Platform
The AI agent for customer conversations
Know why your metrics move, using the conversations your analytics can't touch.
Built for speed
Weeks of analyst work from one question. Answers in minutes, not a ticket queue.
Built for automation
Stop staring at charts hoping a pattern shows up. Ask why and get evidence you can act on.
Built for scale
No more sampling a fraction of your conversations. Get 100% coverage, automatically.
Any source. Any channel. Live or historic.
Connected in minutes, not months. Upload a file or connect Zendesk, Intercom, Salesforce, Qualtrics, Snowflake and more.
All integrationsBuild the Meaning Layer with Dimensions on every record.
Churn risk, buyer intent, predicted CSAT, root cause and more. A human-in-the-loop process keeps every field honest.
Browse example DimensionsWe'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.
- 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.
Ask why. Get causal answers, charts and evidence-backed reports on demand.
Agent-aided analysis that writes the queries, tests the drivers and ships the report. Every claim links back to the transcripts behind it.
How the causal analysis works
The enrichment pipeline
Enrichment at scale. Every day.
Parallel workers read every record and write Dimensions with the same schema on every run, so this month compares cleanly to last year. Results go to the agent, your warehouse and your BI tools.
Coming soon
Your Dimensions, inside Claude and ChatGPT.
We’re building MCP servers for the major AI assistants. Ask in the assistant your team already uses and get answers built on the same enrichment, governed data and causal tests.
dimension_labs.find_drivers{ "outcome": "churned", "population": "enterprise accounts",
"controls": ["tier", "tenure", "ticket_volume"] }- alt_vendor_evaluation6.5×p<0.001
- promise_not_delivered4.7×p<0.001
- repeat_issue2.6×significant
What you get
The layer your stack never had.
Unified Meaning Layer
Every channel normalized into one data model. One source of truth for what your customers are saying.
Per-record Dimension extraction
Every record enriched with structured Dimensions. Intent, sentiment, root cause, risk. At scale. No sampling.
Business-native context
Models calibrated to your industry, your taxonomy and your business logic. You define the meaning. We extract it.
Full explainability and lineage
Every insight traces back to the source record, the extraction logic and the model version. If you can't explain it, you can't govern it.
Versioning and governance
Meaning evolves as your business does. Full version control, change history and audit trails, built for compliance from day one.
Built for agents and knowledge graphs
Agents can't reason over raw text. Graphs can't build from noise. Both need structured, governed meaning at scale.
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.
FAQ
What does Dimension Labs actually do?
You sit on millions of customer conversations you can't use for decisions. We join that language to CRM, usage and financial data, then an AI agent answers why metrics moved and writes the report.
Why can't I just use ChatGPT for this?
ChatGPT can talk about a file you upload. It can't enrich hundreds of thousands of conversations, join what people said to whether they churned, or ship a sourced executive report. We build the Meaning Layer first, then the agent runs on it.
How accurate are the insights?
Every insight is traceable back to the exact conversations that produced it. We extract structured fields from every record through validated pipelines, with classification accuracy exceeding 99%. When a report says churn risk spiked in a segment, you can click through to the verbatims and verify it yourself. One customer described being "burned by ChatGPT" before switching to us. They stayed because every claim we surface can be sourced and validated.
What kinds of questions can Dimension Labs actually answer?
Questions your current tools can't. Which conversation signals predict churn before usage metrics decline. What themes in support tickets correlate with renewal outcomes. Which product complaints are statistically significant and tied to real revenue impact. What changed since last month and why. We recently generated a full executive-level analysis for a professional sports franchise, correlating fan feedback with satisfaction scores, identifying the exact drivers of low experience, and delivering board-ready recommendations, all from a single question to our AI agent.
What data sources do you support?
Any text-based conversational data: call transcripts, live chat and chatbot logs, support tickets, open-ended survey responses, CRM notes and emails. We also ingest structured data like CRM records, product usage, financial data and satisfaction scores so you can correlate what customers say with what they actually do.
How is this different from the analytics tools we already have?
Your current tools can tell you sentiment is down or ticket volume is up. They can't tell you why, which customers are affected, or what to do about it. Dimension Labs enriches every individual conversation, connects patterns across your entire dataset, joins them with your structured business data, and lets anyone ask a question in plain English and get an answer backed by evidence. One enterprise customer spent six months trying to build this internally with their data team and couldn't operationalize a single repeatable pipeline. They were live with us in two weeks.
Do we need to replace our BI stack?
No. You can use Dimension Labs standalone with built-in querying, auto-generated charts, dashboards and full executive reports. Or plug structured output into Snowflake, Databricks or BigQuery so your data team can query it alongside everything else they already use. We're not replacing your stack. We're giving it a data source it's never had access to.
How long does implementation take?
Most customers are live within one to two weeks. Enrichment begins immediately once your data is connected. Customers routinely tell us what used to take a research team weeks now takes a single question to our AI agent.
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.