Untangle your data with AI.
datathere makes partnerships and integrations simple.
Not simpler. Simple.
| Order # | SKU | Qty |
|---|---|---|
| ORD-7802 | APC-1520 | 12 |
| ORD-7803 | SNG-3300 | 4 |
GET /products/APC-1520 "sku": "APC-1520", "description": "USB-C Cable 2m", "weight_kg": 0.12
<price sku="APC-1520" region="US-West"> <amount>14.99</amount> <currency>USD</currency> </price>
Ready to map
Matches ORD-0000 patternDescription excludes restricted termsunit_price > 0.25 AND unit_price < 3000weight_kg > 0 AND weight_kg < 50Matches XX-XXXXX patternPipeline Run
Complete"order_id": "ORD-7802", "sku": "APC-1520", "description": "USB-C Cable 2m", "unit_price": 14.99, "currency": "USD", "quantity": 12
Connect anything
Partners send data in different formats. datathere handles all of them through the same mapping workflow — including joins across sources with complex expressions.
How it works
Connect
Upload files or connect an API. CSV, JSON, XML, PDF, TXT — handled through the same workflow regardless of format.
Map
AI analyzes field names, data types, and sample values to generate mappings with confidence scores and reasoning. Edit in plain English.
Certify
Review mappings, transformations, joins, and quality rules. Certification validates the configuration and locks it for production.
Run
Trigger manually, on a schedule, on file drop, or via webhook. An 8-phase pipeline processes records with quality enforcement at each step.
AI That Shows Its Work
AI generates field mappings, transformations, join conditions, and quality rules. Every suggestion includes a confidence score and the reasoning behind it.
Review. Certify. Run.
Every mapping is editable in plain English. Every transformation is visible before execution. Only certified mappings reach production.
Any Source, One Workflow
CSV, JSON, XML, PDF, and API sources follow the same integration workflow. Join across sources on any condition, including complex expressions.
Data Quality Profiling
datathere profiles your source data and creates quality rules. Run them independently or make them part of your pipeline's run rules.
Production-Grade From the Start
An 8-phase pipeline with scheduled, webhook, and file-drop triggers. Real-time job tracking with phase-level progress. Built as production infrastructure from day one.
Enterprise Without the Wait
SSO via SAML and OIDC. GDPR and CCPA compliance. Audit logging with user attribution. Encryption at rest. Multi-tenancy.
Integration as a tool in your AI stack
datathere exposes its capabilities through the Model Context Protocol. AI agents and LLM-powered workflows can create mappings, run transformations, and trigger pipeline jobs programmatically.
Integration becomes a capability inside your AI workflows — available wherever your agents operate.
Why datathere?
Data Integration Platform vs Embeddable Import Widget: Which Do You Need?
Flatfile and OneSchema solve inbound CSV import for your app users. datathere solves enterprise integration across systems and partners. Different buyer, different problem.
Read moredatathere vs Traditional ETL and iPaaS: Different Problems, Different Tools
iPaaS platforms connect SaaS apps through pre-built connectors. datathere maps messy partner data into clean structure. When you need both, here is how to think about it.
Read moreBuild vs Buy: Custom Development vs a Data Integration Platform
Most integration projects start with 'we will build it ourselves.' Here is what that decision actually costs in engineering time, maintenance, and opportunity.
Read moreData integration fundamentals
The Certification Workflow: Why Mappings Need Review Before Production
Sending uncertified mappings to production is like deploying code without review. Certification validates configuration, locks it for production, and creates an audit trail.
Read moreData Quality Enforcement: Three Ways to Handle Bad Records
When a record fails validation during a pipeline run, what happens next matters. Quarantine it, flag it, or stop the job. Each action serves a different purpose.
Read moreMulti-Source Joins: Combining Data from Multiple Sources in One Pipeline
Most integration tools handle one source at a time. Real-world pipelines need to join customer records from a CSV with transaction data from an API and reference tables from a database.
Read moreIndustries
AML Feed Consolidation: Normalizing Transaction Monitoring and Watchlist Data
Transaction monitoring alerts, sanctions screening results, and network-level intelligence from JPMorgan Access, SWIFT GPI, and FinCEN arrive in incompatible formats. Building a unified AML view requires mapping all of them.
Read more Retail & ConsumerSDS and Hazmat Document Extraction for Retail Compliance
Safety Data Sheets arrive as PDFs from hundreds of suppliers in different layouts. Extracting GHS classifications, storage requirements, and regulatory fields by hand does not scale.
Read more Supply Chain & LogisticsNetwork Design Data: Unifying ERP, WMS, and TMS for Cost Modeling
Modeling cost and service trade-offs requires order data from ERP, inventory from WMS, and transit data from TMS, three systems that never speak the same language.
Read more Manufacturing & IndustrialProcurement Data Consolidation for Vendor Comparison and Spend Analysis
Quotes, delivery terms, and performance data arrive from dozens of suppliers in different formats. Comparing vendors requires normalizing all of it into a common structure first.
Read more SaaS & TechnologyImplementation Starter Kits: Domain Mapping Templates for Customer Self-Service
New customers spend weeks waiting for integration setup. Published mapping templates and adapters let them self-serve initial connections and shorten time to value.
Read moreThe barrier to entry for production-grade data integration has never been lower.