent said a credit would post in 48 hours and it never did. You're the first one who actually listened, thank you, can you note that on the account?

Call transcript · billing queue · agent joined

Dimensions written to the record

First-Time vs. Repeatrepeat · 4th contact
Billing Disputecredit not posted
Expectation Gappromised credit missing
Staff Interaction Qualitypositive · this agent
Named Employeecurrent agent
Resolution Statuspending · note added

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 calls, chats, emails and bot conversations, or connect every channel. Every record comes back with typed Dimensions like resolution status, effort level, first-time vs. repeat, bot containment and staff interaction quality, written the same way every run, then joined to program, agent, queue and outcome data.

Read every record, not a sample
Join each record to program, agent, queue and outcome 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 resolution, repeat contact and CSAT, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal.

Which changes moved resolution for this client
Score every interaction, not a sample
Run the same method across every program
Produce the QBR without building it

dimension labs · ask

Example finding

Which changes moved resolution for this client?

Ranked drivers of repeat contact for each program, with the conversations behind each one, in one run.

Every interaction · program and agent joined

3.3 · Deliver

Ask for the report. Get the report.

Ask for the readout your clients 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