The Automated Onboarding
Engine for Customer Data.
Stop manually mapping files. Elvity uses AI to build deterministic, high-scale pipelines that ingest, validate, and clean data from CSV, JSON, PDF, and Images—automated in minutes, not weeks.
Trusted by leading companies
How it Works
Three steps to a production-ready onboarding pipeline.
Feed the Engine
Upload sample files of any format. No strict schema required initially.
Prompt to Pipeline
Write plain English instructions. Our AI builds a deterministic pipeline instantly.
Inspect & Refine
Total Transparency. Visually inspect and update the built pipeline. No black box.
An Engine that
Auto-Adapts to Change.
Schema drift breaks most importers. When your customer suddenly changes "Total_Price" to "total_amt", Elvity doesn't crash. It auto-patches the pipeline, keeps data flowing, and sends you a notification.
- Detects structural changes automatically
- Applies self-healing AI patches instantly
- Zero downtime for data pipelines
Enterprise-Grade Capabilities
Precision-engineered for complex, high-stakes data environments.
Million-Row Performance
Built on a high-performance streaming architecture. Process massive datasets without dropping a single frame.
Multi-Engine Consensus Accuracy
Cross-references multiple OCR engines and verifies underlying math. If it passes Elvity, it's accurate.
Deep Validations & Logic
Integrate REST calls, webhooks, and complex custom logic. Not just formatting—real semantic validation.
Give your users a portal that talks back.
Embed our white-labeled upload portal directly in your app. It catches errors in real-time and communicates clearly with your users, drastically reducing support tickets.
Drag & Drop your Q3_Financials.csv
AI Review Complete
"Your total doesn't match the sum of line items; please check the subtotal column on row 42."
Why Elvity Wins
Built for modern data teams who demand more than a pretty UI.
Customer Stories
Real results from teams who replaced manual data work with Elvity.
Grocery Tech95% Reduction in Manual Catalog Onboarding
Birdzi's AI-powered loyalty platform for grocery chains depended on clean product catalog data from clients—but the incoming data was riddled with wrong images, missing nutritional info, and inconsistent formatting. Their ops team spent days manually fixing each new catalog, blocking revenue and limiting growth.
"We were drowning in manual data cleaning. Onboarding a new partner meant our team had to stop everything and spend days just fixing basic product information. It was slow, expensive, and frankly, not a good use of their talent. We knew we couldn't scale the business this way."
The fix: A single plain-English prompt to Elvity generated a production-ready pipeline that ingests new catalogs, cross-references product data, runs AI-powered web lookups against Amazon, Target, Walmart and Instacart, scores product images for quality, and structures nutritional data—all automatically.
Download full case study (PDF)87% Faster Enterprise Customer Data Onboarding
A Series B payments platform onboards enterprise customers who submit years of transaction history in dozens of inconsistent formats—different column names, date formats, currency representations, and reconciliation structures per customer. Each new enterprise client previously required a custom two-week ETL build from the engineering team.
"Every new enterprise customer used to mean a two-week engineering sprint just to normalize their transaction files. Now Elvity builds the pipeline automatically from a description of what we need—and it self-heals when a customer changes their export format without telling us."
The fix: Elvity built a self-healing pipeline from a plain-English description—parsing any CSV or Excel variant, validating transaction amounts against statement totals, flagging anomalies, and auto-patching when column schemas drift between uploads.
91% Faster HRIS Data Migration Across Acquired Companies
A Series C HR platform handles employee data migrations whenever their customers acquire or merge companies. Each source system—Workday, BambooHR, ADP, SAP—exports records differently: different field names, date formats, employee ID structures, and benefits codes. Every migration previously required a dedicated implementation sprint lasting 3–4 weeks.
"We handle dozens of HRIS migrations a year. Each one used to require a custom implementation sprint just to normalize field names and date formats. Now we give Elvity a sample export and a plain-English description of our target schema, and it builds the entire mapping pipeline in minutes—including validation rules we'd normally write by hand."
The fix: Elvity auto-detected the schema from any HRIS export format, mapped fields to the platform's canonical employee model, validated required fields like SSN format, dates, and benefits eligibility windows, and flagged discrepancies—all from a single prompt.
Stop Engineering Importers.
Start Onboarding Data.
Deploy your first automated onboarding engine in under 5 minutes.