InfinityQS is built to watch quality data at enterprise scale. DatumKeep is built for what happens next — the investigation, the corrective action, and whether that action actually held.
Your priority is large-scale quality data collection and compliance monitoring across many plants and devices, and root cause analysis is already well served elsewhere in your organisation.
You want monitoring and root cause analysis in the same loop: every signal one step from a guided investigation that reuses your plant’s own solved failures, with CAPA tracked to closure.
InfinityQS is an established enterprise SPC platform focused on real-time quality data collection, monitoring and compliance across plants. Its strength is statistical process control operations at large scale.
DatumKeep is a quality intelligence platform for manufacturing: SPC, AI-guided root cause analysis, CAPA and a knowledge engine built from your own resolved cases. This page compares enterprise SPC operations against our process-quality module and what sits behind it. We are earlier than InfinityQS on scale and integration, and the first group below says so.
Side by side
Read the chips, not the sentences. We start with the rows where InfinityQS is the better answer.
| What | InfinityQS | DatumKeep |
|---|---|---|
| Where InfinityQS is stronger | ||
| SPC at enterprise scale | Yes Multi-plant collection and monitoring, proven over decades. | Partly Built for it; early-stage deployments. |
| Shop-floor data collection | Yes Established collection across lines, devices and operators. | Partly Guided import and a measurement API. No native device layer. |
| Native connectors into plant systems | Yes Established integrations across enterprise quality estates. | No Roadmap. CSV, guided import and a documented API today. |
| Where both do the job | ||
| Control charts and rules | Yes Mature SPC computation at volume. | Yes I-MR, X̄-R and X̄-S; all 8 Nelson rules on every chart. |
| Capability reporting | Yes Standard capability studies across the estate. | Yes Cp, Cpk, Pp, Ppk with confidence intervals, never bare numbers. |
| Where DatumKeep is different | ||
| When a signal fires | Partly Alerts and dashboards; the investigation happens elsewhere. | Yes One step into a structured, AI-guided investigation. |
| Root cause analysis in the same system | Not the focus Monitoring-first; RCA is not its centre of gravity. | Yes 5-Why, Ishikawa and 8D with cited evidence and human sign-off. |
| Past failures resurface | Not the focus Quality data history at scale; solution memory is yours to keep. | Yes Similar resolved cases surface in context, automatically. |
| Asking in plain language | Not the focus Dashboards and reports, built by your quality team. | Yes Ask a question; twelve tools it can call, none it can author. |
| CAPA to closure | Partly Quality workflows vary by product and deployment. | Yes Corrective actions tracked to closure with a named approver. |
| Did the fix hold? | Not the focus Outside a monitoring platform’s scope. | Off by default Solutions scored by whether the failure actually came back. |
// Off by default means shipped and enabled per workspace by an admin, not roadmap. Not the focus is not a defect — it means the capability sits outside that product's centre of gravity, as its own positioning describes it. Competitor rows are based on publicly available information; capabilities evolve on both sides, so verify details with each vendor.
What the monitoring and statistics engine ships
The comparable part, stated as specifics rather than adjectives. Monitoring is shipped and off by default — an admin turns it on per workspace — and everything else here is live.
- Five scheduled checks: Nelson rules, CUSUM/EWMA, trend-to-limit, capability and staleness
- The staleness check is the one that notices a sensor stopped reporting at all
- A routine is scoped to a parameter, a machine or a line, on a cadence you set
- Every run records an outcome — including the quiet ones
- All 8 Nelson rules, evaluated on every control chart
- Individuals (I-MR), X̄-R and X̄-S charts, selected by subgroup size
- Cp, Cpk, Pp and Ppk reported with confidence intervals
- Gage R&R (crossed, ANOVA method) with % study variation and ndc
- CUSUM and EWMA for small sustained shifts a chart rule misses
- Theil-Sen robust trend with a prediction interval on time-to-limit
- Hotelling T² with PCA and robust (MCD) Mahalanobis for multi-parameter drift
- A confirmed signal becomes an investigation in one step, prior cases attached
// Native connectors into an ERP or CMMS are roadmap, not shipped — see the first group above. Measurements arrive today by guided import or the measurement API.