Priority-level SLA reporting
Daily visibility into urgent case mix, under-one-hour performance, and missed-target risk.
For SMB and mid-market operations leaders
Replace fragile spreadsheet reporting with an ops system your team can trust.
I build warehouse-centered KPI pipelines, queue-health diagnostics, and forecasting views that show backlog, staffing risk, and SLA drift before the week goes sideways.
- 24 hourstypical response to qualified inquiries
- 5-10 daysfor a fixed-scope ops systems audit
- 30-45 daysfor a full reporting build and handoff
Operational proof
Real dashboards, real queue mechanics, NDA-safe examples.
These screenshots come from live operations work and are shown here because they help buyers understand the level of rigor behind the engagement.
24-hour failure-window diagnostics
Hour-level views that separate inherited backlog from real demand shocks.
Exception and QA latency visibility
Operational tables and monitoring views that surface silent queue drag before it cascades.
Who this is for
Teams with continuous intake, priority work, and real cycle-time accountability.
If work arrives all day, gets triaged, and leadership needs reliable numbers to staff and intervene, this model fits.
Customer Support
Logistics
Healthcare Ops
Field Services
Claims
Back-office Ops
Engagements
Clear start points, sober scope, and fixed-fee entry where it matters.
The audit fee is credited toward the build when we continue.
Step 1
15-minute fit call
Quick qualification call to confirm whether the problem is operational analytics, workflow design, or something else.
No cost
Step 2
Ops Systems Audit
KPI inventory, source map, trust gaps, edge-case definitions, and a fixed quote for the build.
Fixed fee from $2,500
Step 3
Build and handoff
Automated reporting pipeline, monitored refreshes, dashboards, and ownership docs your team can run.
Most builds start at $10,000
Forecasting and capacity modeling work is scoped after the audit and typically starts at $15,000 when it is layered on top of the reporting foundation.
Founder-led delivery
The person on the first call is the person who defines the metrics, writes the SQL, and owns the handoff.
What buyers get
- Warehouse-centered builds instead of BI-layer band-aids
- Queue-health logic that distinguishes demand spikes from capacity failure
- Metric dictionary, monitoring, and handoff docs included by default
Why it matters
Operations teams do not need more reporting clutter. They need stable definitions, monitored refreshes, and a single source of truth that holds up in executive conversations.
Case studies
Two examples of the mechanics that transfer across ops teams.
Automated TAT reporting pipeline
Problem: turnaround reporting was slow, inconsistent, and dependent on fragile BI-only access.
Build: raw events into standardized BigQuery tables, curated operational slices, and leadership-ready dashboards.
Result: daily KPI visibility with consistent definitions and less manual reporting overhead.
Weekend SLA breakdown analysis
Problem: a multi-hour SLA failure was blamed on “bursty demand.”
Build: arrival events linked to staffing context with hour-level diagnostics for queue health, volatility, and net velocity.
Result: recurring failure windows improved without adding staffing cost by shifting coverage before queue collapse.
Start the conversation
Describe the reporting problem in three sentences.
I’ll reply with whether this is a fit, what the likely workstream is, and the fastest next step.