Telecom company
The same CX report, byte for byte, every time you run it
Auditable NPS, CSAT, CES, tag and sentiment reporting built without a single model call.
Trustworthy CX analytics.
What changed
- Minutesfrom two CSV exports to two finished reports (estimate)
- 0 hallucinated metricszero LLM calls anywhere in the path (estimate)
- Reproduciblesame input, byte-for-byte the same output (estimate)
01The challenge
Where it started.
The customer-experience team receives raw experience data as CSV exports from the data warehouse and has to turn it into readable reports on NPS, CSAT, CES, tag breakdowns and sentiment, for the team and for leadership. Building those by hand every cycle is slow and error-prone.
In detail
Every hand-built or AI-built number invites doubt. If a metric on a leadership slide cannot be traced back to the source rows and reproduced, someone will question it, and the whole report loses authority.
Quote
“If a number lands on a leadership slide, they can open the source rows and get the exact same figure. That is the whole point.”
02What we built
Agents and workflows, on one platform.
We treated this as a reporting problem, not an AI problem. The goal was numbers a leadership team can stand behind, so we removed the one component that cannot be audited: the model.
Inputs
Deterministic processing (code)
Outputs
Why the Elevon platform
Zero-LLM by design
Every calculation and the HTML rendering run deterministically in code. No model call sits anywhere in the path, so there is no possibility of a hallucinated metric.
Reproducible output
The same two CSV exports always produce byte-for-byte the same reports. Anyone can rerun the suite on the source data and get an identical result to verify a number.
Explicit guardrails
A minimum-answers threshold suppresses any metric with too few responses, and an NPS alert threshold flags scores that need attention. The rules are in code, not in judgement.
03Rollout
Step by step.
We started from the two CSV exports the warehouse already produces and mapped every field the CX team needed: response scores, categories, free-text tags and sentiment. No new data pipeline, no new source of truth.
How it runs
Then we wrote the metric logic and the HTML renderer as plain deterministic code, added the minimum-answers and NPS-alert thresholds, and wired it into the existing reporting cycle so the team runs it whenever a fresh export lands.
In practice
The suite runs on demand inside the team's normal reporting cycle. A fresh export goes in, the two HTML reports come out in minutes, and because the output is deterministic, rerunning on the same file is a valid way to verify any figure rather than a source of new variance.
04Results
What changed.
The reports now come out of a repeatable process instead of a manual one, and the numbers hold up to scrutiny.
- Minutesfrom two CSV exports to two finished reports (estimate)
- 0 hallucinated metricszero LLM calls anywhere in the path (estimate)
- Reproduciblesame input, byte-for-byte the same output (estimate)
Figures are illustrative and shown to convey scale; replace them with the client's real numbers before publishing.
- Two CSV exports turn into two interactive HTML CX reports in minutes, down from a slow manual build every cycle.
- NPS, CSAT, CES, tag breakdowns and sentiment are all computed in code, so there is no hand arithmetic left to get wrong.
- With zero LLM calls, no metric can be hallucinated; every figure traces directly back to the source rows.
- The minimum-answers threshold quietly suppresses metrics with too few responses, so nobody reports a number that the sample cannot support.
- The NPS alert threshold flags scores that need attention as part of the same run, without anyone watching for them by hand.
Why it worked
We scoped it as reporting, not AI. Because there is no model in the path, there is nothing to hallucinate and nothing to explain away, and a deterministic renderer means the report is a pure function of its input.
The guardrails are explicit and live in code, not in a person's judgement, so the minimum-answers and NPS-alert rules apply the same way every single run and leadership can reproduce any figure on demand.
Quote
“We removed the one thing we could not audit. What is left is arithmetic anyone can check.”
Next step
Your process could be the next case.
A 30-minute call. You tell us about one process, and we say whether it fits the platform, a product build or a consulting project.