Dimension Labs

We published a new research paper today where we ran a study to prove why CX leaders using CSAT and NPS as main KPIs in their dashboards are running their VoC programs off of misleading metrics. We analyzed 8,880 real-world customer support conversations for a company, where only about 6% of customers ended with a CSAT rating (about industry average). Those responders averaged 3.62 out of 5. The 94% who never rated averaged 2.91 when enriched for predicted scores from the conversation. Across all customers who actually made contact with support, satisfaction is a full 0.71 points lower than the survey reports. One conclusion from this is that the CSAT dashboard isn't the Voice of the Customer, but the voice of the SATISFIED customer, and nearly everyone who had a worse experience (94%) left no rating. All of our customers at Dimension Labs have unified KPIs across 100% of their customer interactions leveraging our text dimensionality approach.

The agent ranked all 24 by low-star share, then isolated the drivers: order accuracy, wait times, rude service. A schema agent resolved the tables, a query agent wrote the SQL, a charting agent rendered it against a spec, and skills files kept the methodology consistent.

A preview of the Dimension Labs Agent analyzing data for 24 Chick-fil-A locations, no SQL needed.

What is Causal Intelligence? dimensionlabs.io/causal-intelli…

The rise of the language data engineer: dimensionlabs.io/blog/the-rise-…

80% of enterprise data is unstructured. Yet the Modern Data Stack is built for the other 20%. Learn why in 2025 that is changing: dimensionlabs.io/blog/the-missi…

We went deep into: 🔹 Prompt engineering techniques used by F500 companies 🔹 How to extract insights from CSAT surveys and live call transcripts 🔹 Building structured taxonomies for unstructured data sets 🔹 Using enums and prompt guardrails to reduce hallucinations

Dimension Labs Beta Testers Group This beta is a collaborative build environment — where expert prompt engineers, AI practitioners, and data scientists get hands-on with unstructured data and push the limits of what’s possible with LLMs.

5/ Ever wish you could just ask your data a question? Now you can! Our chat-based querying feature lets you interact with your data in a natural, intuitive way. No more deciphering cryptic queries or waiting for reports to run. Ask your question and get the answer you need!

4/ But we're not stopping there! Our AI-powered dashboards take data visualization to the next level. Get real-time insights and identify trends that would otherwise be buried deep within your unstructured data. Let AI be your guide to data discovery. 🤖 #AI #DataVisualization

12/30 Key Component 4: Hallucination Prevention. LLMs can sometimes generate incorrect or nonsensical information (hallucinations). We need strategies to mitigate this.

11/30 Prompt chaining allows you to automate multi-step processes and create more sophisticated AI applications.

✅ AI-powered churn prediction with customizable scoring ✅ Fine-tune insight extraction—choose your style, from ELI5 to hacker slang ✅ Dive deep into metrics, from raw transcripts to emerging trends ✅ Leverage AI to auto-generate relevant summaries and process optimizations

Dimension Labs flips the script on data analytics, giving you unparalleled control over how insights are derived and visualized. In this demo, we dissected T-Mobile support call data, tweaking the AI to explain customer issues in our chosen vibe.

All of this data feeds a customizable AI Health Score—so you can track real impact. With Dimension Labs: ✅ Spot user frustrations ✅ See drop-off points ✅ Detect hallucinations ✅ Auto-generate fixes ✅ Catch off-brand replies Learn more at dimensionlabs.io

LLM analytics ≠ uptime & token counts. Most teams track usage…But miss how the LLM actually performs for users. With Dimension Labs, we go deeper: —Operational metrics (tokens, volume, models) —Experience metrics (completion, sentiment, effort) 👇