Turn raw data into production-ready AI infrastructure
Data pipelines, warehouses, and analytics infrastructure engineered for AI workloads. We build the foundation that makes machine learning actually work—clean data, reliable pipelines, and scalable architecture.
Trusted by forward-thinking organizations
80%
Faster Data Prep
99.9%
Pipeline Uptime
10x
Query Performance
<1hr
Data Freshness
Data infrastructure that AI can actually use
Most AI projects fail because of data problems, not model problems. We build the infrastructure that solves this: data pipelines that handle messy real-world inputs, warehouses optimized for analytical and ML workloads, and quality systems that catch issues before they corrupt your models. Our clients skip the 6-month data cleanup phase and go straight to production ML.
Benefits
Ship ML features without data engineering bottlenecks
Self-service data infrastructure that lets data scientists and ML engineers access clean, joined, and transformed data without waiting on engineering tickets. Feature stores, embedding pipelines, and training datasets on demand.
Go from raw data to production ML in weeks
Pre-built patterns for common data sources (CRMs, ERPs, event streams) with automatic schema detection and quality validation. Most clients have their first ML model in production within 6 weeks.
Scale without replatforming
Architecture designed to grow from gigabytes to petabytes without rewrites. Start with cost-effective solutions and scale infrastructure as your data volume and ML usage demands increase.
Use cases
Production Data Pipelines
Reliable ETL/ELT pipelines that handle real-world data at scale. Automatic error handling, data validation, and monitoring. Never lose data or miss a load.
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Modern Data Warehouse
Cloud-native warehouses optimized for analytics and ML workloads. Separation of storage and compute, automatic scaling, and query optimization.
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ML Feature Store
Centralized feature management for ML teams. Store, version, and serve features consistently across training and production. Eliminate training-serving skew.
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Vector & Embedding Infrastructure
Infrastructure for AI applications: embedding generation, vector storage, and similarity search. The foundation for RAG, semantic search, and recommendation systems.
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Data Quality Systems
Automated quality monitoring that catches issues before they impact downstream systems. Data contracts, anomaly detection, and alerting for data teams.
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Case Studies
See how our clients build with Zunkiree
Rapid Investment transforms data processing
Rapid Investment needed real-time data analytics to power their investment decisions. We built a data system that processes millions of data points.
CMS Group automates enterprise workflows
CMS Group transformed their operations with custom enterprise software that automated manual workflows and improved team collaboration.
Technology
Technologies we use
Snowflake
Warehouse
Cloud DW
dbt
Transform
Data Modeling
Airflow
Orchestration
Workflow DAGs
Fivetran
Ingestion
ELT Pipelines
Pinecone
Vector DB
Embeddings
Spark
Processing
Big Data
Kafka
Streaming
Event Streams
Databricks
Platform
Lakehouse
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Custom AI systems using RAG pipelines, LLM integration, and intelligent automation. We build AI that solves specific business problems, not generic chatbots.
Custom Software
Enterprise applications tailored to your workflows. We build internal tools and business systems that teams actually want to use.
SaaS Development
Multi-tenant platforms with subscription billing, user management, and scalable architecture. Launch your SaaS product in months, not years.
Take the next step
Let's assess your current data infrastructure and identify the fastest path to production-ready ML. Free data audit for qualified projects.
Get to know Data Systems
A full migration takes 3-6 months depending on complexity, but we deliver value incrementally. Most clients have their first critical data sources in production within 4-6 weeks, with expansion happening in parallel with business-as-usual operations.
Modern lakehouse architectures combine both. We typically recommend cloud warehouses (Snowflake, BigQuery) for structured analytics and Databricks/Delta Lake for ML workloads. The right choice depends on your data types, team skills, and use cases.
We implement quality checks at every stage: schema validation on ingestion, data contracts between teams, anomaly detection on key metrics, and automated alerting. Issues are caught before they propagate, not after they've corrupted downstream models.
Data warehouses serve analytics queries; feature stores serve ML systems. A feature store ensures that the features used to train a model are exactly the features served in production—eliminating training-serving skew, which is a major cause of ML model failures.
Start with batch unless you have a clear real-time use case. Batch is simpler, cheaper, and sufficient for most analytics. When real-time matters (fraud detection, personalization), we implement streaming with Kafka or managed services, with batch as fallback.