Data Fragmentation
Customer, product, and operational data sit in warehouses, CRMs, and files that do not agree. Every AI initiative starts by reconciling sources instead of shipping.
Data preparation for enterprise AI
Your AI Strategy is Only as Good as Your Data. Clean the Garbage Before You Model.
We audit, structure, and sanitize legacy data ecosystems into
production-ready pipelines for Enterprise AI and Salesforce implementations.
High-velocity data preparation in a 3-week sprint.
Architected and engineered solutions for leading enterprise organizations worldwide.
- AT&T
- IBM
- Salesforce
- USAA
- Tata Consultancy Services
- Bradesco
- C&A
- BTG
- Cielo
- Yduqs
- RX Global
- Minsait
- CloudKitchens
The trap
Garbage in, garbage out — after the AI budget is already spent.
The expensive failure mode is not the model. It is hiring AI engineers and watching them stall on duplicate records, undocumented extracts, and spreadsheets that were never a system of record.
Legacy Technical Debt
Mainframe extracts, undocumented SQL, and one-off pipelines turn each new use case into a manual cleanup. The stack looks modern. The foundation does not.
AI Inaccuracy
Models trained on duplicate, stale, or unlabeled records answer with confidence leadership cannot use. The spend is committed. The output is not.
Productized service
The 3-week data prep sprint.
A fixed sequence. No open-ended discovery theater. The output is a data foundation an AI or Salesforce implementation can actually run on.
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01
Audit
Map sources, quality gaps, and the conditions that must be true before a model or Salesforce AI workflow is allowed into production.
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02
Clean & Map
Deduplicate, standardize, and map entities into a structure downstream systems can use without another cleanup cycle.
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03
Vectorization / Integration Framework
Prepare the cleaned corpus for retrieval and for Salesforce or warehouse integration, so implementation is not blocked on data.
Start with the audit, not the model.
Choose a time. Before the meeting is confirmed, company email, current stack, timeline, and budget are required.