Private Access

Automatic Data Consolidation
Powered by AI Intelligence

Consolidate massive files automatically. Detect structures, transform rows with regex logic, and ensure 100% data relevance using Pandas and LLMs.

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Postgres
AWS S3
Pandas
LangChain
Gemini Anthropic

Core Capabilities

Content Atlas handles the dirty work of ETL so you don't have to. From schema detection to duplicate protection.

Automatic Consolidation

Sample massive files, detect structures automatically, and stream datasets via API. We handle the complexity of merging scattered data.

Pandas-Powered Integrity

Ensure 100% data relevance. Merge data into existing tables, create new ones on the fly, or move data with precision using Pandas.

AI Transformation Logic

Apply Regex and complex transformation logic driven by AI. Clean, format, and standardize rows automatically during ingestion.

Privacy & Scale

Host 100% in-house, including the AI model. We only send samples to the AI, keeping your massive datasets secure and local.

Actionable Deduplication

Detect duplicate entries and take action: merge, skip, or flag. Our engine proactively manages data consistency.

Conversational Control

Discuss with the LLM to decide the best manipulation strategy. The AI suggests actions based on your specific data context.

Conversational Data
Manipulation

Discuss with the LLM to decide the best actions for your data. Whether it's applying complex regex, transforming rows, or deciding on merge strategies, the AI guides you through the process.

  • Interactive Strategy: Discuss how to handle duplicates, outliers, or messy formatting before running the job.
  • Logic & Regex: Ask the AI to "Extract domain names from emails" or "Format dates to ISO8601" using advanced logic.
  • Visual Confirmation: See the impact of your conversation on sample data immediately before processing the massive file.
Explore the Interface docs
AI
Mapping Assistant
Map clients_2024.csv. Please exclude the 'temp_id' column and merge into 'Global_Clients'.
YO
AI
I've analyzed the file. Here is the proposed mapping structure for the Global_Clients table.

Mapping Preview Ready to Merge

CSV Column Action Target Column
Client Name
name
Revenue
annual_rev
temp_id
Excluded
Ingestion Progress Processing... 45%

Row Validation Quality

Cloud-Native Architecture

1

Upload to S3

Direct streaming uploads. Files land securely in S3 buckets instantly.

2

Process In-Memory

Content Atlas engine maps, validates, and dedupes using parallel async workers.

3

Write to Postgres

Clean, 100% accurate data is committed to your database schemas.

Stop Wrestling with Excel.

Join the teams using Content Atlas to bridge the data gap.
Unlock your data. Unleash your AI.