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Operating Intelligence for the Physical World

See what your systems can't.

NagaOS turns activity captured by your existing cameras into structured operational events — then connects them with POS and business data to reveal where time, capacity and profit are being lost.

Starting with multi-location restaurants.
TABLE 14
Event trace · Zone 03
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Lost seat-minutes
24
Confidence
94%
Illustrative product data

Your POS records the transaction.
NagaOS explains the operation.

Business systems know when an order was opened, changed and paid. They do not know what happened between those moments on the floor.

Existing systems see
Check opened
Items ordered
Payment completed
Revenue recorded
NagaOS connects them
Physical event
+
Business context
=
Operational intelligence
Physical operations contain
Party seated
First service contact
Table state changed
Cleaning delayed
Capacity remained unavailable
How NagaOS works

Observe → Structure → Connect → Act → Measure

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Current product · For restaurants

Three core metrics. One measurable baseline.

All values shown are illustrative interface content, not verified customer results.

Metric 01 · Seat-to-first-contact
02:35
Median seat-to-first-contact
vs location baseline+00:42
Confidence94%

Measure the time between a party being seated and the first meaningful service interaction.

Metric 02 · Departure-to-table-ready
07:14
Median departure-to-table-ready
vs target+02:18
Confidence91%

Measure how long capacity remains unavailable after a party leaves.

Metric 03 · Peak lost seat-minutes
124
Lost seat-minutes
Peak window19:00–20:30
EvidenceLinked events

Quantify usable seating capacity lost during periods of peak demand.

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Product · The intelligence layer

Every insight has evidence.

Insight → metric → timeline → evidence clip → confidence → review. The product is transparent and reviewable, never falsely certain.

Insight · Deviation detected
Reset delay increased during peak demand
Miami — Brickell
Dining Room 02
Departure-to-ready
09:31
Baseline
06:48
Deviation
+02:43
Observed 19:55:44 Est. lost seat-minutes 18 Confidence 94%
Evidence 03
operational evidence frame · no identity data
EventTable reset started
Timestamp19:55:44
ZoneDining Room 02
Confidence94%

Know why one location outperforms another.

Revenue shows that locations perform differently. NagaOS helps explain which physical processes create the difference.

Location A
Median first contact02:11
Median table reset05:48
Peak lost seat-minutes68
Location B
Median first contact03:02
Median table reset08:17
Peak lost seat-minutes131
The largest performance gap occurs between party departure and cleaning start during the 19:00–20:30 demand window.
Business outcomes

More throughput.
Less paid labor waste.
Less managerial waste.

An event is not the result. An insight is not the result. The business outcome is the result.

Request a Pilot →
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See the pattern across every location.

Compare similar shifts, zones and demand conditions to understand which operating patterns consistently produce better outcomes.

Comparable Friday dinner shiftsFirst contactTable resetvs network baseline
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Common bottleneck across underperforming shifts: cleaning start delayed under peak demand, not cleaning speed.
Privacy and trust

Process intelligence, not employee surveillance.

NagaOS is designed to measure operational processes and capacity — not identify, rank or discipline individual employees.

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Vision · Future direction

Toward a Physical Business OS.

NagaOS is beginning with a focused restaurant event model. Over time, the same operating intelligence layer can support richer process models, simulation, controlled automation and measurable feedback loops across physical businesses.

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Starting with multi-location restaurants

Make your physical operations measurable.

Run a focused pilot using your existing camera infrastructure and begin with three operational metrics that connect directly to service, capacity and performance.

Request a Pilot → Talk to the Founders