Labor Guru builds the 
recommendations. Tenyks makes sure they're built on reality.

Every restaurant labour optimisation engagement starts with data. Tenyks makes that data continuous, objective, and already there when your consultants walk in.

Book an alignment call

Trusted by Leading Operational Leaders:

NVIDIA
INTEL
Tec Tel
Dunkin
PAR
Eagle Eye
McDonald's
Sub Way
The consulting is right. The underlying data isn't complete enough.

The consulting is right. The underlying data isn't complete enough.

Labor Guru has developed something restaurant operators genuinely need – the ability to assess how a workforce is deployed and identify exactly where scheduling, structure, and labour allocation are costing money.

The constraint is the same facing every practice that relies on in-person data collection: observations are episodic, self-reported numbers drift, and by the time a recommendation is built, it's already describing the past.

Better data makes every engagement faster and more defensible.

Tenyks connects to existing camera infrastructure in 15 minutes and surfaces continuous labour utilisation data – idle time by station, demand curves by hour, peak-period variance – across every location simultaneously.

Labor Guru's consultants walk into every engagement already knowing where inefficiencies are concentrated, which stations are consistently over- or under-resourced, and how patterns shift across dayparts and sites.

Tenyks handles the measurement. Labor Guru handles the expertise measurement alone can't replace.

Standards don't drift because the work was wrong. They drift because no one's watching.
Outcomes

How this partnership helps restaurant operators

Labour optimisation grounded in what's actually happening on the floor.

Operational Efficiency

Faster time to recommendation

Consultants walk in with data already collected. Discovery time collapses – clients see insights sooner and value materialises faster.

Multi-Location Management

Easier to defend

Recommendations backed by continuous, objective data – not a single observation window. Push-back from operators drops sharply.

More likely to hold

Post-implementation monitoring confirms improvements are working between engagements – so the next engagement doesn't start from drift.

Scalable Across Locations

Scales across every site

Every location generates data simultaneously – no additional observation overhead as the client's estate grows.

Process

How the Tenyks + Labor Guru partnership works

01

Discovery

Define client labour objectives. Led by whichever partner sources the engagement.

Discovery
Data collection
02

Data collection

Tenyks connects to existing RTSP camera streams and begins generating continuous labour utilisation data. 15-minute setup, no new hardware required.

03

Analysis & recommendations

Labor Guru builds precise restaurant labour scheduling models, identifies structural inefficiencies, and designs workflow changes grounded in what's actually happening on the floor.

Analysis & recommendations
Implementation
04

Implementation

Changes are embedded on-site by Labor Guru.

05

Monitoring

Tenyks tracks whether labour optimisation improvements hold post-implementation. Drift is surfaced automatically.

Monitoring
Who it’s for

Who this partnership serves

Multi-site QSR groups
Multi-site QSR groups

Chains where labour scheduling complexity compounds across locations.

Casual dining operators
Casual dining operators

FOH and BOH labour structures with measurable peak-period variance.

Franchise networks
Franchise networks

Labour standards enforced at the brand level across independent operators.

Drive-thru chains
Drive-thru chains

Operators where station-level staffing directly drives speed-of-service KPIs.

FAQ

Your questions, answered

Do we need to install new cameras?
How long does setup really take?
What data does Tenyks provide Labor Guru?
Is the video data secure?
Does this replace Labor Guru consulting?
Press

As featured in

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Let’s align

Let's run the loop together.

Book a 30-minute alignment call. We'll walk through the partnership structure, share the data model, and map it to your active engagements.

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