Assemble the spend and outcome series, fit and refit the model, design the geo or holdout experiment that tests it, and turn the result into a budget recommendation the analyst accepts, interval attached.
Spend and outcome series arrive from ad platforms, the CRM and finance systems.
02
Series are aligned to one calendar, one currency and one channel taxonomy.
Reason
03
The model is fit and refit against the aligned series, one channel at a time.
04
Each channel's prior is logged with its source — an experiment, an assumption or a default.
05
A geo or holdout test is designed with its minimum detectable effect and power agreed first.
Decide
06
The model's estimate is checked against the experiment, and any gap is surfaced, not resolved.
07
Every recommendation is routed to the named analyst before a number leaves the model.
Out
08
The specification, the priors, the experiment and every edit are retained against the run.
09
Execute write actions only inside the approval boundaries agreed during implementation.
→Product statement
The agent fits the model and reads the experiment; the analyst decides which specification to accept, and no modelled figure leaves the page without its interval.
Example workflow
One channel estimate, series to approval
AgentHuman
1Series receivedSpend and outcome series, a new experiment result or a scheduled refit
2Inputs assembledHistoric series, the prior log, calibration history and channel taxonomy, each with its source
3Estimate draftedChannel contributions, credible intervals, experiment status and confidence
4Controls appliedSpecification checks, prior-source checks, calibration-gap checks and confidence threshold
No human action required
Stages 1 to 4 run unaided, and no figure is recommended at any of them — the agent is modelling, and the analyst's lane opens at the confidence gate.
5DecisionBranches at the confidence threshold
High confidence
Goes to the analyst to approve.
Low confidence
Adds a second modeller review first.
Analyst approval
The estimate is held with its interval, its calibration status and the confidence.
Approve · Revise · Escalate
Approved — released to planning▼
6Reporting systems updatedOnly where write access and approval policy allow it
7Outcome evaluatedInterval accuracy, calibration gap, edits and post-decision corrections
Edits
Every analyst edit is counted in the evaluation.
What should not run autonomously
Human approval stays in control
Outside the boundary — human approval required8 items
Reallocating budget between channels.
Publishing a modelled figure to the market.
Setting a channel's ROI prior on its own.
Declaring a test result before it is powered.
Automation boundaryAgent acts unaided
✓Assemble the spend and outcome series and fit the model.
✓Design the geo or holdout experiment and its power.
✓Attribute each channel's interval to its prior and source for the named owner.
✓Flag the gap when the model and the experiment disagree, and hold it for the analyst.
Any output leaves the model only inside the boundaries agreed at implementation, never as fact.
Presenting a modelled return as revenue already earned.
Reconciling attribution, incrementality and MMM into one figure.
Approving the budget shift the recommendation implies.
Changes to specification, priors or calibration rules.
Example output
One estimate, annotated
Everything the agent produces is attached to the run it was drawn from.
MMM output · single channelIllustrative example
Channel
Finding
Estimate status
Model run
Confidence
Attribution
Brand search, always-on
Large modelled contribution, no calibrating holdout run
Interval untested
This quarter's model run
68%
Analyst name on file
As receivedTaken from this quarter's model run and the series behind it — nothing on this side is asserted as fact by the agent.
Inputs checkedSpend and KPI seriesPrior source loggedHoldout test status
Why it's flaggedThe contribution is real in the model and a person still decides.
ActionApproveReviseEscalate
What the score decidesBelow the configured threshold the flag picks up a second modeller review before 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
Every channelFrom the assembled series
03Modelling
Fit against the series
Draw on the assembled series, the logged priors and the calibration history.
01Approved path
A model is not a measurement
Brand search and retargeting are the classic case: an observational read can show a strong return while a controlled test shows the outcome would have happened anyway — what the experiment exists to catch.
02Human review
Label the question each number
Attribution, incrementality and MMM measure different things; every output states which one produced it, and the three are never blended into a single figure.
04Build an evidence trail
The estimate, the interval around it and the analyst who accepted it stay on the plan.
Integrations
Typical integrations
Five system groups connect to the same agent. Which of them are in scope is decided in discovery.
Media & spend platformsGoogle Ads · Meta Ads TikTok · Amazon Ads · DV360
Business & finance dataERP · POS systems Revenue and conversion feeds
A single model fit assigns every channel one number; some channels move far more than others when the specification changes. Nestack reports estimate instability by channel, not only in aggregate.
Slice performance — reported separately, not only in aggregateIllustrative example
Slice
Failure rate
Lift
Lift vs. threshold
Status
Channels with little spend variation
7.5%
3.9×
Review
Brand search and retargeting
5.7%
3.0×
Review
Channels changed mid-period
3.3%
1.7×
Watch
Large channels with varied spend
1.9%
0.7×
Normal
Bar: estimate-instability lift vs. the large-varied-spend baseline · scale 0–4.0× · tick marks the 2.0× review threshold2 of 4 slices over threshold
Evidence-linked improvement
The loop closes on a case, not a story
The loop shuts when the wrong estimate is a regression case, not when it has been explained. That suite is what the next model run is measured against.
Improvement cycle · five stagesSwitchback — the path turns at Improve and returns at Learn
01Detect
Interval width or edit rate rises in a channel slice.
02Diagnose
The point estimate everyone quoted is checked against the series, the priors and the specification until one cause explains the swing.
03Improve
Whatever changes ships against a version, with the runs that prompted it attached.
04Verify
Nothing ships until the affected specification cases pass a second time.
05Learn
The suite grows by one case; so does the calibration record.
Learn → DetectThe return edge. The next run starts 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, modelling workflow, evaluation, integration, then production validation and handover.
WorkstreamWeek 1Week 2Week 3Week 4Week 5Week 6
01Analytics discovery and boundary scoping and boundary definition.
02Media, KPI and warehouse source assessment.
03Prior-source and calibration rule mapping and rule mapping.
04Series ingestion and taxonomy mapping.
05Model-fitting logic and interval binding.
06Confidence scoring and gap routing.
07Analyst approval workflow.
08Warehouse and experiment-tool integration.
09Specification and prior cases.
10Guardrails and acceptance controls.
11Run-trail instrumentation.
12Deployment, documentation and Agent Care handover.
12 workstreams · 6 weeks · bar shows the weeks a workstream is active — several run in parallelFinal 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 channel, one marketProductionProduction data and warehouse accessAdvancedMultiple markets / brands
Introduced at Pilot
Model fit to your data✓✓✓
Analyst approval✓✓✓
Calibration baseline✓✓✓
Introduced at Production
Reporting by channel—✓✓
Escalation workflow in your tools—✓✓
Approved output actions—✓✓
Warehouse integration—✓✓
Introduced at Advanced
Multi-market calibration rules——✓
Multi-stage analyst approvals——✓
High channel volume——✓
Multi-market model controls——✓
Build priceFrom $5,000From $8,000Custom 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 spend, KPI and warehouse access→Series ingestion and taxonomy mappingWeek 1
02Representative historical series and any past tests→Model-fitting baseline and interval attributionWeek 2
03Your prior-source and calibration rules→Prior-source and calibration-boundary mappingWeek 1
04Access to relevant APIs, feeds or exports→Media, KPI and warehouse assessment, then integration setupWeek 2
05Estimates you would not want budgeted on→Experiment cases and failure-mode testingWeek 4
06What no estimate may be read as→Confidence scoring, gap routing, guardrails and approval controlsWeek 3
07Named analysts to review recommendations→Analyst approval workflow, then pilot and production validationWeeks 5–6
Nothing else is requiredDeployment, documentation and Agent Care handover are ours.
Delivery timeline
Four phases across six weeks
Bands follow the real work rather than the plan, which is why evaluation and pilot share week 5.
PhaseW1W2W3W4W5W6
DiscoveryW1
BuildW2 – W3
EvaluateW4 – W5
Pilot & LaunchW5 – W6
Week focusW1Analytics discovery, calibration mapping and the boundaryW2Source integration and the model-fitting baselineW3Modelling workflow, confidence logic and approval controlsW4Evaluation suite, guardrails and failure-mode testingW5Warehouse integration, pilot runs and targeted correctionsW6One planning cycle modelled under the analytics lead, then handover
Reading the bandA bar covers only the weeks its work is named in — the week 5 overlap is real, not padding.
At the end of W6Once the cycle validates, Agent Care owns the running agent.
DurationSix-week plan shown · typical delivery 4–6 weeks depending on scope confirmed in discovery.
Next step · Advertising AI agent
Build a marketing-mix agent around your measurement stack.
Show us your spend and outcome data, your prior sources and who signs off on a number. The judgement that turns an experiment into a calibrated prior stays with the analyst, not the agent.
