top of page
Frequently Asked Questions
Consulting
Backup and Recovery
Security
General
Netsuite
We support a wide range of export formats to fit your business and analytics needs. Data can be delivered as flat files (CSV, TSV, or custom-delimited), relational database tables, cloud object storage (S3, Azure Blob, Google Cloud Storage), or directly into data warehouse destinations like Snowflake, BigQuery, or Amazon Redshift. Custom formatting and schema mapping are also available.
Yes. We ensure that your exported data is compatible with leading reporting and analytics platforms. Data can be structured and formatted to import seamlessly into tools such as Tableau, Power BI, Snowflake, or Looker. Flat files can be customized with your preferred delimiters, headers, and encoding for smooth integration with your existing workflows.
Our migration process is designed for speed, transparency, and control:
Connect & Configure: Use our intuitive interface to connect your endpoints, define mappings, and set export parameters.
Extract & Structure: We securely extract your data—preserving metadata, relationships, and history—and organize it into your chosen format or destination.
Automate or Export: Run a one-time migration or schedule recurring syncs. Every export is secure, auditable, and fully configurable for your ongoing data needs.
Check out our videos
How to Build AI-Ready Datasets With Enterprise Data Preparation
Enterprise AI projects fail at the data layer — not because of the model, not because of the algorithm, but because the pipelines feeding the model were never governed. Duplicate records, missing fields, and undocumented transformations flow downstream and corrupt training data before a single prediction runs. This guide covers the six steps enterprise IT teams use to build AI-ready datasets with quality controls, governance, and pipeline architecture that holds up under regu
Aug 119 min read
Data Pipelines: Take Control of Your Enterprise Data
Data doesn't sit still. It moves between CRMs, ERPs, cloud platforms, and analytics tools — constantly, at scale, and often without adequate oversight. For modern enterprises, the ability to control that movement isn't a technical nicety. It's a business imperative.
May 256 min read
How to Prepare AI Data Without Leaving Your Stack
Preparing enterprise data for AI inside your own stack means running every pipeline stage on infrastructure you control. This guide provides the step-by-step framework for IT teams with compliance and sovereignty requirements.
May 810 min read
Enterprise Data Labeling for AI in 2026
Enterprise data labeling is where most enterprise AI initiatives succeed or fail in 2026. This guide covers labeling strategy, quality controls, governance, and the pipeline infrastructure that connects labeled data to model training.
Apr 2310 min read
How to Audit AI Data Readiness Before You Deploy
A seven-step framework for auditing AI data readiness: inventory, quality scoring, governance mapping, integration testing, and remediation.
Mar 226 min read
Realistic Objectives for AI Projects: Why AI Readiness Depends on Understanding Your Business
AI initiatives don’t fail because of bad models. They fail because objectives, data, and business processes aren’t clearly understood first. In this thought-leadership piece, Sesame Software CEO Rick Banister explains why AI should be treated as a tool, not a goal, and how examining data quality and workflows during the discovery phase often delivers more value than deploying AI itself.
Jan 25 min read
Building AI Readiness: How Leading Enterprises Prioritize the Right Initiatives with the Right Tools
AI is now part of every conversation. CIOs are being asked where AI fits, how fast it can be deployed, and what needs to happen first. But as with any new technology wave, not every problem is an AI problem.
Dec 9, 20253 min read
Enterprise Data Preparation for AI: A 2026 Pipeline Guide
AI models are only as good as the data pipelines feeding them. This guide shows enterprise IT teams how to build AI-ready data pipelines with the quality, governance, and infrastructure control that machine learning requires.
Sep 30, 202512 min read
AI for Bing and Edge: Your Copilot for the Web
Microsoft Unleashes AI-Powered Search Microsoft has transformed its Bing search engine and Edge browser by introducing an AI-powered experience featuring ChatGPT capabilities. The aim is to provide users with better search results, more complete answers, a new chat experience, and the ability to generate content.
Jul 17, 20233 min read
bottom of page