Applied AI for Decisions That Need Evidence
Evidence-first approaches for marketing decisions that need clearer measurement and control
Incrementality Measurement
Estimate causal lift against a defined baseline
Decision outcome
Defensible incremental lift
The Challenge
Last-click and siloed models can mislead budget decisions.
Our Approach
Design an evaluation around the decision using suitable experiments, holdouts, or causal models.
Key Benefits
Explicit baseline and success criteria
Uncertainty reported with the estimate
Decision-linked business outcomes
Methods matched to available data
Conceptual comparison
Estimate the outcome that would have happened without the intervention.
Causal baseline
Observed outcome
Observed outcome - causal baseline
Estimated incremental lift
Evidence
Causal baseline
Control
Budget decision trail
Reconciled Attribution
Build a reviewable view of connected touchpoints
Decision outcome
Reconciled journey view
The Challenge
Fragmented sources make channel and journey analysis difficult to reconcile.
Our Approach
Align available identifiers, event definitions, and source limitations before selecting an attribution method.
Key Benefits
Documented source coverage
Identity assumptions made explicit
Purpose-scoped data handling
Reviewable reconciliation rules
Reconciliation before attribution
Source coverage and identity assumptions determine what can be inferred.
Covered sources
Paid media
CRM
Commerce
Offline
Reconciliation rules
Identifiers, event definitions, coverage gaps
Reviewable journey
Method and limitations stay visible
Evidence
Source alignment
Control
Cross-channel review
Guardrailed Optimization
Turn observed signals into reviewable recommendations
Decision outcome
Guardrailed budget decisions
The Challenge
Manual optimization lags market changes.
Our Approach
Coordinated monitoring that proposes bid and budget changes within explicit constraints and approval rules.
Key Benefits
Recommendations bounded by guardrails
Timely bid and budget recommendations
Model assumptions made visible
Human-in-the-loop controls
01
Observe
Check signals and uncertainty
02
Recommend
Apply constraints and tradeoffs
03
Review
Approve, reject, or revise
Evidence
Explicit constraints
Control
Human approval points
Resilient Measurement
Plan for variable signal and consent availability
Decision outcome
Purpose-scoped measurement
The Challenge
Browser controls, consent choices, and platform changes make legacy tracking less dependable.
Our Approach
Combine permitted first-party signals, experiments, aggregated measurement, and models appropriate to the decision.
Key Benefits
Methods that do not assume third-party cookies
Consent and purpose-aware data handling
First-party signal assessment
Documented retention and access rules
Signal availability shapes the method
Start with data rights and coverage, not a preferred tracking technique.
Permitted signals
First-party events, aggregates, experiments
Purpose and access
Consent, authority, retention, deletion
Measurement plan
Experiment, causal model, reported limits
The plan can adapt when signal coverage changes.
Evidence
First-party signals
Control
Defined retention
AI SEO (AIO)
Make brand evidence easier for AI systems to interpret
Decision outcome
Source-ready brand evidence
The Challenge
Search and answer systems need clear entities, well-supported claims, and accessible source content.
Our Approach
Review technical access, entity signals, structured data, citations, and answer-ready content across relevant surfaces.
Key Benefits
Entity and source clarity
Answer-ready content structure
Valid structured data where applicable
Defined monitoring questions
From source to answer surface
Visibility starts with accessible evidence, not engine-specific tricks.
Access
Crawlable sources
Technical access, canonical pages, useful copy
Meaning
Claims and entities
Named relationships, supported facts, citations
Evaluation
Answer monitoring
Questions, cited sources, material changes
Measure source inclusion and answer quality, not a guaranteed rank.
Evidence
Entity clarity
Control
Source-ready content
Frequently Asked Questions
Scope, methods, and limitations behind these services
How does incrementality testing differ from A/B testing?
A/B tests compare variants, while incrementality studies ask whether an intervention caused an outcome relative to a credible baseline. The appropriate design depends on the decision, available data, and practical constraints.
How do you choose a measurement method?
We begin with the decision, baseline, data-generating process, and acceptable uncertainty. Experiments, geo holdouts, synthetic controls, or observational models are considered only when their assumptions fit the situation.
How do you handle cross-device attribution?
Cross-device analysis is only proposed when permitted identifiers and an appropriate legal basis are available. We document source coverage, matching assumptions, expected error, and cases where a unified journey cannot be supported.
Can you attribute offline conversions?
Sometimes. Feasibility depends on consent, matching authority, identifier quality, CRM or transaction data, and the evaluation design. Discovery determines whether a defensible link can be made.
How does multi-agent AI optimization work?
Specialized AI agents can monitor bidding, budgets, audiences, and creatives, then coordinate recommendations within defined constraints and human-in-the-loop controls.
Will AI optimization work with my existing campaigns?
Integration fit depends on your platforms, available APIs, data quality, and required decision cadence. A focused assessment identifies the sources that matter and the safest way to add recommendations without disrupting the current workflow.
How does sig.ai handle privacy and compliance?
We scope data purpose, authority, consent, access, retention, deletion, and technical controls for the engagement. Any regulatory or certification requirement should be confirmed explicitly before implementation.
How do you measure without third-party cookies?
The available approach may combine first-party data, consented tracking, experiments, aggregated measurement, and modeled signals. The method and expected uncertainty are defined around the decision and the data that can be used lawfully.
What is AI SEO (AIO)?
AI search optimization improves the clarity, accessibility, and support behind brand information that search and answer systems may use. It cannot guarantee inclusion, ranking, citation, or a particular generated answer.
Which AI engines do you optimize for?
The monitoring scope can include Google AI features and answer systems such as ChatGPT, Perplexity, Claude, Grok, and Gemini. Work focuses on shared source quality and surface-specific observations rather than guaranteed placement.
Can sig.ai work with my existing marketing stack?
Potentially. Integration scope is defined around the ad platforms, analytics tools, CRMs, warehouses, and proprietary systems required for the project. Compatibility is confirmed during discovery rather than assumed.
How long before I see measurable results?
Timing depends on data readiness, baseline quality, decision frequency, and the evaluation method. A focused assessment should establish success criteria and an evidence window before optimization claims are made.
Have a Decision That Needs Better Evidence?
Bring the decision, the available signals, and the constraints. We will help determine whether an AI or measurement approach is appropriate before proposing implementation.
Scope first • Define evidence • Keep review points explicit