Operational Visibility
Every action becomes
a data point.
Sigmoid Analytica's workflow system logs every decision, retrieval, and action as structured operational data. That data surfaces here, so you can monitor performance, investigate exceptions, and decide where automation should expand next.
This is not a business intelligence tool. It is operational visibility into the automation system you have already deployed.
Requests today
+12%214
vs yesterday
Automated
76.2%163
automation rate
In review
17.8%38
of volume
Exceptions
6.1%13
within threshold
Resolution breakdown
Representative data. Actual figures depend on workflow type, volume, and configuration.
What most operations teams cannot currently see
Without a structured workflow system, the data that would answer these questions simply does not exist in a queryable form.
No structured record of where agent time actually goes
Without systematic classification, you can't tell which request types consume the most hours, or how that changes week to week.
No baseline to measure improvement against
If you have never tracked manual resolution time by workflow type, you have no reference point for what automation is actually changing.
Exceptions happen but are not categorised
You know automation sometimes escalates. You do not know which workflows escalate most, which exception triggers are most common, or whether the pattern is improving.
Automation decisions are not reviewable without diving into raw logs
When a workflow behaves unexpectedly, there is no structured way to review what the system retrieved, what it checked, and why it acted as it did.
Automation expansion is guesswork
Without data on which unautomated request types have the most consistent patterns, decisions about what to automate next are based on instinct rather than evidence.
No way to verify policy compliance at scale
When the system handles hundreds of requests, you need confirmation that responses stayed within policy, not a manual spot-check of a sample.
Sample output
This is what the system produces as a standard output
Every automated workflow generates structured records. The data below represents a month of operation for an ecommerce support team running return, cancellation, and address change workflows.
| Metric | Automated | Manual | Total |
|---|---|---|---|
| Total requests processed | 1,312 | 535 | 1,847 |
| Average resolution time | 2.4 min | 19.1 min | 7.3 min |
| Responses matching policy | 100% | 94% | 98% |
| Exception rate | 6.2% | — | 6.2% of auto |
| Escalated to agent review | 114 | — | 114 |
| Request type | Volume | Auto rate | Avg resolution | Top exception trigger |
|---|---|---|---|---|
| Return requests | 847 | 78% | 2.1 min | Outside return window (67%) |
| Order cancellations | 412 | 71% | 2.8 min | Post-dispatch (54%) |
| Address changes | 298 | 89% | 1.4 min | After cutoff window (81%) |
| Policy queries | 183 | 62% | 3.2 min | Ambiguous policy scope (44%) |
| Escalated / other | 107 | — | — | — |
Eight categories of operational data, all from the same workflow system
None of this requires a separate data infrastructure. It is produced as a direct consequence of the system logging every step of every automated workflow it runs.
The data is structured, queryable, and available from the first day the system runs a workflow.
Automation rate
Percentage of requests resolved without agent involvement, broken down by workflow type and time period. The figure that tells you whether the system is performing as scoped.
Resolution time, automated vs. manual
Side-by-side comparison of how long automated and manually handled cases take to resolve, per workflow type. It shows you the operational cost of handling things manually.
Exception rate and triggers
Which workflows generate the most human-review events, and what causes them. Identifies both process gaps and automation expansion opportunities.
Policy retrieval coverage
Which policy sections are retrieved most often, whether the relevant section was found, and whether retrieval patterns are consistent over time.
Agent review patterns
How often agents approve automated drafts unchanged versus edit or override them. A high edit rate on a specific workflow type usually points to a gap in the policy documents or the context being retrieved.
Request type distribution
What comes in, at what volume, at what frequency, and how that distribution shifts. The input data for any automation expansion decision.
Automation expansion signals
Request types not yet automated that show the highest volume and most consistent patterns. These are your clearest candidates for the next automation scope.
Structured audit trail
A reviewable record for any workflow run: what was retrieved, what was checked, what action was taken, and whether a human reviewed the output. Queryable, not just logged.
Decision support
The questions this data answers
These are the questions operations leaders and technical evaluators ask most often once a workflow system is in place. The data exists to answer them because every action was logged.
Is the automation performing as expected?
Compare resolution time and exception rate for automated vs. manually handled cases, by workflow type, week over week. Deviation from your scoped targets shows up immediately.
How do I know the system is staying within policy?
Policy retrieval logs record which section was used in each decision. If the wrong clause gets applied, or a policy section isn't found, it shows up in the logs before it turns into a customer complaint.
Where should we expand automation next?
Request type distribution identifies unautomated workflows with the highest volume and most consistent pattern. These are your lowest-risk, highest-return automation candidates.
An exception happened. What did the system actually do?
The audit log for any workflow run shows every step in sequence: what was retrieved, what eligibility check ran, what the system decided, and where the exception was triggered. You can review it in full.
How do I build the internal case that this is working?
Export structured performance data by workflow type, time period, and automation mode. Compare against your pre-automation baseline. The numbers exist because every action was logged from day one.
An agent keeps editing the automated drafts. Why?
Agent review patterns flag workflows where drafts are consistently edited or overridden. A high edit rate on a specific type usually means the policy documents need updating or the context retrieval needs tuning.
This is not a separate analytics product
The data becomes available because Sigmoid Analytica's workflow system logs every action it takes: what was retrieved, what was checked, what decision was made, and what happened as a result. That logging is not optional. It is how the system operates.
Operational Visibility surfaces that structured data in a form that is useful for monitoring, review, and planning, without requiring a separate data infrastructure, a BI tool, or a reporting engagement.
Where the data comes from
Workflow sequence decisions: which steps ran, in what order, and what triggered each
Policy retrieval records: which sections were fetched and used in each decision
Action logs: every system operation with its inputs, outputs, timestamp, and authorisation status
Approval records: which drafts were reviewed, whether they were approved unchanged, edited, or overridden
Operational visibility starts when the first workflow runs.
The data is a consequence of how the system works, not a separate add-on. If you want to see what this looks like for your workflows, start with a discovery call.