AI-native platform

The file pipeline
that learns.

Intelligence at every layer. Not a feature bolted on, but architecture built from the ground up. Every client you onboard makes the system smarter. Every file processed improves the next.

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Traditional ETL has an automation ceiling

Traditional tools automate execution but not configuration. Every new client requires manual mapping, transformation code, and validation rules. The engineering burden scales linearly with your client count.

4-8 hrs

to configure a new client pipeline

30%

of data eng time on mapping maintenance

support tickets when clients double

72%

say onboarding is their scaling bottleneck

Five layers of intelligence

Not one AI feature. Five interconnected layers that share a common learning system and improve simultaneously.

Intelligent ingestion

Files arrive via SFTP, email, API, or upload. The AI analyzes structure, detects format, identifies encoding, and flags anomalies before a single row is processed.

Volume anomaly detection
Schema drift alerts
Format and encoding auto-detection
Duplicate file fingerprinting

AI field mapping

Multi-signal analysis considers headers, sample data, data types, and the complete history of mappings across all your clients. Confidence scores improve with every file.

Cross-client mapping memory
80-95% auto-resolution after learning period
One-click accept for high-confidence matches
Historical pattern recognition

Natural language transformations

Describe transformations in plain English. The AI generates the function and shows a live preview against your actual data. No code, no deployment.

"Convert all dates to ISO 8601"
"Split full name into first and last"
"Map department codes to full names"
"Remove non-numeric characters from phone"

Smart validation

The AI analyzes your data and suggests validation rules automatically. Data-driven schema design instead of guesswork.

Email, phone, SSN format detection
Enum suggestions from categorical values
Range validation from numeric distributions
PII identification and flagging

Conversational management

Set up pipelines, investigate failures, modify configurations, and generate integration code through natural language conversation.

AI Assistant in the dashboard
Complete pipeline setup in 3 minutes
38 MCP tools for IDE integration
Works with Claude Desktop, Cursor, VS Code

The compounding effect

In traditional ETL, every new client is a linear cost. Same effort for the 100th client as the 10th. In FileFeed, every new client makes the system smarter. Mapping confidence increases, auto-resolution rates climb, validation suggestions become more accurate. The per-client cost decreases as your client base grows.

80-95%

fields auto-mapped

3 min

pipeline setup via AI

< 1 min

to investigate a failed run

transformation code written

Three AI-native interfaces

Same intelligence, three ways to work.

Dashboard

Visual interface with AI suggestions pre-filled. Mapping recommendations, validation suggestions, and anomaly alerts surface automatically. Override anything manually.

Best for: Operations and CS teams

AI Assistant

Conversational interface embedded in the dashboard. Upload files, describe what you need, and the assistant builds the entire pipeline. Every write operation requires your approval.

Best for: Anyone who knows the requirements

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MCP Server

38 tools accessible from Claude Desktop, Cursor, VS Code, and any MCP-compatible assistant. Manage pipelines without leaving your IDE.

Best for: Engineering teams