FAQs about SentienX, The Company

1) What does your company do?
We help consumer and retail brands modernize growth with AI. Our work spans data foundations, personalization, measurement, and governance so leaders can scale AI that is trusted, compliant, and tied to financial impact.

2) Who do you serve?
We focus on consumer products, retail, grocery, QSR, and marketplaces. These sectors benefit most from rich first-party data, retail media, and inventory-linked decisions.

3) What problems do you solve first?
Data quality and latency, identity and consent, clean-room collaboration, unified measurement, and decisioning. We remove bottlenecks that block scale, then unlock revenue lift, better media efficiency, and faster test velocity.

4) How do engagements work?
We start with a 12-week program: baseline and guardrails, a retailer clean-room pilot with a content factory, then policy-controlled decisioning and MLOps hardening. After that we scale across brands, channels, and partners.

5) How fast will we see results?
Most clients see early wins within the first 6 to 12 weeks, such as validated incremental sales, budget shifts to high-lift contexts, and faster time to winner in creative testing.

6) How do you price?
Pricing depends on scope, speed, and required talent mix. We offer fixed-fee pilots, outcome-linked programs, and retainers for ongoing optimization. Each proposal includes clear deliverables and acceptance criteria.

7) How do you handle data privacy and security?
We use clean rooms, DPIAs, DSR workflows, region-aware storage, and strict key management. Every activation checks consent and purpose, and we audit joins and exports. We align with GDPR and CCPA requirements.

8) Who owns the IP and models created?
Clients own their data, features, and tailored models unless a separate license is agreed. We may provide reusable accelerators, but client deliverables and usage rights are defined in the statement of work.

9) What technology stacks do you support?
We are vendor-neutral and work across Snowflake, BigQuery, Databricks, major clouds, and leading RMNs. We integrate with common CDPs, ESPs, and ad platforms, and we deploy models through your preferred MLOps tooling.

10) How do we get started?
Schedule a discovery session with your CMO, CFO, and data lead. We align on goals and constraints, review current data and measurement, and confirm a 12-week plan with milestones, KPIs, and governance checkpoints.

General FAQs

1) What is an “intimacy engine” in marketing?
An intimacy engine is an AI-enabled operating model that predicts customer needs, composes personalized experiences, and measures financial impact across web, app, media, stores, and supply chain.

2) Why is trusted data the foundation for AI at scale?
AI only performs when data is reliable, explainable, and governed. Clean lineage, quality controls, and consent management turn pilots into production results that Finance can validate.

3) What is a feature store and why do I need one?
A feature store is a shared library of ready-to-use signals like days since last purchase or stock-out risk. It accelerates model delivery, ensures consistent definitions, and improves auditability.

4) How does a first-party identity graph work?
It links logins, hashed emails, devices, receipts, and loyalty IDs using deterministic and probabilistic matching, creating a unified customer view for personalization and measurement.

5) What is a consent and preference vault?
It stores the legal basis, purposes, and opt-ins for data use. Every activation checks permissions to ensure compliance and maintain customer trust.

6) What are data clean rooms and when should we use them?
Clean rooms allow retailers, brands, and media partners to analyze exposure-to-purchase without sharing raw PII. They are essential for privacy-safe collaboration and audited incrementality.

7) MMM vs MTA: what is the difference and when to use each?
Marketing Mix Modeling (MMM) guides budget allocation using aggregated data. Multi-Touch Attribution (MTA) informs tactical changes using path-level data. They work best when calibrated by incrementality tests.

8) What is incrementality testing and why does it matter?
Incrementality testing measures the true lift caused by an ad or offer. Methods include geo holdouts, CUPED adjustments, and uplift models to avoid over-crediting platforms.

9) What is ROAS and how is it different from ROI?
ROAS measures revenue per ad dollar. ROI uses profit and subtracts costs. For accuracy, many teams track contribution-margin ROAS aligned to Finance.

10) How do contextual decision models improve performance?
Adaptive decision models select the best creative, offer, and channel in real time, while reinforcement learning optimizes for long-term value like repeat purchase and LTV.

11) What is policy-as-code in marketing AI?
Policy-as-code encodes brand rules, inventory and pricing limits, legal exclusions, and fairness constraints so every decision is both profitable and compliant.

12) Which real-time signals matter most for personalization?
High-impact signals include weather, store-stock status, delivery ETA, competitor price gaps, user recency and affinity, and session context computed in the stream.

13) How do we measure retail media network performance accurately?
Use clean-room queries at item-by-store and campaign levels, reconcile to Net Sales and contribution margin, and apply guardrails to prevent inflated attribution.

14) How can we reduce CAC while maintaining volume?
Shift budget from low-lift to high-lift contexts using MMM and MTA, tighten audience eligibility, and optimize offers with real-time decision models.

15) What governance is required for GenAI content?
Use model cards, brand prompt libraries, toxicity and factual checks against product data, a rights-aware DAM, and audit logs for generated assets.

16) How do we prevent bias and exclusion in AI?
Monitor disparate impact across cohorts, enforce fairness constraints in decision policies, and regularly review model outcomes with cross-functional teams.

17) What is MLOps and why is it critical?
MLOps covers deploying, monitoring, and maintaining models in production. It includes drift detection, rollback rules, versioned features, and automated testing.

18) How do we handle privacy and security for customer data?
Conduct DPIAs, support DSR workflows, use region-aware storage and key management, and audit clean-room joins and exports to meet regulatory requirements.

19) What KPIs prove AI-driven growth to Finance?
Track incremental revenue and contribution margin by channel and audience, CAC and LTV trends with confidence bands, promo ROI reconciled to Net Sales, and working-capital improvements.

20) How fast can we stand up this operating model?
A focused 12-week plan can move from baseline and guardrails to pilot and initial scale, including clean-room activation, content factory, and policy-controlled decisioning.

21) How do we avoid platform lock-in with RMNs and walled gardens?
Maintain independent measurement, use clean rooms for incrementality, and reconcile outcomes to Finance rather than relying solely on platform-reported metrics.

22) What are common data quality problems and how do we fix them?
Late or noisy POS and identity events degrade models. Set SLAs, implement backfill logic, add anomaly alerts, and standardize schemas on an event backbone.

23) How does this improve creative velocity?
GenAI plus a test-and-learn factory increases variant tests with the same headcount, reduces time to first winner, and lowers cost per variant.

24) Which industries benefit most from this approach?
Consumer products, retail, grocery, quick-serve, and marketplaces see outsized gains due to rich first-party data, retail media, and inventory-linked decisions.

25) What does a successful team look like?
A CMO-led program with CFO and CDO co-sponsors, a Growth PM, data science and MLOps, creative technologists and brand owners, engineering for decision APIs and streaming, and a Finance partner for reconciliation.