Injury-Risk Forecasting & Load-Management AI Agent

What this agent does

Reports what happened, not what will happen

Session and match load from the squad's wearables, GPS units, gym systems and the athlete's own entries.

Normalisation of units, session types and time zones, so one measure means one thing across the squad.

Reason

Descriptive measures — volume, intensity, distribution and change against the athlete's own recent range.

Squad and position rules configured with your performance staff, applied the same way to each athlete.

Ratios reported as one descriptive measure among several, never as a threshold that moves an athlete.

Decide

Flags where a measure sits outside the athlete's own range, with the window and the inputs shown beside it.

Routing to the named performance and medical staff who hold the athlete's availability.

Out

Retention of the measures, the window, the flag and the staff read against the athlete's load record.

Execute write actions only inside the approval boundaries agreed during implementation.

→ Product statement

The agent describes load; named medical staff decide availability, and the load record is walled off from contract and selection work.

Example workflow

One athlete week, sessions to read

AgentHuman

1Session data receivedGPS file, wearable sync, gym log or the athlete's daily entry

2Measures assembledVolume, intensity, distribution and the athlete's own range, each with the device behind it

3Load describedMeasures, the window they cover, flagged lines and confidence

4Controls appliedInput-completeness checks, squad and position rules, personnel-wall checks and confidence threshold

No human action required

Stages 1 to 4 run unaided, and no athlete moves at any of them — the agent is describing, and the medical lane opens at the confidence gate.

5DecisionBranches at the confidence threshold

High confidence

Goes to the named medical staff to read.

Low confidence

Adds a data-quality check first.

Medical read

The flag is held with its measures, its window and the confidence.

Acknowledge · Annotate · Escalate to physician

Read — logged against the athlete▼

6Squad systems updatedOnly where write access and the medical read allow it

7Outcome evaluatedWithdrawn flags, staff annotations, input completeness and corrections after the fact

Withdrawals

Each flag staff withdraw is counted in the evaluation.

What should not run autonomously

Human approval stays in control

Outside the boundary — human approval required8 items

Changing an athlete's availability, session or minutes.

Clearing an athlete to return to play.

Any load data reaching a contract, transfer or selection decision.

Naming an injury, or attributing a flag to one.

Automation boundaryAgent acts unaided

✓Compute descriptive load measures from the squad's own sessions.

✓Carry each measure forward with its window.

✓Apply the squad and position rules you configure.

✓Raise a flag for the named medical staff, and hold it with them.

Any write happens inside the boundaries agreed at implementation, never to availability.

Overriding a mandatory removal-from-play criterion.

Enrolling an athlete in a device, or revoking that choice.

Releasing a load record outside the medical wall.

Changes to thresholds, squad rules or routing.

Example output

One athlete week, annotated

Everything the agent reports is attached to the sessions it was measured from.

Load output · single athlete weekIllustrative example

Athlete

Reported measure

Window

Source of record

Confidence

Reader

Midfielder, in season

High-speed distance above this athlete's own recent range

Rolling week

GPS units and session log

88%

Named medical staff

As receivedMeasured from the squad's own session records — nothing on this side is a prediction.

Inputs used GPS session file Gym session log Athlete daily entry

Why this is flaggedIt says where the load sat against this athlete's own range, not what comes next.

ActionAcknowledgeAnnotateEscalate to physician

What the score decidesBelow the configured threshold the flag picks up a data-quality check before staff see it.

Value

Where AI adds value

The same four claims, placed at the point in the workflow where each one applies.

Where the value landsValue 01 – 04

All logged sessionsFrom the squad's own records

03Description

Describe from the record

Draw on the sessions on file and the squad and position rules your performance staff configured.

01Approved path

Describe load, do not predict

Routine load reporting arrives already assembled, by athlete and by window.

02Human review

Send the read where the load moved

Flagged measures and low-confidence records are marked, so the staff read starts where the change sits.

04Build an evidence trail

The measure, the window it covers and the staff who read it stay on the athlete's record.

Integrations

Typical integrations

Five system groups connect to the same agent. Which of them are in scope is decided in discovery.

Athlete monitoringCatapult · STATSports
Polar Team · WIMU

Athlete managementKitman Labs · Smartabase
Teamworks · AMS exports

Medical recordsInjury and rehab notes
Availability status

Agent

Injury-risk and load monitoring

Reads the sessions
Describes the load
Holds for medical

Strength and conditioningTeamBuildr · Bridge
Force plates · velocity

Observability & evaluationOpenTelemetry · Langfuse
Supported monitoring/evaluation sources

Integration availability depends on the client's existing systems and API access.

Agent controls

Six layers between the model and the athlete

The layers sit one inside the next. What none of them catches is in the map below.

L6 · Outermost — last line of defenceInward → L1 · closest to the model

L6Rollback / safe modeFall back to raw load reporting when evaluation or production signals degrade.Roll back

L5Version monitoringTrack model, prompt, squad-rule and threshold-configuration changes.Track

L4TraceabilityRecord the inputs, the window, the measure, the flag and the staff read.Record

L3Medical holdHold each flag with the named medical staff; it governs who reads it, not whether it was worth raising.Gate

L2Policy guardrailsTest each record against the personnel-data wall and the squad rules; a failure returns the record.Restrict

L1Confidence thresholdsRoute low-confidence records to a data-quality check before staff see them.Require review

Model coreMeasure produced — load, window, flagged lines and confidence

L1 – L2Test whether a measure may stand

L3Puts the read in medical hands

L4 – L5Hold the load record the flag was drawn from

L6Falls back to raw load reporting when signals degrade

How Nestack evaluates it

Evaluate the monitoring workflow — not only the finished measure.

Coverage runs the whole depth of the workflow, and every layer is cut by slice.

Surface — the measure the staff read

Depth of coverage ▼

E1Final-output evaluationDid each reported measure match the session data behind it?

E2Step-level evaluationDid the agent use the right athlete, window and squad configuration?

E3Tool evaluationDid it read the correct athlete record and the correct device file?

E4Confidence calibrationDo low-confidence records actually carry more input gaps?

E5Slice evaluationHow does performance change across specific squads and positions?

E6Business outcomeHow many flags were withdrawn by staff or corrected after the fact?

Floor — the outcome the club answers for

Failure modes

Where each failure originates in the agent

Seven failure modes, placed at the stage each one originates.

Agent lifecycleDirection of processing →

01 · Retrieval1 mode

HL-03

Unsynced device record

Load read from a unit that did not sync after the session.

Stage gathersSession files, gym logs, athlete entries and squad rules

02 · Reasoning2 modes

HL-04

Causal reading of a ratio

A descriptive measure is written up as a cause of injury risk.

HL-06

Range built on thin history

An athlete's own range is drawn from too few recorded sessions.

Stage proposesLoad measures, the window they cover and confidence

03 · Tool / write2 modes

HL-02

Flag worded as an instruction

A record reaches staff reading as a change to the session.

HL-05

Record crosses the wall

A measure is written to a system that touches personnel work.

Stage writesOnly where write access and approval policy allow it

04 · Output1 mode

HL-01

Availability implied

The report reads as though the athlete had been cleared or held.

Stage returnsThe measure the medical staff read and record

05 · Change / Version1 mode

HL-07

Silent threshold drift

A model or rule change moves what the agent will flag.

Stage tracksModel, prompt, squad rules and threshold config

Sev-1 · load data reaches personnel work Sev-2 · a wrong measure reaches the staff read Sev-3 · inputs degrade, record routes to check

See the complete Sports & Fitness failure-mode catalogue →

Affected slices

A squad average hides where flags fail

Injury is rare in any one athlete week, so a squad-wide withdrawal rate stays low while a few cohorts absorb most of the wrong flags. Nestack reports the withdrawn-flag rate by slice, not only in total.

Slice performance — reported separately, not only in aggregateIllustrative example

Slice	Failure rate	Lift	Status
Athletes back from rehab	8.0%	3.4×	Review
Congested fixture blocks	6.1%	2.6×	Review
New squad arrivals	4.2%	1.8×	Watch
Settled squad, full history	1.6%	0.7×	Normal

Bar: withdrawn-flag-rate lift vs. settled-squad baseline · scale 0–4.0× · tick marks the 2.0× review threshold 2 of 4 slices over threshold

Evidence-linked improvement

A cycle ends in a test, not a note

The cycle ends in a regression case, not in a meeting about what happened. That suite is what the next flag raised against a squad is measured against.

Improvement cycle · five stagesSwitchback — the path turns at Improve and returns at Learn

01Detect

Withdrawn-flag rate rises in one squad slice.

02Diagnose

Not a model question first — staff read the device records behind the flags until one cause holds.

03Improve

Every change is versioned against the flags that exposed it.

04Verify

A failing case holds the release back.

05Learn

The case joins the suite for good, and the flag thresholds are revisited.

Learn → DetectThe return edge. The next detection runs against a suite one case longer.

Typical build scope

Twelve workstreams across six weeks

The build scope read against the delivery timeline. Week structure follows the six-week plan — discovery, sources, load workflow, evaluation, integration, then production validation and handover.

Workstream Week 1Week 2Week 3Week 4Week 5Week 6

01Load workflow discovery and boundary definition.

02Monitoring and AMS assessment.

03Squad, position and flag-threshold rule mapping.

04Session ingestion and measure normalisation.

05Load-description logic and input binding.

06Confidence scoring and flag routing.

07Medical read workflow.

08Athlete-management-system integration.

09Flag-calibration cases.

10Guardrails and disclosure controls.

11Load-trail instrumentation.

12Deployment, documentation and Agent Care handover.

12 workstreams · 6 weeks · bar shows the weeks a workstream is active — several run in parallel Final scope and sequence confirmed in discovery

Engagement tiers

What each tier includes

Rows are the capabilities named in each tier's scope. Higher tiers include everything below them.

Capability✓ in scope · — not at this tier PilotOne squad, one season ProductionProduction athlete systems AdvancedMultiple squads / sites

Introduced at Pilot

Description to your sessions and rules✓✓✓

Medical read✓✓✓

Flag-calibration baseline✓✓✓

Introduced at Production

Reporting by squad and position—✓✓

Staff read workflow in your systems—✓✓

Approved write-back—✓✓

Athlete-management-system integration—✓✓

Introduced at Advanced

Multi-league and multi-sport rules——✓

Multi-stage medical sign-off——✓

High squad volume——✓

Multi-squad load controls——✓

Build price From $5,000 From $8,000 Custom quote

Final build priceConfirmed after discovery based on integrations, workflow complexity, transaction volume, approval controls and deployment requirements.

Separate from buildBuild pricing is separate from recurring Agent Care, which covers managed monitoring, evaluations, incidents and verified improvements after launch.

What we need from you

What you bring, and what we build with it

Each input maps to a piece of build scope and a week in the delivery timeline.

You bringWe build with it

01Your session sources and athlete record structure → Session ingestion and measure mappingWeek 1

02Representative recorded training blocks → Load-description baseline, range fitting and input bindingWeek 2

03Your squad rules and configured thresholds → Squad, position and threshold rule mappingWeek 1

04Access to relevant APIs, feeds or exports → Monitoring, AMS and medical assessment, then integration setupWeek 2

05Flags that should not have been raised → Calibration cases and the evaluation suiteWeek 4

06What must reach medical staff before anything changes → Confidence scoring, flag routing, guardrails and disclosure controlsWeek 3

07Named medical staff to read the flags → Medical read workflow, then pilot and production validationWeeks 5–6

Nothing else is required Deployment, documentation and Agent Care handover are ours.

Delivery timeline

Four phases across six weeks

The bands follow the real work, which is why evaluation and pilot share the fifth week.

Phase W1W2W3W4W5W6

Discovery W1

Build W2 – W3

Evaluate W4 – W5

Pilot & Launch W5 – W6

Week focus W1Load workflow discovery, rule mapping and the automation boundary W2Source integration and the load-description baseline W3Load workflow, confidence logic and medical read controls W4Evaluation suite, personnel-wall checks and failure-mode testing W5AMS integration, a pilot squad and targeted corrections W6One training block monitored under the medical staff, then handover

Reading the bandA bar covers the weeks its work is named in, and nothing else. The week 5 overlap is real, not padding.

At the end of W6The block closes validation and Agent Care assumes monitoring.

DurationSix-week plan shown · typical delivery 4–6 weeks depending on scope confirmed in discovery.

Next step · Sports & Fitness AI agent

Build a load-monitoring agent around your medical staff's authority.

Show us your monitoring feeds, your squad rules and who holds availability. A wrong flag costs an athlete minutes nobody gives back, so we set the boundary before anything ships.

Discuss this agent →

Nestack Agents · Injury-risk and load monitoringAGT-SF-08 · Agent Care available after launch