DeepWave builds AI-ready data from documents, public sources, and unconventional signals.
DeepWave SourceFusion turns fragmented, high-volume inputs into governed AI-ready outputs: extracted pages, structured records, retrieval chunks for RAG, model-feature tables, API payloads, scheduled feeds, quality records, and source lineage that can move directly into production data systems.
AI-ready data engineering for sources too fragmented to use directly.
DeepWave specializes in source integration across documents, APIs, feeds, tables, refresh cadences, access methods, locations, and confidence levels. The output is not a dashboard alone; it is a usable AI-ready data layer for retrieval, analytics, forecasting, and enterprise workflows.
Messy sources become AI-ready data.
Document pages, files, feeds, and records are extracted, normalized, de-duplicated, time-aligned, geocoded, enriched, and packaged with schema, manifest, quality fields, and clear limitations.
RAG and model inputs are first-class outputs.
Each AI-ready dataset can include JSONL retrieval chunks, embedding-ready metadata, model-feature tables, API JSON, batch CSV, and validation summaries.
Lineage and quality travel with the data.
DeepWave keeps organization-level provenance, retrieval timing, geography, date precision, source family, confidence scoring, and limitation notes attached to records.
A repeatable pipeline for AI-ready datasets.
Collect public and customer-approved sources by PDF, web page, file, feed, API, table, and scheduled retrieval.
Standardize pages, chunks, time, location, units, source family, entity labels, and update cadence.
Add domain features, derived indicators, historical context, and retrieval-ready text.
Attach quality scoring, lineage status, limitation notes, and evidence records.
Ship AI-ready outputs as CSV, JSONL, feature tables, APIs, feeds, or cloud-marketplace packages.
Focused prebuilt datasets where integration difficulty creates the product.
DeepWave starts with categories where useful AI systems need more than a single public table: extreme weather, antimicrobial resistance, and atmospheric-persistence research. The same SourceFusion engine supports document processing and custom enterprise datasets when customers need a governed data layer for another domain.
Weather, mobility infrastructure, network-flow, event, atmospheric-persistence, and historical analog data.
Built for forecasting models, risk teams, emergency planning, grid analysis, prediction-market research, and AI systems that need location-specific context.
Antimicrobial-resistance evidence organized for surveillance and modeling.
Designed for public-health analytics, RAG systems, model features, pathogen/drug trend review, and preparedness workflows.
AI-ready data for any data-heavy category.
DeepWave can adapt SourceFusion to specialized public data, documents, web sources, unconventional context layers, customer reference data, and private deployment requirements.
Built for data science, retrieval, analytics, and operational teams.
Outputs fit RAG applications, forecasting pipelines, feature stores, dashboards, scheduled notebooks, procurement workflows, and governed enterprise systems.
Process pages, buy a dataset, provision a feed, or procure through AWS and Azure.
DeepWave offers sample outputs for review, document-page processing, account-based dataset access where enabled, and enterprise packages for APIs, scheduled feeds, RAG-ready exports, private endpoints, AWS Marketplace, Azure Marketplace, and customer VPC deployments.
PDF, report, web, OCR, and long-document processing into AI-ready outputs.
CSV, JSONL, manifest, schema, lineage, quality, and model-feature outputs.
AWS Marketplace, AWS Data Exchange, Azure Marketplace, and private offers.
Customer-scoped source coverage, endpoints, refresh cadence, and deployment boundaries.