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Every team watches a number it has to keep in range. Most react to random blips and miss the slow drift that actually costs them.
Defect rates, response times, error rates, delivery times, conversion: every operations team has a handful of numbers that must stay in range. And every operations team has had the same two bad days. The day they reacted to a spike that was just noise. And the day they missed a slow drift until it became a problem. Statistical Process Control answered this question in 1924, with rules that separate real signals from random variation. It has lived in quality-management software and consulting engagements ever since, while most teams still eyeball a dashboard.
A Statistical Process Control agent closes that gap. Not a chatbot that draws a chart once. An agent that watches your metric continuously, applies the full set of run-detection rules, and alerts you only when something has genuinely changed, with the rule that fired shown. This isn’t a nice-to-have feature. It’s the difference between catching a problem early and explaining it afterwards.
Stop thinking of it as a dashboard. Think of it as a standing quality analyst that watches your numbers every hour, and only speaks up when it matters.
A Statistical Process Control agent, built the right way, gives you:
Pick a period when the process was behaving normally and the agent calculates the limits from it. No arbitrary thresholds that someone guessed three years ago.
Not every wobble. The agent tells normal variation apart from a real shift, so your team stops reacting to noise and stops ignoring real problems.
Many tools only check whether a single point crosses a limit. This agent also looks for patterns: several points in a row on one side of the average, steady trends, and clusters near the limits. These patterns often show a problem long before a single point crosses the line.
Some problems creep in too gently to cross any limit for weeks. Dedicated methods for small sustained shifts spot them early.
No black box. Every alert names the rule that fired, how far from normal the reading is, and what it means, so a supervisor can judge it in seconds.
Ask how capable a process is of staying within specification and get a capability index (Cpk) back, calculated from your own data.
Defect rates, support response time, error rates, latency, delivery SLAs, sales-cycle length. If it’s a number over time, it can be monitored.
This is what separates a process-control agent from a dashboard: it doesn’t just show you the number. It tells you whether the number has actually changed, and why. It supports your team’s decisions. Your operators decide whether to stop, adjust or carry on.
This isn’t built for statisticians. It’s built for the people tired of staring at dashboards and wondering whether today’s blip is a problem.
If you’re looking for a system that stops your line or rolls back a release on its own, this isn’t that, and it won’t pretend to be. It’s decision support: it tells you when a process has genuinely changed and why, and your operators own the stop/go decision. And it’s only as good as the baseline you give it, so the agent helps you pick a stable baseline period and starts with conservative limits. This is for people who want alerts they can trust and explain.
| Generic AI Agents / Chatbots | Us | |
|---|---|---|
| Consistency | Different answer each time you ask — probabilistic, hard to trust twice | Deterministic — same input, same output, every time |
| Cost to run | Re-processes your entire dataset through an LLM on every query | Purpose-built models, minimal token usage — cheap enough to run on the open book; pipelines larger than 500 leads can score locally |
| Reliability | A 0–100 score invented in the chat — confidently wrong when the closed sample is thin | Fits conversion from your outcomes, shows a range when it should, and refuses to fit when there isn't enough data — honesty instead of a bluff |
| Data privacy | Your data often leaves your systems, sits with a third party | Full SOC 2 compliance — no data ever leaves your premises |
In short: Other agents give you an answer. This gives you conversion probabilities, money at risk, and a daily queue you can defend, reproduce, and put in front of your security team without a fight.
We didn’t build new models and hope your processes behave. Everything underneath is the statistical process control that factories have relied on for a century, now available for any metric.
| What It Does | Method | In Use Since |
|---|---|---|
| Separates normal variation from a real change in the process, using limits set from a stable baseline | Shewhart control charts | 1924 |
| Detects patterns beyond a single point: runs on one side of the average, trends and clusters near the limits | Western Electric rules (and Nelson rules) | 1956 / 1984 |
| Detects small sustained shifts that a standard chart is slow to catch | CUSUM | 1954 |
| Detects small sustained shifts by weighting recent readings more heavily | EWMA | 1959 |
| Measures how capable a process is of staying within specification | Process capability index (Cpk) | 1980s |
Why this matters to you: every layer here has been published, cited, and used on real production data for decades before we ever touched it. We didn’t ask an LLM to “eyeball whether this looks off.” We apply proven rules to your measurements, the same way a quality engineer would: reproducible, defensible, and the same result every time you ask. Teams that apply statistical process control well have reported scrap and rework reductions in the range of 10–25%. Your results will depend on your process, your data and how you act on the alerts.
That’s what “deterministic” actually means. Not a marketing word. A math word.
ChatGPT and other LLMs are built to talk. Ask them whether your defect rate is drifting and they generate the most plausible-sounding answer, which is exactly why they’re brilliant at explaining control charts and unreliable at applying them to your data every hour.
Here’s the practical difference:
The short version: ChatGPT can explain a control chart beautifully. It can’t reliably watch your process tonight and tell you the moment it genuinely drifts. That’s the gap this agent exists to close.
In plain terms, it takes your measurements over time and does three things continuously:
That’s the whole point of the agent: it turns a dashboard you have to keep watching into alerts you can trust.
No, this isn’t an engineering project. If you can export a spreadsheet of measurements with dates, from a QC log, a production system or a metrics tool, you already have everything the agent needs.
Just follow the step-by-step tutorials we provide, and you’re up and running in minutes: no models to train yourself, no code to write. There’s a free tier you can try without a sales call. Not sure which period to use as your baseline? The agent helps you pick a stable stretch of data and starts with conservative limits.
The short version: if you can export a spreadsheet, you can have this running today. Follow the step-by-step tutorial.
Yes. This is built to be fully SOC 2 compliant, and your data never leaves your premises.
The agent is stateless: your data is processed in the request and never stored, and each user signs in with their own OAuth login. There’s no export to a third-party cloud and no black-box vendor holding your records. Process measurements are usually non-personal data, but this was built with data protection as a requirement from day one, not an afterthought. You decide what is sent, and the agent only needs values and timestamps.
The short version: you get the intelligence, not the exposure.
It’s based on statistical process control methods that have been researched, tested, and used in production for decades, not a new algorithm hoped to work.
The agent looks at three things: how your process normally behaves (the baseline), how far each new reading is from normal, and whether recent readings form a pattern that real processes show when something has changed. From that it decides whether the process is in control or has genuinely shifted.
The short version: it isn’t guessing from patterns in text, like a general AI chatbot would. It’s calculating, using methods that have had decades to prove themselves.
Non-deterministic means: ask the same question twice, get two different answers. This is how most general AI chatbots work. They generate the most likely-sounding response each time, even when nothing about your process changed. Fine for brainstorming a root-cause list. Risky when the answer decides whether anyone investigates.
Deterministic means: same data in, same answer out, every single time. If an alert fires, it’s because your measurements changed, not because the model rolled different dice.
What we use: deterministic, every time. The verdicts this agent gives you come from fixed statistical rules, not a language model guessing at what looks normal. That’s what lets you show an alert to a supervisor or an auditor and know it holds up if someone re-runs it tomorrow.
The short version: we don’t give you an alert you have to hope is right. We give you the same, defensible verdict, every time you ask.
It’s built so you don’t. Alert fatigue is the main way monitoring fails, so the agent starts with conservative limits and only alerts when a recognized rule fires. You can tune it as you learn how your process behaves. Every alert states which rule fired, so you can decide quickly whether it deserves attention.
The short version: fewer, better alerts, each with a reason attached.
Ready to Stop Staring at Dashboards?
You already have the data. A spreadsheet of measurements over time has everything this agent needs; you just need to know how to connect it. We’ll walk you through exactly how, step by step.
How to Build an AI Agent →No credit card. No Six Sigma consultant. Just your measurements and a few minutes.
