Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Engineer a single, actionable customer view by unifying fragmented data across every touchpoint.
Your customer data is trapped in silos—web sessions, mobile app logs, CRM entries, and in-store transactions exist in isolation. This fragmentation prevents true personalization and distorts marketing ROI.
We build probabilistic graph models and deterministic matching algorithms to resolve identities with >99% accuracy, creating a unified Golden Record for each customer.
This resolved identity layer is the foundational data asset for all hyper-personalization services, enabling our Real-Time Behavioral Pricing Engine Development and Dynamic Product Recommendation System Development.
Outcome: Increase customer lifetime value by 18-25% through coordinated, channel-aware experiences.
Our probabilistic graph models and deterministic matching unify anonymous and known customer data across all touchpoints. This creates a single, accurate customer profile that directly drives measurable business results.
Maintain a single source of truth for customer data preferences and consent, simplifying compliance with regulations like GDPR and CCPA while demonstrating responsible data stewardship.
A clear roadmap for deploying a unified customer identity graph, from initial data assessment to full-scale production orchestration.
| Phase & Deliverables | Timeline | Key Outcomes |
|---|---|---|
Phase 1: Data Assessment & Model Design | Weeks 1-2 | Architecture blueprint, data schema mapping, and probabilistic matching logic defined. |
Phase 2: Pipeline Development & Initial Graph | Weeks 3-6 | First unified customer profiles, deterministic matching pipeline, and initial accuracy metrics. |
Phase 3: Probabilistic Model Tuning & Validation | Weeks 7-10 | Refined identity resolution with >95% accuracy, validation dashboard, and edge case handling. |
Phase 4: Integration & Orchestration Layer | Weeks 11-14 | Real-time API for downstream systems (CDP, marketing tools), and monitoring suite deployed. |
Phase 5: Production Handoff & Optimization | Weeks 15-16 | Full documentation, SLA definition, and a 30-day optimization period for peak performance. |
Ongoing Support & Evolution | Post-Launch | Optional managed service for model retraining, compliance updates, and integration with new channels. |
Our Cross-Channel Customer Identity Resolution AI transforms probabilistic customer data into a single source of truth, enabling hyper-personalized experiences and precise analytics across critical sectors.
Unify web, mobile, and in-store behavior to create a 360-degree customer view. Drive hyper-personalized recommendations, dynamic pricing, and cart recovery by understanding the complete customer journey. This is the foundational data layer for all services within our Retail and E-Commerce Hyper-Personalization pillar.
Securely link anonymous browsing with known account activity for superior fraud detection and personalized banking offers. Build accurate customer lifetime value models and comply with KYC/AML regulations using a unified, deterministic identity graph.
Anonymously connect patient engagement across digital portals, telehealth apps, and provider interactions to support personalized care journeys and clinical trial recruitment while maintaining strict HIPAA/GDPR compliance via privacy-preserving techniques.
Resolve user identities across streaming devices, booking platforms, and loyalty programs to deliver seamless, context-aware content and travel recommendations. Maximize subscriber retention and ancillary revenue through a unified profile.
Merge online sales journeys with network usage data and customer support interactions. Enable precise cross-sell/upsell campaigns, predict churn with higher accuracy, and optimize customer service by providing agents with a complete interaction history.
Connect prospective buyer research across websites, configurators, and dealership visits with post-purchase app usage and service history. Enable personalized owner communications, predictive maintenance alerts, and targeted loyalty programs.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Get clear answers on how we implement probabilistic identity graphs to unify your customer data and drive personalization.
Typical deployment is 4-8 weeks from kickoff to production. This includes data pipeline integration, probabilistic model tuning, and unification of 3-5 core channels (web, mobile, POS). Complex enterprise deployments with legacy system integration may extend to 12 weeks. We provide a detailed project timeline during the discovery phase.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
