DATA & AI PLATFORMS

Data Platform Modernization for the AI Era

Enterprise data platform modernization requires an AI-native approach. Most AI projects fail because the data underneath them is broken: siloed systems, inconsistent schemas, missing context, and pipelines that snap under production load. MetaSys builds the data foundation first, then deploys AI on top of it for enterprise clients who need results in production, not just in demos.

Talk to a Data Architect See what we build

Foundation-first approach|Real-time and batch|Cloud-native

DATA PIPELINE STATUS

SourcePostgres + S3 + Kafka

DestinationSnowflake + Pinecone

[✓]Raw ingestion: 2.4M events/day

[✓]Transformation: dbt (84 models)

[✓]Vector sync: 1.2M embeddings

[✓]RAG retrieval: p95 < 280ms

[→]Drift check: running

[ ]Next retrain: 14 hrs

Data freshness:< 4 min lag

Pipeline uptime:99.94%

Last incident:6 days ago

THE PROBLEM

Bad data architecture kills AI before it starts.

Siloed and disconnected data

Your customer data is in the CRM. Your transactions are in the ERP. Your operational data lives in spreadsheets. AI cannot reason across data it cannot see.

Pipelines that break under load

Batch jobs that run overnight and fail silently. No monitoring, no alerting, no recovery. By the time someone notices, the dashboards are showing stale data from three days ago.

Retrieval that returns the wrong context

RAG systems that retrieve poorly make your AI confidently wrong. Chunk size, embedding model, retrieval strategy, and reranking all matter. Most teams skip all of them.

WHAT WE BUILD

Five data infrastructure layers we deliver.

Data lakehouses and warehouses

We design and build your central data store on Snowflake, Databricks, BigQuery, or a custom lakehouse architecture. Structured for analytical queries, AI feature extraction, and real-time access patterns.

Snowflake, Databricks, BigQuery, Delta Lake

Data pipelines and transformation

Ingestion from any source, transformation with dbt or Spark, orchestration with Airflow or Prefect. We build pipelines that run reliably in production and recover gracefully when upstream systems break.

dbt, Airflow, Prefect, Spark, Kafka

RAG systems and vector infrastructure

Retrieval-Augmented Generation done properly. We design your chunking strategy, embedding model selection, vector database schema, and retrieval pipeline so your AI retrieves the right context with every query.

Pinecone, Weaviate, pgvector, OpenAI Embeddings

Real-time data streaming

Event-driven architectures using Kafka, Kinesis, or Pub/Sub that give your AI systems access to live operational data, not last night's batch. Built for high-throughput, low-latency production environments.

Kafka, Kinesis, Pub/Sub, Flink

ML feature stores and model infrastructure

Feature engineering pipelines, training data versioning, model registries, and serving infrastructure. The plumbing that makes model development fast and model deployment reliable.

Feast, MLflow, SageMaker, Vertex AI

OUR APPROACH

Data architecture is an engineering discipline, not a sprint task.

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Week 1

Data audit

We map every data source, schema, and pipeline in your current environment. We identify gaps, inconsistencies, and the highest-leverage fixes before touching anything.

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Week 1-2

Architecture design

We design the target architecture: storage layer, transformation layer, serving layer, and AI access patterns. You get a written spec before any build work.

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