Trusted by leading brands
Bad data architecture kills AI before it starts.
Every failed AI project has the same root cause. The data wasn't ready.
Policy data in Duck Creek. Claims in a custom system built in 2005. Billing in Excel. Customer history in a CRM nobody updates. That's not a data problem — that's four data problems that compound every time you try to build something on top of them.
The reality most vendors won't tell you: You can have the best AI model in the world. If it's pulling from fragmented, inconsistent, unstructured data — it will fail.
What this costs you:
Pilots that work in demo and break in production.
Models trained on dirty data that make wrong predictions.
Months of engineering time cleaning data instead of building.
You don't need a bigger AI budget. You need a solid foundation first.
83%
of AI projects fail due to data readiness issues
$40B+
lost annually on AI projects that never reach production
3x
faster AI deployment when data architecture is addressed first
End-to-end data infrastructure for AI-ready organizations
Data Integration & Pipelines
Connect your systems and make data flow.
Use cases:
- ETL pipelines (extract, transform, load from legacy systems)
- Real-time event streaming (Kafka, Kinesis)
- API integrations (REST, SOAP, EDI)
- Legacy system connectors (mainframes, FoxPro, homegrown platforms)
Data Warehousing & Lakes
One place. All your data. Clean.
Use cases:
- Cloud data warehouses (Snowflake, Databricks, BigQuery)
- Data lakes for unstructured data (documents, images, sensor data)
- Unified data models across business units
- Historical data migration from legacy systems
Data Quality & Governance
Data you can trust. Compliance you can prove.
Use cases:
- Data quality frameworks (validation, cleansing, monitoring)
- Master data management (single source of truth)
- Data lineage tracking (where did this number come from?)
- Regulatory compliance (HIPAA, CCPA, SOX)
Feature Stores & ML Infrastructure
Data that serves AI at scale.
Use cases:
- Feature stores (precomputed variables for ML models)
- Real-time feature serving (low-latency predictions)
- Training data pipelines (historical data for model development)
- Model monitoring infrastructure (detect data drift)
Every organization is different. We tailor these solutions to your systems, data, and goals, starting with a conversation to understand what will deliver the most impact.
Book time with an expertFrom Data Chaos to
AI-Ready Infrastructure
Phase 1
Discovery & Assessment
Map all data sources, assess data quality, identify integration gaps, define AI use cases that depend on this infrastructure.
Deliverables
- Data architecture assessment with gap analysis
Phase 2
Architecture Design
Design target state architecture, select technologies, define data models and governance framework, build migration roadmap.
Deliverables
- Architecture blueprint with implementation plan
Phase 3
Build & Integrate
Build data pipelines and integrations, deploy data warehouse or lake, implement quality controls, connect to existing systems.
Deliverables
- Working data infrastructure
Phase 4
AI Enablement
Build feature stores for ML models, validate data quality for AI training, enable real-time data serving.
Deliverables
- AI-ready data infrastructure with documentation
"DOOR3 is a trusted partner in the designing and building of our digital properties. For this project, DOOR3 was able to meet our goals within a tight timeline, working closely with our team."
Joe Lalle, Vice President, Digital Product & Operations, WWE
The DOOR3 difference
We map what you actually have
Not what your org chart says you have. What's real. Shadow IT. Homegrown databases. The FoxPro system 80 people use daily. The spreadsheet finance runs everything from.
We find it all, document it, and design around it. No rip-and-replace mythology.
We build for AI, not just storage
Most vendors optimize your data for dashboards. We optimize it for decisions. Unified models AI can query. Real-time pipelines for live decisions. Feature stores that serve ML at speed. Governance that satisfies compliance without killing momentum.
We integrate what exists
Your legacy systems aren't the problem. Trying to replace them is. We build data architecture that works with Duck Creek, Guidewire, SAP, Oracle, and the custom platform your team built before the iPhone existed.
1,000+ integrations. We know exactly where it breaks.
Ready to turn messy data into a competitive advantage?
Let's discuss your systems, data quality gaps, timeline, and what production-ready architecture looks like for you.
