A marketing technology innovator needed to unify planning, execution, measurement, and budgeting — scattered across 15+ disconnected systems — into a single, real-time source of truth. Predikly designed and engineered SAVI, an Artificial Specialized Intelligence (ASI) platform that orchestrates 17+ specialized AI agents to plan, execute, and optimize marketing spend simultaneously.
Download Full Case Study (PDF)The client's marketing organization was running on more than 15 disconnected systems — CRM, ad platforms, finance and ERP tools, and spreadsheets — with no unified intelligence layer stitching them together. Planning a single campaign cycle consumed 14 to 16 weeks of manual spreadsheet work rather than strategic thinking. Attribution reporting lagged 30 to 45 days behind reality, so by the time the CMO's team could see what was working, the budget behind it was already spent.
Meanwhile, budget variance ran as high as 4% — translating into millions of dollars in unexplained spend — and market conditions could shift in days while reallocating budget to respond still took weeks. Leadership needed a straight answer to a simple question: "Which campaigns are actually working, and can we move money to them faster?" No existing tool in the stack could answer it in real time.
Predikly began with a structured discovery phase across the client's marketing, finance, and operations teams — CMOs, regional marketing directors, campaign managers, and the finance function that governs budget. The goal was to understand not just the existing tool stack (Salesforce, Marketo, HubSpot, Google Ads, LinkedIn, and the finance systems behind them) but the actual decision loop: how plans were made, how budgets moved, and where attribution broke down.
Rather than bolting another point solution onto an already fragmented landscape, we designed toward a single architectural principle: planning, execution, measurement, and budgeting should happen as one continuous, AI-native process — not as four sequential, hand-off-driven stages owned by different teams and different tools.
Predikly architected and built SAVI — a Marketing Intelligence Platform positioned as an Artificial Specialized Intelligence (ASI) system rather than a conventional marketing automation tool. SAVI unifies four functions that legacy MarTech forces teams to handle in isolation: Plan, Execute, Measure, and Optimize — running simultaneously against one shared source of truth, integrated directly with the client's existing stack with no rip-and-replace required.
At the core of SAVI is a five-layer agent holarchy comprising 17+ specialized AI agents, orchestrated rather than operating as an ad-hoc chatbot: an L1 Orchestrator for intent routing, L2 Mode and Domain agents handling context switching across Programs, Campaigns, Budget, Goals, and Market Intelligence, L3 Specialists covering marketing mix modeling, retrieval-augmented generation, attribution, and forecasting, and L4/L5 Worker and Validator agents that execute tasks and enforce guardrails before anything reaches a human. The stack is built on the Claude API, LangGraph, GraphRAG, and the Model Context Protocol (MCP), with Databricks — Delta Lake, Unity Catalog, and Spark — as the data and governance foundation across bronze, silver, and gold data layers.
Rather than treating marketing mix modeling as an optional add-on, Predikly made a Bayesian MMM layer (built on PyMC) the default processing fabric for every budget decision, so allocation recommendations carry 95% credible intervals instead of point-estimate guesswork. Real-time, multi-touch attribution replaces the traditional 30–45 day reporting lag, and a continuous reconciliation engine replaces the month-end close process with sub-minute streaming variance detection — so finance and marketing see the same numbers, updated continuously, instead of reconciling two versions of the truth once a month.
Because SAVI touches budget authority and customer data, Predikly built governance in from day one rather than bolting it on later: OAuth 2.0 and SAML SSO, role-based access control via Unity Catalog, AES-256 encryption at rest and TLS 1.3 in transit, a two-layer AI guardrail system with automatic budget limits and confidence-score requirements, and a human-in-the-loop approval matrix for higher-stakes decisions. The platform aligns to ISO/IEC 42001 and the NIST AI Risk Management Framework, with full audit lineage retained for seven years.
Within a quarter of production launch, SAVI delivered quantifiable, board-level results across every stage of the marketing lifecycle:
Perhaps most tellingly, the platform changed the nature of the conversation between marketing and finance. As early feedback on the platform put it: "Your tool allows both macro-level planning with finance and micro-level planning at the marketer level." Questions that once began with "we think" are now answered with "the data shows."
For the complete case study including the full reference architecture, agent layer breakdown, and implementation timeline, download the PDF below.
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