Cognitive Data Processing
Most of your company's data is 'dark'—trapped in emails, PDFs, audio recordings, and images. We build cognitive pipelines that ingest this unstructured media at scale, extracting relationships, entities, and structured metrics to feed your analytics engines.
Core Features
Named Entity Recognition (NER)
Automatically identifying People, Companies, Locations, and Dollar Amounts buried in thousands of legal contracts.
Knowledge Graph Generation
Mapping the relationships between extracted entities (e.g., Company A owns Company B, which signed a contract with Person C).
Audio & Video Processing
Transcribing call center recordings and extracting action items, sentiment, and compliance violations automatically.
Data Normalization
Taking messy data (e.g., '100 USD', '$100.00', 'one hundred dollars') and converting it into a single, queryable database integer.
Our Process
Data Lake Integration
Week 1Connecting to your raw data storage (AWS S3, Azure Blob, SharePoint) where the dark data currently resides.
Pipeline Architecture
Week 2Designing the serverless architecture (AWS Lambda/Step Functions) required to process thousands of files simultaneously.
Model Deployment
Week 3-4Deploying specific ML models for specific data types (Whisper for audio, LayoutLM for PDFs, GPT for text reasoning).
Data Structuring & Graphing
Week 5Writing the logic that takes the raw model outputs and structures them into JSON, SQL rows, or Neo4j graph nodes.
Analytics Integration
Week 6Connecting the newly structured, clean database to your BI tools (Tableau, Snowflake) for executive reporting.
Technologies We Use
FAQ
What is 'Dark Data'?
Why use a Knowledge Graph instead of a regular database?
Can you process data securely on-premise?
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