One dashboard for service requests and response times
that a department head can actually trust

The problem in municipalities and public services

A municipal office typically tracks service requests in one system, permit applications in another, and resolves a meaningful share of informal complaints, a pothole reported by phone, a noise complaint handled on the spot, with no structured record of how long resolution actually took. The office’s real response-time performance exists nowhere as a single number anyone can check.

The second problem is that without a joined picture, a backlog building up in one department goes unnoticed by leadership until residents start complaining loudly enough that it becomes visible anecdotally rather than through the data. By that point, the backlog has often been growing for weeks.

The third is reporting itself. A quarterly report to a council or oversight body usually means someone manually pulling numbers from several disconnected systems and reconciling them by hand, a process that takes real staff time and is prone to the exact kind of small errors that undermine confidence in the report once someone checks the math.

A fourth problem shows up when comparing departments or service types: without a common schema, comparing permit processing time to service-request response time is an apples-to-oranges exercise that usually gets skipped entirely, even though it is often the comparison that reveals where resources are actually misallocated.

What we build for municipalities and public services

A single PostgreSQL warehouse that joins your case-management system, permit system and any structured request logs into one place, with a daily sync and backfill so history is never lost. Each source gets a raw layer first, then marts built around the questions that actually matter operationally: requests by type and location, response time by department, and backlog aging so a request sitting unresolved for weeks is visible, not buried in an average.

Dashboards surface what a department head or an oversight body needs at a glance: response-time trends over time, backlog aging by department and request type, and alerts when a department trends toward missing a target response time, flagged while there is still time to act rather than after residents start complaining in volume. A reconciliation pass checks the warehouse numbers against what each department currently reports manually, so the dashboard and the department’s own records agree.

Typical integrations: your case-management or permit system’s API or export, a structured spreadsheet where no dedicated system exists yet, and a reporting tool or generated document matched to the format your council or oversight body expects.

How it works in 3 to 6 weeks

1. Audit. What exists across case-management, permit and request systems, what is already tracked, and what the real data quality looks like. One week.
2. Model. Which operational questions leadership must answer; the marts are designed around response time and backlog from the start.
3. Connectors and sync. Raw layer for each source, daily jobs, backfill, reconciliation checks against what each department currently reports.
4. Dashboards and alerts. Built with the department heads who will use them day to day, with backlog alerts tuned to leave time to act.
5. Handover. Documentation, access in your office’s name, and two to four weeks of adjustment once real data flows through.

What it costs

Typical results

Public bodies that reconcile case-management, permit and request data for the first time typically find that a meaningful share of real resolution times were invisible to existing reporting, since no single system had the full picture, an industry-wide pattern in the 15 to 30 percent range reported across comparable public-sector deployments. Offices with a working response-time dashboard commonly catch a department’s backlog trending upward within weeks instead of a full reporting quarter, once the numbers are checked regularly instead of compiled manually once every few months. These are typical ranges reported across the public sector, not a guarantee, since system count and existing data quality both move the number. Our own numbers are in the case studies linked below: a two-brand analytics warehouse with 1,025 automated tests and an AI analyst catching anomalies with zero false alarms is detailed in the analytics hub case study, and a factory ERP recovered from a lost cloud account and rebuilt self-hosted with cost accounting and plan-versus-actual reporting is in the factory ERP recovery case study.

Why Senator Media

We build these warehouses the way we build our own: a reconciliation pass against what departments already report, before any dashboard goes live, so leadership trusts the number the first time they see it. The price is fixed once the plan is agreed, you get a working demo every week during the build, and the warehouse and all access stay in your office’s own account.

If nobody can currently say which department or request type is quietly falling behind, that gap compounds every week it goes unmeasured. We would rather start with the tracking audit and show you exactly where the blind spots are than sell a full warehouse build before either of us knows what the real data actually looks like.

Pair this with an AI agent for municipalities and public services so resident-facing status questions get answered from the same clean data. See the full package breakdown on the analytics service page, or get a written plan with a fixed price for your office.