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Sports & Fitness AI agent · Injury risk

Injury-Risk Forecasting & Load-Management AI Agent

Describe what the load actually did, with the window it covers and the athlete's own range beside it, and hold the flag for the named medical staff, who decide availability.

Typical delivery
4–6 weeks
Deployment
Your stack
Medical read
Descriptive
After launch
Agent Care

What this agent does

Reports what happened, not what will happen

In

  1. 01

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

  2. 02

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

Reason

  1. 03

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

  2. 04

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

  3. 05

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

Decide

  1. 06

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

  2. 07

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

Out

  1. 08

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

  2. 09

    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

  1. 1Session data receivedGPS file, wearable sync, gym log or the athlete's daily entry
  2. 2Measures assembledVolume, intensity, distribution and the athlete's own range, each with the device behind it
  3. 3Load describedMeasures, the window they cover, flagged lines and confidence
  4. 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

  1. 6Squad systems updatedOnly where write access and the medical read allow it
  2. 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 required

8 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 boundary

    Agent 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 week

Illustrative example
Athlete
Midfielder, in season
Reported measure
High-speed distance above this athlete's own recent range
Window
Rolling week
Source of record
GPS units and session log
Confidence
88%
Reader
Named medical staff

As received

Measured 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 flagged

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

What the score decides

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

Action

AcknowledgeAnnotateEscalate to physician

Value

Where AI adds value

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

Where the value lands

Value 01 – 04

All logged sessions

From 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.

04

Build 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 monitoring

    • Catapult
    • STATSports
    • Polar Team
    • WIMU

  • Athlete management

    • Kitman Labs
    • Smartabase
    • Teamworks
    • AMS exports

  • Medical records

    • Injury and rehab notes
    • Availability status

Agent

Injury-risk and load monitoring

  • Reads the sessions
  • Describes the load
  • Holds for medical

  • Strength and conditioning

    • TeamBuildr
    • Bridge
    • Force plates
    • velocity

  • Observability & evaluation

    • OpenTelemetry
    • 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 defence

Inward → 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 – L2
Test whether a measure may stand
L3
Puts the read in medical hands
L4 – L5
Hold the load record the flag was drawn from
L6
Falls 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

  1. E1Final-output evaluationDid each reported measure match the session data behind it?
  2. E2Step-level evaluationDid the agent use the right athlete, window and squad configuration?
  3. E3Tool evaluationDid it read the correct athlete record and the correct device file?
  4. E4Confidence calibrationDo low-confidence records actually carry more input gaps?
  5. E5Slice evaluationHow does performance change across specific squads and positions?
  6. 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 lifecycle

Direction of processing →

  1. 01 · Retrieval

    1 mode

    • HL-03

      Unsynced device record

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

    Stage gathers

    Session files, gym logs, athlete entries and squad rules

  2. 02 · Reasoning

    2 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 proposes

    Load measures, the window they cover and confidence

  3. 03 · Tool / write

    2 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 writes

    Only where write access and approval policy allow it

  4. 04 · Output

    1 mode

    • HL-01

      Availability implied

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

    Stage returns

    The measure the medical staff read and record

  5. 05 · Change / Version

    1 mode

    • HL-07

      Silent threshold drift

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

    Stage tracks

    Model, 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 aggregate

Illustrative example
SliceFailure rateLiftLift vs. thresholdStatus
Athletes back from rehab8.0%3.4×Review
Congested fixture blocks6.1%2.6×Review
New squad arrivals4.2%1.8×Watch
Settled squad, full history1.6%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 stages

Switchback — the path turns at Improve and returns at Learn

  1. 01

    Detect

    Withdrawn-flag rate rises in one squad slice.

  2. 02

    Diagnose

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

  3. 03

    Improve

    Every change is versioned against the flags that exposed it.

  4. 04

    Verify

    A failing case holds the release back.

  5. 05

    Learn

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

  6. Learn → Detect

    The 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

  1. 01Load workflow discovery and boundary definition.Week 1
  2. 02Monitoring and AMS assessment.Week 1, Week 2
  3. 03Squad, position and flag-threshold rule mapping.Week 1, Week 2
  4. 04Session ingestion and measure normalisation.Week 2
  5. 05Load-description logic and input binding.Week 2, Week 3
  6. 06Confidence scoring and flag routing.Week 3
  7. 07Medical read workflow.Week 3, Week 4
  8. 08Athlete-management-system integration.Week 5
  9. 09Flag-calibration cases.Week 4, Week 5
  10. 10Guardrails and disclosure controls.Week 4
  11. 11Load-trail instrumentation.Week 5, Week 6
  12. 12Deployment, documentation and Agent Care handover.Week 6

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 tierPilotOne squad, one seasonProductionProduction athlete systemsAdvancedMultiple 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 priceFrom $5,000From $8,000Custom quote
Final build price
Confirmed after discovery based on integrations, workflow complexity, transaction volume, approval controls and deployment requirements.
Separate from build
Build 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.

  1. 01Your session sources and athlete record structure

    Session ingestion and measure mapping

    Week 1

  2. 02Representative recorded training blocks

    Load-description baseline, range fitting and input binding

    Week 2

  3. 03Your squad rules and configured thresholds

    Squad, position and threshold rule mapping

    Week 1

  4. 04Access to relevant APIs, feeds or exports

    Monitoring, AMS and medical assessment, then integration setup

    Week 2

  5. 05Flags that should not have been raised

    Calibration cases and the evaluation suite

    Week 4

  6. 06What must reach medical staff before anything changes

    Confidence scoring, flag routing, guardrails and disclosure controls

    Week 3

  7. 07Named medical staff to read the flags

    Medical read workflow, then pilot and production validation

    Weeks 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

W1

W2

W3

W4

W5

W6

  1. DiscoveryW1
  2. BuildW2 – W3
  3. EvaluateW4 – W5
  4. Pilot & LaunchW5 – 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 band
A bar covers the weeks its work is named in, and nothing else. The week 5 overlap is real, not padding.
At the end of W6
The block closes validation and Agent Care assumes monitoring.
Duration
Six-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.

Typical delivery
4–6 weeks
Deployment
Your monitoring stack
Medical read
Descriptive
After launch
Agent Care available

Nestack Agents · Injury-risk and load monitoring

AGT-SF-08 · Agent Care available after launch

Build a load-monitoring agent

Four questions so the call starts from your actual setup — then pick a time that suits you.

About you
Your squads

No obligation. We’ll map the workflow, name the automation boundary and tell you if an agent is the wrong answer.