Multi-Modal Fusion Engine
6 signal types. 1 unified decision. Near real-time.
Unlike single-modal systems that rely on just clicks or past behavior, MicroTarget fuses multiple signal types in real-time to create a living behavioral profile for each user.
App Events
Every tap, scroll, and interaction
Temporal Patterns
Time-based behavior changes
Engagement Intent
How invested is the user right now?
Purchase Intent
Buying signals and conversion probability
Churn Risk
Early warning before users leave
Zero-Party Data
Explicit user preferences and consent
Architecture Overview
Explore the real-time infrastructure behind our Decision Engine
MicroTarget's data processing architecture completely departs from classic CRM/CDP batch paradigms, building upon a stream-first, stateful, low-latency decision architecture. The core principle is processing raw signals generated by the user in-app and returning a decision within a maximum of 150–200 ms.
1. Event Ingestion Layer
Raw event data streaming from each user's device is captured by a high-throughput, low-latency ingestion API. To prevent data loss and ensure crash-resilience, the pipeline leverages a WAL-like, lock-free, append-only disk queue structure. Events are processed idempotently using a combination of UUID-v4 and incremental session indices to definitively suppress duplicates. The data is then deterministically sharded into Kafka/Redpanda topic partitions via a 'user_id mod N' strategy, guaranteeing continuous 'per-user affinity' for all downstream stateful stream operators.
2. Streaming Feature Extraction
Raw stream payloads are continuously evaluated by a stateful inference engine—resembling Flink or Spark Structured Streaming—where they are mapped into machine-learning compatible features. Because this layer maintains streaming state, it mitigates latency trade-offs by utilizing high-performance in-memory KV stores or optimized RocksDB instances, ensuring multi-threaded read/write capability to feed the encoder in less than 15 milliseconds.
3. Intent Model (ML Scoring)
The extracted features are routed to the Intent Model Scoring Server, designed around micro-batch inference architecture for maximal throughput. It computes and refines independent probability vectors for every active user profile: 'engagement_intent ∈ [0,1]', 'purchase_intent ∈ [0,1]', and 'churn_risk ∈ [0,1]'. Advanced predictions, such as conversion_window phasing and interest-weighted vectors, are also structurally formulated and written back to the memory-resident feature store.
4. Fusion Layer
The Fusion Layer departs from rudimentary rule-based static segmentation. It dynamically merges the freshly updated embeddings, real-time ML intent scores, and temporal device signals into a unified, graph-style fused behavioral state. The primary architectural goal here is to construct an evolutionary state vector that accurately encapsulates the rolling longitudinal trend of user behavior, transcending isolated static snapshots.
5. Ranking Engine
Acting as the decisive core algorithm, the Ranking Engine matches the fused user state against all active marketing campaigns in an instantaneous 'micro-auction'. The process leverages a GSP (Generalized Second Price) logic paired with an explicit hybrid rule system. Engineered with an entirely monotonic and deterministic calculation footprint, identical states predictably yield identical decisions—rigidly confined to a latency target of < 50ms.
6. Concept Bottleneck & RTME
To mitigate the opaque nature of black-box advertising ML, our Real-Time Marketing Engine (RTME) integrates an explainable 'Concept Bottleneck' model. This paradigm shift maps ambiguous latent arrays directly into highly interpretable human marketing constructs—like 'frustration', 'drop-off', and 'stickiness'. Consequently, the engine not only scores targeted users but offers 100% transparent algorithmic explainability as to precisely *why* a specific campaign orchestration was dynamically triggered.
System Parameters & SLA Tolerances
MicroTarget is engineered to operate under strict deterministic conditions. Below are the hardware latency guarantees and stream thresholds.
| Component | Protocol / Mechanism | Latency / Threshold |
|---|---|---|
| Event Ingestion (API) | Idempotent POST / WAL Queue | < 10 ms |
| Feature Extraction | Stateful KV Store (RocksDB) | < 15 ms |
| Intent ML Scoring | Micro-batch Inference | < 25 ms |
| Ranking Engine (GSP) | Monotonic / Deterministic | < 50 ms |
| End-to-End Decision | Full DAG execution | 150 - 200 ms |
Post-IDFA & Privacy Ecosystem
Following Apple ATT (+80% rejection rates) and the Google Privacy Sandbox horizon, legacy batch-working CDP/CRMs are becoming obsolete. MicroTarget eliminates IDFA dependency by purely utilizing privacy-first, on-device Zero-Party behavioral data architectures.
4-7x Performance Over Legacy Specs
While legacy platforms rely on periodic CRON jobs or webhook triggers causing severe latency, MicroTarget enforces Strict Event Streams rather than Eventual Consistency. Sub-15ms feature extraction leads to massive stability in conversion rates and sharp CPA drop-offs.
Data Integration Examples
{"event_id": "9f8c4b2a-1e3d-4f7c-8a2b-...","session_index": 42,"user_mode": "authenticated","vectors": {"scroll_depth_hz": 72.4,"tap_velocity_ms": 120,"active_duration_s": 48},"timestamp": 1712613245000}A lock-free append operation pushing continuous implicit data.
{"decision_id": "d_78291a","latency_ms": 28.4,"rtme_state": {"engagement_intent": 0.92,"churn_risk": 0.04,"concept": "high_stickiness"},"orchestration": {"winner_campaign": "WINTER_FLASH_SALE_15","trigger_type": "in_app_modal"}}The resulting Concept Bottleneck state and orchestrated campaign response.