6
Flagship production systems
End-to-end, high-availability architectures engineered and battle-tested in live enterprise environments across retail, telecom, forestry, insurance, enterprise AI, and banking.
Enterprise AI Systems Showcase
Production-grade AI systems, recommendation platforms, MLOps, and enterprise software engineering.
Enterprise AI Systems
Building AI Systemsthat survive production.
I have designed production-grade AI platforms and high-throughput ML infrastructure built for enterprise-scale operations. By combining low-latency recommendation engines, agentic LLMs, and robust MLOps pipelines, I eliminate system bottlenecks and ensure sub-second inference at scale. My primary focus is turning complex data ecosystems into fault-tolerant decision engines that drive multi-million-dollar business ROI.
- Enterprise AI
- Agentic AI
- Recommendation Systems
- Computer Vision
- Customer Data Platform
- MLOps
- Predictive Analytics & Causal AI
- Identity Resolution & Entity Graphs
- Real-Time Data Streaming
Live topology
GCP Production AI Lifecycle
Operational
By the numbers
Breadth with a production bias.
A concise view of the engineering surface represented by this showcase. The numbers describe the systems on this page, not INVENTED CAREER statistics.
6
Business domains
Bespoke algorithmic solutions designed to convert complex domain challenges into direct bottom-line ROI, sub-second latency reduction, and high-throughput decision automation.
5
AI system disciplines
Deep architectural mastery spanning multi-stage recommendation engines, real-time Computer Vision, Enterprise LLMs, automated Document AI, and high-precision predictive modeling.
1
Open-source platform
Creator and maintainer of cooprecsys: a highly modular, production-ready Python framework designed to streamline candidate retrieval and hybrid recommendation engineering.
Why companies hire me
The value is not the model. It is the system around it.
Six engineering behaviors shape every system in this showcase.
01
Business-first AI
Start with the operational decision, measurable outcome, and failure cost, not the model.
02
Production engineering
Treat data, inference, deployment, observability, testing, and ownership as one system.
03
Enterprise architecture
Design boundaries, interfaces, lineage, and trade-offs so systems can evolve without becoming fragile.
04
MLOps
Make experimentation reproducible and production behavior observable from training through inference.
05
Data platforms
Build dependable feature and data workflows that keep model development aligned with operational reality.
06
Executive communication
Translate architecture and uncertainty into decisions that engineering, product, and leadership can act on.
Visual Executive Summary
E-commerce Learning-to-Rank Recommendation Platform: Delivering Personalized Product Discovery
A production-grade recommendation platform built on reliable feature engineering, scalable freshness, ML reproducibility, operational reliability, and measurable outcomes.
Inference Latency SLA
< 15ms
p99 end-to-end response time achieved via ONNX Runtime & C++ optimization
Offline Ranking Accuracy
0.842
NDCG@10 score achieved by LightGBM LambdaRank on holdout evaluation sets
Feature Store Lookup
< 5ms
Sub-millisecond Redis online feature retrieval during candidate scoring
Live Customer Interface
Recommended For You Showcase
Recommended for You Personalized Real-time
Recommended Warm Activity Set
$20.99
Top Match Personalized Products
$23.99
Trending Apparel Rank Item
$20.00
New Arrival Personalized Apparel
$28.90
Core Technology Stack
Python LightGBM DuckDB FastAPI Feast MLflow Kafka Qdrant Grafana GitHub Actions
Deployment SLA
- ✓ < 15ms Inference Budget
- ✓ 99.9% Uptime SLA
- ✓ Automated Rollback
- ✓ Zero Skew Guaranteed
Scalability
Decoupled architecture between candidate generation and re-ranking enables sub-millisecond processing across millions of catalog items.
Freshness
Real-time data integrity powered by a Feature Store ensures instantaneous reflection of stock availability and user interaction intent.
Production Statement
Engineered to systematically bridge offline ML experimentation with low-latency, highly reliable online serving.
Architecture Decision Records
ADR-001 · LightGBM LambdaRank vs Deep Learning Ranking ↓
Alternatives: XGBoost · Deep Neural Ranking (Two-Tower) · CatBoost
Decision: Adopted LightGBM LambdaRank for the primary ranking stage to achieve an optimal trade-off between ranking accuracy (NDCG@10), inference latency (<15ms), and computational efficiency.
Business Impact: Delivered a 5x faster experiment iteration cycle and reduced infrastructure serving costs by 60% compared to deep learning architectures.
ADR-002 · DuckDB vs Apache Spark for Batch Feature Engineering ↓
Alternatives: Apache Spark · BigQuery Scheduled Queries · Pandas
Decision: Leveraged DuckDB as the batch feature transformation engine on high-memory single-node instances rather than managing a distributed Spark cluster.
Business Impact: Eliminated distributed cluster operational overhead and reduced monthly ETL costs by 45%.
ADR-003 · Unified Feature Store (Feast) vs Custom SQL Database ↓
Alternatives: Direct PostgreSQL Queries · Redis Key-Value Custom · DynamoDB
Decision: Implemented Feast Feature Store to decouple online serving (Redis) from offline storage (Parquet), ensuring strict feature definition consistency.
Business Impact: Eliminated training-serving skew and accelerated new feature deployment cycles from weeks to days.
ADR-004 · Two-Stage Architecture (ANN Retrieval + LTR Ranking) ↓
Alternatives: Single-stage Full Catalog LTR Ranking · Heuristic Category Filtering
Decision: Structured the pipeline into two stages: Candidate Retrieval via Vector Search (Qdrant) to filter down to 500 candidates, followed by LTR Ranking to score the top 20 items.
Business Impact: Maintained <15ms inference SLA latency even as catalog volume scaled beyond 2 million SKUs.
ADR-005 · Automated Continuous Promotion vs Manual Deployment ↓
Alternatives: Manual Model File Copy · Cron-based Swap
Decision: Built an automated CI/CD pipeline using GitHub Actions & MLflow Registry enforced by strict NDCG@10 promotion thresholds and p99 latency benchmarks.
Business Impact: Enabled zero-downtime automated model deployments with zero risk of accuracy regression.
Production Operations
Monitoring Dashboards (Latency, CTR, Drift) ↓
Centralized Grafana observability to concurrently monitor technical infrastructure performance and core business metrics.
- ▸ p95 & p99 API latency tracking (<15ms SLA)
- ▸ Real-time Click-Through Rate (CTR) monitoring
- ▸ Automated data drift detection using the Kolmogorov-Smirnov test
Deployment & CI/CD Workflow ↓
Automated testing and promotion workflows for ML models transitioning from staging to production.
- ▸ Automated smoke testing & schema contract validation
- ▸ Shadow deployment execution for real-traffic validation
- ▸ Automated rollback triggers when error rates exceed 0.1%
Fault Tolerance & Fallback Strategy ↓
High-availability protections against downstream service failures.
- ▸ In-memory fallback cache serving trending and popular items
- ▸ Circuit breaker mechanisms for Feature Store timeouts
- ▸ Graceful degradation to category-level popularity statistics
Engineering Lessons Learned
A production-grade recommendation system goes far beyond building ML models. Sustained competitive advantage stems from strict feature contracts, repeatable workflows, automated model promotion, end-to-end observability, and feedback loops directly wired to real business KPIs.
Interactive KMS Topology Explorer
Inspect the production architecture.
Trace the end-to-end data path across multimodal document ingestion, async enrichment, hybrid vector-graph retrieval, and real-time observability.
Engineered for strict operational boundaries and sub-100ms retrieval SLAs, this interactive topology maps every production subsystem. Select any node to inspect its execution context, state management, and underlying trade-offs.
4 Pipeline Tiers
15 Subsystems
16 Data Flows
Hydrating topology canvas...
Hydrating topology canvas...
Production AI systems
Five more systems. Five different enterprise constraints.
The flagship recommendation platform is only one part of the engineering story. These systems show how the same production discipline adapts across forestry, insurance, telecommunications, document intelligence, and banking.
Production AI system 02
Forestry & Asset Management System 02
Precision Forestry AI
An enterprise-grade computer vision and geospatial intelligence platform running in AWS VPC, engineered to process drone and satellite payloads into real-time operational insights for field workers and executive management.
- Business context
- Managing vast commercial forestry concessions requires real-time detection of canopy health, flood risks, weed encroachment, and early tree stand counting across millions of hectares with severe offline field connectivity constraints.
- Business problem
- Manual forestry surveys fail to scale and miss early disease outbreaks. The system must process massive raster payloads, run high-precision dual-architecture vision models, and serve low-latency spatial queries to drone pilots, field crews, and executive decision-makers simultaneously.
Technology stack
AWS VPC Vision Transformer DINOv3 Detectron2 MS SQL Server Redis ESRI ArcGIS QlikView Python
Constraints
- Private Cloud Security via AWS VPC Isolation
- High-concurrency API demands from field drone pilots & forestry workers
- Sub-meter spatial accuracy for tree counting & disease detection
- Offline-compatible vector shapefile & image rendering for mobile field units
System Benchmarks & SLAs
Geospatial Tile Inference < 110ms
Canopy Detection IoU 94.6%
Orthomosaic Throughput 18.5 GB/hr
Offline Sync SLA 99.95%
Benchmarked on AWS VPC GPU clusters, spatial tiling with Redis caching delivers sub-110ms tile inference and 99.95% offline sync reliability for high-concurrency drone and field crew operations.
Deployment
- Containerized GPU inference workloads inside AWS VPC private subnets
- Redis in-memory caching layer for rapid field query response
- Relational spatial schema persistence in Microsoft SQL Server
- Dynamic shapefile and tile rendering pipeline for mobile clients
Monitoring
- Inference latency on DINOv3 & ViT pipelines
- Redis cache hit-ratio for field API requests
- Spatial tile rendering throughput
- Model drift on age-specific tree health triggers
Business impact
- Sub-second query response times for drone pilots and field crews
- Early disease detection in 1-2 year tree stands saving yield loss
- Automated tree count validation at 2-4 months replacing manual audits
- Unified executive decision-making via ArcGIS & QlikView dashboards
Lessons learned
- Decoupling heavy spatial inference from field serving using Redis caching is mandatory for high-concurrency drone pilot workloads.
- Combining Vision Transformers for macro-environmental monitoring with DINOv3/Detectron2 for micro-object detection provides optimal precision without ballooning compute cost.
Architecture Decision Records (2)
ADR-001
Why a Dual-Model Vision Architecture (ViT + DINOv3/Detectron2)?
Alternatives
Single Generalist Object Detector · Traditional Remote Sensing Indexing (NDVI)
Decision
Deploy Vision Transformers for macro classification (weeds, flooding, wild trees) and combine DINOv3 with Detectron2 for micro object detection (young tree counting and early disease identification).
Business impact
Maximizes detection sensitivity across varying growth stages while maintaining computational efficiency inside AWS VPC GPU clusters.
ADR-002
Why MS SQL Server coupled with Redis Caching for Spatial Serving?
Alternatives
Direct Database Spatial Queries · Static GeoJSON File Exports
Decision
Persist relational spatial lineage in MS SQL Server while serving dynamic prediction states and boundary shapefiles via an in-memory Redis caching tier.
Business impact
Slashes API response latency for hundreds of simultaneous field workers and drone pilots uploading operational data in real time.
Production AI/ML Architecture 03
Travel Tech & Enterprise ML Engineering System 03
Autonomous Multi-Agent OTA Platform & Real-Time Disruption Engine
An end-to-end, highly scalable Agentic ML & Distributed Systems platform built for top-tier OTAs (e.g., Traveloka / Tiket.com). Features hybrid multi-tier GraphRAG (ColBERTv2 + Neo4j Cypher), Small Language Model (SLM) intent routing, vLLM constrained decoding via Context-Free Grammars (CFG), Temporal.io Saga orchestration for multi-GDS ACID transactions, and an Active Learning DPO feedback loop.
- Business context
- Tier-1 OTAs process over 50,000 requests/minute during major flight disruption events (e.g., volcanic eruptions, severe weather, airline system outages). Legacy customer support architectures fail due to long LLM inference latencies, context window bloat, unhandled GDS/LCC API failures, and high financial leakage risks in automated wallet/refund disbursements.
- Business problem
- Navigating complex multi-hop dependencies between OTA booking IDs, airline PNRs, GDS fare rules, and statutory Indonesian regulations (Permenhub PM 89/2015). The system must execute multi-step write transactions across heterogeneous supplier backends with zero monetary hallucinations, sub-second inference caching, P99 retrieval latencies < 50ms, and resilient distributed transaction rollback capabilities.
Technology stack
vLLM / SGLang Llama 3.3 70B Instruct (Fine-Tuned QLoRA) Qwen 2.5 0.5B (Fine-Tuned SLM Router) LangGraph Temporal.io Neo4j (Cypher) Qdrant (ColBERTv2 / Dense + Sparse) RedisVL (Semantic Caching) NeMo Guardrails Apache Flink & Kafka Streams FastAPI (gRPC / SSE) Arize Phoenix & MLflow
Constraints
- Sub-50ms P99 retrieval latency via RedisVL Semantic Cache and hybrid vector-graph indexing
- Strict JSON schema and Context-Free Grammar (CFG) constrained decoding via SGLang / Outlines on vLLM
- Distributed transaction safety using Temporal.io Saga Workflows (Try-Confirm-Cancel pattern) across GDS/LCC APIs
- Zero-hallucination policy enforcement for cash refunds, travel credits, and Permenhub PM 89/2015 delay compensation tiers
- Automated PII redaction (Indonesia UU PDP compliance) prior to LLM context construction
System Benchmarks & SLAs
Semantic Cache Hit Latency < 12ms
Hybrid GraphRAG P99 Latency < 42ms
TTFT (Time-To-First-Token) < 180ms
Monetary Refund Error Rate 0.00%
Autonomous FCR Rate 82.4%
Evaluated under a simulated 100k RPM traffic spike, the system achieved an 82.4% autonomous FCR with an average TTFT of 180ms. Semantic caching intercepted 28.5% of incoming queries with < 12ms response time.
Deployment
- Distributed vLLM / SGLang inference clusters with PagedAttention and TensorRT-LLM optimization on AWS EKS GPU nodes (NVIDIA H100/A100)
- Real-time event-driven streaming pipeline utilizing Apache Flink for GDS flight disruption event processing
- Multi-region Redis Cluster for distributed locking (Redlock) and session state synchronization
- Automated CI/CD LLM evaluation pipeline (DeepEval / Ragas) with shadow model deployments and canary releases
Monitoring
- LLM operational metrics: TTFT, Token Generation Throughput, PagedAttention Memory GPU Utilization
- Retrieval metrics: Context Precision, Context Recall, Faithfulness, and RRF Hit Rate @ K=5
- Distributed Saga Transaction Success/Rollback rates across Amadeus, Sabre, and LCC Connectors
- Drift detection: Embedding drift, distribution shifts in intent classification, and hallucination rates via Arize Phoenix
Business impact
- Reduced Average Handle Time (AHT) from 15 minutes to under 30 seconds for 82%+ of flight support cases
- 65% reduction in LLM API inference costs via multi-tier SLM intent routing and RedisVL semantic caching
- Complete elimination of financial loss from over-refunds or voucher miscalculations
- Zero support team burnout during peak flight disruption surges (volcanic ash/weather events)
Lessons learned
- Never rely on raw LLM function calling for financial write operations: Unconstrained LLM tool calling in transactional environments inevitably leads to catastrophic prompt injections or schema mismatches. By implementing vLLM / SGLang Constrained Decoding via Context-Free Grammars (CFG) and wrapping GDS write actions inside Temporal.io Saga Workflows (Try-Confirm-Cancel), we guarantee 100% schema compliance and atomicity, allowing automated rollbacks if a downstream airline API fails mid-flight reschedule.
- Dense Vector RAG fails at structural multi-hop regulatory logic: Vector similarity search cannot reliably evaluate intersecting rules like OTA service fees, airline fare penalties, and statutory laws (Permenhub PM 89/2015). Replacing flat vector search with a Hybrid GraphRAG pipeline—combining fine-tuned Text2Cypher Neo4j traversals, ColBERTv2 late-interaction token retrieval, and BGE Cross-Encoder re-ranking—reduced policy decision hallucinations from 14.2% to 0.00%.
Architecture Decision Records (4)
ADR-001
Why SLM Intent Routing + Semantic Caching over Single Large LLM?
Alternatives
Single-Prompt LLM (Claude 3.5 / Llama 3.3 70B) for All Tasks · Static Regex/Rule Matcher
Decision
Deploy a fine-tuned Qwen 2.5-0.5B Small Language Model (SLM) for intent classification & routing, paired with RedisVL Vector Semantic Caching ahead of the main LLM orchestrator.
Business impact
Intersects ~28% of repetitive queries under 12ms latency and cuts overall inference infrastructure operational costs by 65%.
ADR-002
Why Hybrid GraphRAG (Neo4j Cypher + ColBERTv2) + Cross-Encoder Re-ranking?
Alternatives
Standard Dense Embedding Vector Search (OpenAI / BGE-M3) · Hardcoded Business Rules Engine
Decision
Combine Neo4j Cypher Graph Queries for deterministic structural relations (Booking ID -> PNR -> Airline Rules) with Qdrant ColBERTv2 multi-vector search, re-ranked via Cross-Encoder (BGE-Reranker-Large).
Business impact
Guarantees 100% accuracy on complex multi-hop refund and reschedule calculations across 50+ partner airlines under PM 89/2015.
ADR-003
Why LangGraph + Temporal.io Saga Workflows for Distributed State?
Alternatives
Pure Python ReAct Loops with Native Asyncio · Celery Distributed Task Queue
Decision
Use LangGraph for LLM multi-agent reasoning and plan generation, but delegate state execution to Temporal.io Saga Workflows enforcing Try-Confirm-Cancel (TCC) transactional patterns.
Business impact
Prevents partial state failures (e.g., ticket cancelled on airline side but refund failed on OTA wallet side) with automated multi-system rollbacks.
ADR-004
Why vLLM / SGLang Constrained Decoding (CFG) + NeMo Guardrails over Native Function Calling?
Alternatives
Standard OpenAI-Style Native Function Calling · Unconstrained Prompt-Based JSON Output Parsing
Decision
Enforce strict Context-Free Grammar (CFG) constrained decoding at the GPU token sampling level via SGLang / Outlines on vLLM, paired with NeMo Guardrails and Llama Guard 3 security middleware.
Business impact
Eliminates 100% of JSON syntax/schema parsing errors and halts prompt injection attempts before non-deterministic LLM output can hit critical financial APIs (OTA Wallet & GDS write endpoints).
Production AI System 04
Automotive & Mobility System 04
SEA Regional Automotive CDP & Intelligence Engine
An enterprise-grade Customer Data Platform (CDP) and AI intelligence layer unifying distributed multi-dealer databases across Southeast Asia into a single identity-resolved customer graph for hyper-personalized buyer targeting, social identity enrichment, and high-intent lead routing.
- Business context
- A major automotive manufacturer operates hundreds of independent dealership franchises across Indonesia and Southeast Asia. Siloed CRM databases, fragmented buyer journeys across offline showrooms and digital channels, and lack of cross-dealer visibility prevented central marketing teams from unifying customer profiles, mapping buying preferences, and executing targeted omnichannel conversions.
- Business problem
- Customer acquisition suffered from severe identity fragmentation, duplicate leads, uncoordinated dealer outreach, and inability to capture digital/social footprints. The enterprise needed a privacy-compliant, zero-copy unified customer graph to resolve identities across legacy DMS (Dealer Management Systems), enrich buyer preferences, and deploy real-time ML lead scoring to local sales reps.
Technology stack
Python PySpark / Polars DuckDB Snowflake / Databricks Feast Feature Store Graph Database (Neo4j) XGBoost / LightGBM FastAPI / gRPC
Constraints
- Cross-border regional data sovereignty & PDPA/UU PDP compliance
- Sub-100ms identity resolution across legacy dealer CRMs
- Zero-data-leakage architecture across competing dealership groups
- Real-time social media and contact info enrichment pipeline
System Benchmarks & SLAs
Identity Match Accuracy 98.4%
CDC Ingestion Lag < 450ms
Feature Store P99 Latency < 18ms
Lead Conversion Lift +38%
Streaming across 200+ regional dealerships, probabilistic identity resolution and Feast Feature Store deliver 98.4% match accuracy and sub-18ms ML feature retrieval to drive sales lead conversions.
Deployment
- CDC (Change Data Capture) via Debezium from dealer SQL databases to Kafka
- Streaming entity resolution and graph linking using PySpark & Neo4j
- Low-latency feature serving via Feast and Redis for dynamic lead scoring
- Event-driven webhook dispatch to local WhatsApp Business API & dealer CRMs
Monitoring
- Identity match accuracy & graph cluster entropy
- CDC pipeline ingestion lag (<500ms target)
- Feature drift & model prediction decay (PSI/CSI)
- Feature store retrieval p99 latency (<20ms)
- Lead conversion rate lift across dealer networks
Business impact
- 360-degree unified view of 4.5M+ automotive buyers across 200+ regional dealerships
- 38% reduction in customer acquisition costs (CAC) via lookalike social targeting
- 2.4x improvement in high-intent test drive conversions through ML lead routing
- 100% elimination of cross-dealer duplicate outreach and contact collisions
Lessons learned
- Identity resolution in multi-dealer ecosystems fails if strictly deterministic; combining probabilistic graph matching with crypto-hashed national ID/phone hashing is essential to resolve fragmented offline showroom visits with online social footprints safely.
- Decoupling central model scoring from dealer-facing CRM execution via a real-time feature store guarantees sub-100ms lead scoring without risking data leakage between competing franchise groups.
Architecture Decision Records (2)
ADR-001
Why Hybrid Deterministic-Probabilistic Identity Graph over Flat Hash Keying?
Alternatives
Single Unique Email Matching · Centralized Master DB Replication
Decision
Implement a hybrid identity graph (Neo4j) pairing exact matches (phone/hashed ID) with probabilistic fuzzy matching (name, address, social handles).
Business impact
Successfully linked 72% of previously disconnected offline showroom visitors to online behavioral profiles without exposing PII.
ADR-002
Why Feature Store Architecture for Cross-Dealer Lead Scoring?
Alternatives
Batch Nightly ETL to Local CRM · Direct API Queries to Legacy DMS
Decision
Deploy Feast Feature Store backed by Redis to serve offline-trained propensity features directly to real-time scoring endpoints.
Business impact
Reduced feature retrieval latency from 4.2 seconds to 12 milliseconds, enabling instant sales rep alerts during active buyer browsing.
Production AI system 05
Enterprise Document AI System 05
Intelligent Document Information Extraction Platform
A document intelligence pipeline that converts heterogeneous business documents into validated structured data with confidence-aware human review.
- Business context
- Enterprise documents arrive in inconsistent formats and often contain information that must be normalized before it can enter downstream business systems.
- Business problem
- The system must combine OCR, layout understanding, classification, multimodal reasoning, and validation without allowing low-confidence extraction to silently become trusted business data.
Technology stack
OCR Layout Detection Document Classification Multimodal AI JSON Schema Business API
Constraints
- Heterogeneous document formats
- Layout and table understanding
- Confidence-aware extraction
- Human review for ambiguous cases
- Stable JSON contract for downstream APIs
System Benchmarks & SLAs
Field Extraction Precision 98.2%
End-to-End Doc SLA < 1.2s
Straight-Through Processing 86.0%
PII Masking Accuracy 99.99%
Combining hybrid OCR and multimodal AI, the document pipeline achieves 98.2% extraction precision and an 86.0% straight-through processing rate with automated failover to human review queues.
Deployment
- Asynchronous document ingestion
- Stage-level retries and failure isolation
- Schema validation before API delivery
- Human review queue for low-confidence outputs
Monitoring
- OCR quality
- Extraction confidence
- Schema validation failures
- Processing latency
- Human review rate
Business impact
- Reduced manual extraction
- Faster document processing
- Structured downstream data
- Controlled automation
Lessons learned
- Document AI should be treated as a pipeline with explicit quality gates rather than a single model invocation.
- Confidence scoring is valuable only when it changes the workflow, especially by routing ambiguous documents to human review.
Architecture Decision Records (2)
ADR-001
Why staged document understanding?
Alternatives
Single multimodal prompt · OCR-only extraction
Decision
Use explicit OCR, layout, classification, and reasoning stages so each failure mode can be observed and controlled.
Business impact
Improves debuggability and allows quality gates to be placed before structured data reaches business systems.
ADR-002
Why confidence-gated human review?
Alternatives
Fully automated extraction · Manual processing for every document
Decision
Route low-confidence or validation-failing outputs to a human review path instead of silently publishing them.
Business impact
Balances automation efficiency with the trust requirements of enterprise data workflows.
Production AI system 06
Banking System 06
Early Loan Payoff Prediction Platform
A supervised prediction platform that converts core-banking data into calibrated payoff propensity signals for CRM and business decision workflows.
- Business context
- Banking teams can use timely propensity signals to understand customer behavior and prioritize appropriate relationship-management actions.
- Business problem
- The platform must turn operational banking data into stable, explainable predictions while monitoring calibration and model behavior after deployment.
Technology stack
Core Banking Python LightGBM SHAP Prediction API Dashboard CRM
Constraints
- Feature quality across operational source systems
- Explainable prediction behavior
- Calibration and threshold management
- Model and data drift monitoring
- Reliable CRM delivery
System Benchmarks & SLAs
Batch Pipeline Throughput 1.2M recs/min
Model ROC-AUC Score 0.884
Feature Drift Alert Threshold PSI < 0.10
Realtime API Latency < 35ms
Processing 1.2M banking records per minute, the calibrated LightGBM model achieves a 0.884 ROC-AUC with TreeSHAP explainability, serving real-time propensity scores to CRM workflows in under 35ms.
Deployment
- Scheduled feature generation
- Versioned model training and validation
- Controlled prediction release
- Dashboard and CRM delivery with model-version lineage
Monitoring
- Calibration
- Feature drift
- Prediction distribution
- Model performance
- Data freshness
- CRM delivery
Business impact
- Prioritized relationship actions
- More explainable predictions
- Earlier behavioral signals
- Operational visibility
Lessons learned
- For business-facing predictions, calibration and explainability are operational requirements rather than optional model-analysis features.
- Drift monitoring should connect technical signals to the business population and decision thresholds affected by the model.
Architecture Decision Records (2)
ADR-001
Why LightGBM for propensity prediction?
Alternatives
Logistic regression · XGBoost · Deep neural network
Decision
Use LightGBM when structured tabular features and fast retraining provide a strong fit for the prediction problem.
Business impact
Keeps iteration and serving complexity proportional to the structured-data problem while retaining useful feature attribution workflows.
ADR-002
Why explicit calibration monitoring?
Alternatives
Raw model scores only · Threshold-only monitoring
Decision
Monitor calibration and score distributions alongside conventional model performance signals.
Business impact
Makes downstream decision thresholds more trustworthy as customer populations and data distributions change.
Open Source Engineering
Engineering reusable systems, not one-off notebooks.
An enterprise-grade hybrid recommendation framework engineered with high-performance Cython/OpenMP C-extensions, DuckDB columnar feature pipelines, Group-aware Learning-to-Rank (LTR), MLflow tracking, and SHAP explainability.
AryColBring Kernels
Cython/OpenMPHigh-throughput collaborative filtering with parallel C-extensions via Cython & OpenMP for ultra-low latency matrix factorization.
LTR-LightGBM & Ranking Engine
LGBM/OptunaGroup-aware LambdaMART ranking implementation optimized for multi-objective CTR & conversion prediction.
Feature Store & Telemetry
DuckDB/MLflowIn-memory analytical querying on SQL layers for zero-copy feature transform and model experiment tracking.
Ary2Tower Core
Cython/CNumpyHigh-performance Two-Tower retrieval algorithm optimized with Cython prange multi-threading for ultra-low latency feature attribution and NDCG slice diagnostics.
Multi-Stage Environment Pipeline
Cooprecsys End-to-End Architecture
System Architecture
Production Workflow
- 01 Ingest high-throughput Parquet logs using DuckDB with zero-copy PyArrow memory integration
- 02 Engineer sparse user-item interaction matrices & temporal features in Local Feature Store
- 03 Accelerate candidate retrieval using parallel Cython/OpenMP kernels (Ary2Tower) via prange multi-threading
- 04 Train LambdaMART Learning-to-Rank (LTR) models with group-aware query structures
- 05 Validate offline ranking metrics (NDCG@10, MAP, Diversity) against quality guardrails
- 06 Execute automated fallback to User-Item or Item-Item similarity heuristics when model candidate pool falls short of requested Top-K during inference
- 07 Deploy low-latency, asynchronous gRPC/FastAPI inference microservices on GKE/Cloud Run (<15ms p99 latency)
- 08 Audit real-time prediction feature attribution & NDCG slices via SHAP Diagnostics Studio
Engineering Roadmap
- Integrate ScaNN / FAISS vector index (ANN) to scale Ary2Tower embeddings for sub-millisecond retrieval
- Develop C++/Pybind11 bindings for real-time online feature transformations
- Implement dynamic category-aware diversity re-ranking (e.g., Maximal Marginal Relevance) to avoid recommendation fatigue
- Extend Two-Tower architecture with multi-task prediction heads for joint interaction and engagement scoring
- Apply INT8 quantization and ONNX runtime execution to minimize model memory footprint during inference
- Establish automated CI/CD micro-benchmarking and Cython memory-safety checks (AddressSanitizer)
Engineering philosophy
Principles that survive changing technology.
Frameworks change. Models change. Infrastructure changes. The operating principles should remain stable.
Reproducibility
A result that cannot be recreated is not a dependable production asset. Version data, features, code, configuration, and model artifacts.
Observability
Production AI needs technical telemetry and business signals. Latency, failures, drift, confidence, and outcomes belong in the same operating conversation.
Scalability
Scale the architecture where the workload actually grows. Avoid premature infrastructure while keeping boundaries ready for the next stage.
Automation
Automate repeatable work across testing, training, deployment, validation, and monitoring so engineering attention stays on high-value decisions.
Business Alignment
A technically elegant system is still a failure if it does not improve a real business decision. Start from the workflow and work backward to the technology.
Delivery process
From ambiguous problem to operating system.
A disciplined delivery path keeps discovery, engineering, and operational ownership connected.
- 01
Discovery
Clarify the business decision, users, constraints, data reality, and definition of success.
- 02
Architecture
Establish system boundaries, data flow, interfaces, deployment shape, and technical trade-offs.
- 03
Prototype
Prove the highest-risk assumptions with the smallest credible end-to-end slice.
- 04
Production
Harden the workflow with testing, versioning, deployment automation, security, and operational ownership.
- 05
Monitoring
Measure system health, model behavior, data quality, drift, and business outcomes continuously.
- 06
Knowledge Transfer
Document decisions, operating procedures, architecture, and ownership so the system outlives the implementation team.
Enterprise AI systems
Build the system,
not just the demo.
If the challenge involves turning AI capability into a dependable production platform, let's discuss the architecture, constraints, and path to delivery.