Build AI and Data Platforms That Deliver Measurable Business Value
From fragmented source systems to reliable AI, analytics, and decision workflows.
One team from first decision to deployment
Built around the decision or workflow that matters
Reliable systems, not one-off prototypes
Your team can own and operate what we build
Bring us the hard data problem.
We help turn uncertainty into a practical, scoped path forward.
Data is fragmented
Important information lives across systems and is hard to use reliably.
Reporting is manual
Teams spend too much time preparing numbers instead of acting on them.
An AI use case is stuck
A promising proof of concept needs a reliable route into production.
You need a roadmap
You need to prioritize the next data or AI investment with confidence.
What we build.
Six service pillars — from strategy to production deployment.
AI & Generative AI
Enterprise AI assistants, RAG pipelines, document intelligence, and LLM integration.
Machine Learning
Forecasting, recommendation, classification, and optimization — deployed to production.
Data Engineering
ETL/ELT pipelines, streaming, warehouses, and lakehouse architectures at scale.
Cloud Platforms
AWS, Azure, GCP architecture, migration, and infrastructure-as-code.
Business Intelligence
Executive dashboards, real-time KPIs, self-service analytics.
Data Strategy
Architecture, governance, roadmaps, and platform modernization.
End-to-end data platform architecture
From raw source systems to actionable intelligence — we design and build the complete pipeline.
- Ingest from any source — SAP, Salesforce, IoT, APIs, databases
- Transform with dbt, Spark, or custom Python pipelines
- Store in Snowflake, Databricks, or cloud-native warehouses
- Serve ML models, dashboards, and real-time APIs
Production work. Measurable outcomes.
Selected results from work delivered by Serenlytics founders in prior professional roles.
Multilingual product classification
Built a RAG-based workflow that retrieved and classified product-listing candidates, routing lower-confidence results to an LLM for validation.
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A multilingual BGE-M3 embedding model was fine-tuned for a 12-language catalog. A two-stage hybrid-search pipeline retrieved candidate matches before structured validation and production evaluation.
Retail fraud detection
Led machine-learning initiatives for fraudulent activity identification, applying supervised, semi-supervised, and unsupervised techniques to transaction data at scale.
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The work combined anomaly detection with operational data workflows to identify fraudulent activity more accurately and reduce the review burden caused by false positives.
Forecasting and inventory optimization
Directed analytics initiatives that improved inventory planning, optimized supply-chain operations, and surfaced phantom-inventory issues across store locations.
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The initiative combined demand forecasting, inventory analysis, and operational decision support to improve planning and reduce the impact of phantom inventory.
Credit-risk customer grouping
Designed a network-based householding scheme to identify customer groups with common risk characteristics for use in rating models.
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The work paired data-processing pipelines with network theory to surface related customer groups across 300 customers, delivering a 5% lift to the risk model.
Relationship-manager text classification
Prepared data and deployed a random-forest model to classify proactive client-engagement text from relationship managers.
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The project covered data preparation, model development, and containerized deployment in Python, classifying more than one million messages with 95% accuracy.
Funding-integrity analytics
Analyzed relief-program data to identify linked applications, flag high-risk cases for review, and strengthen the integrity of business-funding decisions.
View project detail
The work combined analytics, NLP, and network analysis with cross-organization data-quality work, QA/UAT test-data preparation, and removal of personally identifiable information from public data.
How we deliver.
A proven five-phase approach from discovery to production support.
Discovery
Understand data, KPIs, constraints
Architecture
Design the solution with your team
Prototype
Validate with a working proof
Production
Ship to your infrastructure
Support
Monitor, tune, hand off cleanly
Enterprise-grade expertise.
Our team brings director-level experience from enterprise data and ML organizations.
Ph.D.-Level Depth
Founders hold doctorates in physics and applied math. We bring research rigor to production engineering.
Seasoned Data & Analytics Professionals
Seasoned data & analytics professionals who understand what it takes to move from idea to production
Production at Scale
We've built systems processing 17M+ records daily. Our work runs in CI pipelines, not notebooks.
Cloud Native
AWS, Azure, GCP — we build for the cloud you're already on, using IaC from day one.
ROI First
Every engagement starts with the business metric. We ship the smallest change that moves it.
Full Knowledge Transfer
Your team owns the system when we leave. Documented, tested, no lock-in.
Built for complex domains.
Modern stack. Battle-tested tools.
Ready to Modernize Your Data Platform?
Whether you're building a modern data warehouse, deploying AI into production, or creating executive dashboards — we help you move from idea to production faster.
Start with a free 30-minute technical discovery call. No obligation and no generic sales pitch.