TableIQ turns the security cameras and POS you already run into objective speed-of-service measurement — time-to-first-drink, order-to-table, greet, seat, and check-drop — across every location.
You can't fix a number you don't have
Most operators have a gut feel for whether service is slow, but no actual clock on greet, first-drink, or order-to-table — so problems only surface in bad reviews and walked tabs.
POS, KDS, and checklists don't time the dining room
POS knows when an item was rung and KDS knows when the kitchen bumped it, but neither sees whether the food reached the guest or how long they waited to be greeted. Checklist apps track tasks, not seconds, and rely on staff self-reporting.
Measure the floor with the cameras already on the wall
TableIQ starts a timer at the POS ring and ends it when a vision-language model sees the item hit the table, while reading greet, seat, first-drink, and check-drop straight off existing CCTV. You get objective, location-by-location speed-of-service numbers — validated by human reviewers — without buying or installing anything new.
What operators ask
TableIQ measures restaurant speed of service using the security cameras and POS you already have — no new hardware, no KDS, no scheduler. A timer starts when an item is rung into POS and ends when a vision-language model detects it reaching the table, giving you true order-to-table time. Floor and bar events (greet, seat, wait time, first-drink, check-drop, empty-glass dwell) are read directly from existing CCTV, so time-to-first-drink needs no POS wait at all. When several cameras cover one table, the readings are de-duped automatically. Most tables are measurable on the cameras you already have, and human reviewers validate the AI's accuracy in the first week. Use it for live speed-of-service tracking, retroactive investigations (search POS plus camera to diagnose a bad review or a slow shift), and cross-location rollups with role-based reports for GMs, regional directors, and COOs. Pricing is per metric, per location, with a free first-month pilot. It works alongside your POS, KDS, and scheduler — it does not replace them. For the product itself — what TableIQ measures and how deployments run — see https://www.tableiq.io/solutions/speed-of-service.
Frequently asked questions
Three, in practice. A manager with a stopwatch (accurate but samples one station on one shift and costs labor every time). KDS/POS timestamps (automatic but they time the bump, not the delivery, and staff can game them). And camera-plus-POS measurement, where the order ring starts a timer and camera detection of the item reaching the table stops it — automatic, continuous, and not gameable from the floor.
POS and KDS reports time button presses — when an order was rung and when someone bumped it. They can't see whether the food or drink actually reached the guest, how long it sat in the window, or anything the floor does (greets, seating waits, check-drops). Camera-based measurement times the events guests actually experience.
Yes — full-service and bar concepts are the primary use case. Drive-thru timing is a solved problem with loop timers and QSR tools; dine-in order-to-table, time to first drink, greet time, and check-drop have historically been unmeasurable without a stopwatch. That's the gap TableIQ closes.
TableIQ is priced per metric, per location, and the first month is a free pilot. Book a call at tableiq.io/schedule for exact numbers on your setup.
Each timing is anchored to a POS event on one end and a visually detected delivery on the other, and human reviewers validate the AI's readings during the first week of a deployment. Because no one on the floor can bump, skip, or pre-enter anything, the numbers stay comparable across staff, shifts, and locations — which is what makes cross-location rankings meaningful.
Keep reading
How to Track Speed of Service Without a POS or KDS
Read Speed of serviceHow to Measure Time to First Drink at the Bar
Read Speed of serviceBar Ticket Times: Benchmarks, and How to Actually Measure Them
Read Buying guidesThe Best Restaurant Video Analytics Software in 2026, by Use Case
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