Common Data Challenges Blocking AI Adoption
Companies are sitting on goldmines of data but can't leverage it for AI/ML because their data infrastructure is fragmented, poorly governed, and architected for reporting—not machine learning.
Data Silos & Fragmentation:
Data scattered across 20+ systems with no single source of truth makes AI/ML impossible. We unify your data into modern lakehouse architectures (Snowflake, Databricks, BigQuery) that support both analytics and AI workloads.
Data scattered across 20+ systems with no single source of truth makes AI/ML impossible. We unify your data into modern lakehouse architectures (Snowflake, Databricks, BigQuery) that support both analytics and AI workloads.
Not AI/ML Ready:
Legacy data warehouses can't support real-time machine learning, feature stores, or vector databases. We design AI-first data architectures that enable modern ML operations from day one.
Legacy data warehouses can't support real-time machine learning, feature stores, or vector databases. We design AI-first data architectures that enable modern ML operations from day one.
Core Data Consulting Services
Data Architecture & Strategy
- Data warehouse & lakehouse design (Snowflake, Databricks, BigQuery)
- Cloud data platform architecture (AWS, Azure, GCP)
- Real-time data streaming architecture (Kafka, Kinesis)
- Data mesh & domain-driven design
Data Engineering & Pipelines
- ETL/ELT pipeline development (dbt, Fivetran, Airbyte)
- Data integration & API development
- DataOps & workflow orchestration (Airflow, Dagster)
- Data quality & validation frameworks
AI/ML Data Infrastructure
- Feature stores & ML data platforms (Feast, Tecton)
- Vector databases for RAG & embeddings (Pinecone, Weaviate)
- MLOps pipelines & model deployment
- Training data management & versioning
Data Governance & Compliance
- Data governance frameworks & policies
- Data catalogs & metadata management (Collibra, Alation)
- GDPR, CCPA, HIPAA compliance
- Data lineage & observability (Monte Carlo)
Data Maturity Journey
Where is your organization on the data maturity curve?
Level 1: Data Chaos
Data in spreadsheets & siloed systems. No single source of truth. Manual reporting.
Level 2: Basic Infrastructure
Basic data warehouse. Some ETL pipelines. BI tools deployed but underutilized.
Level 3: Data-Driven Culture
Modern cloud platform. Automated pipelines. Self-service analytics. Data governance.
Level 4: AI-Ready Enterprise
Real-time data & ML in production. Feature stores. AI-powered analytics. Data as strategic asset.
Case Studies
BI Services Provider — AI-Accelerated Data Normalization: A business intelligence services firm relied on senior engineers hand-writing ETL in SQL, Python, SSIS, and DataStage to reconcile entity names across systems and rebuild 1,000+ legacy reports per acquisition. Synvestable built an Amazon Bedrock–powered workflow that ingests source ERP schemas and auto-generates Python/Spark normalization code for AWS Glue, with engineers only handling 10–20% of edge cases.
Digital Health Platform — Analytics Modernization: A digital health company managing a homegrown EMR had 74 static Power BI dashboards fed by manual MongoDB exports, maintained by one retiring employee. Synvestable deployed a MongoDB MCP integration within AWS VPC, establishing a read-only bridge to AWS QuickSight that enabled natural language text-to-NoSQL querying and dynamic dashboards — replacing a maintenance-dependent, retrospective reporting stack with live, prompt-driven analytics.
Event Ticketing Platform — Data Infrastructure Rebuild: A mid-market ticketing platform had $3.75M in abandoned cart revenue with operational records in MongoDB, reporting in MySQL-backed Zoho with 20-minute lag, and Premier product in separate Redshift. Synvestable deployed AWS DMS with CDC to continuously replicate MongoDB into Aurora PostgreSQL, replaced Zoho with QuickSight, and reduced reporting latency from 20 minutes to seconds — enabling automated cart recovery and AI chatbot access to live data.