Unlock exclusive Amazon funding for your AWS migration with a certified partner
Subsidize Your Migration Costs
STX Next is a certified AWS Migration Acceleration Program (MAP) partner, continuously maintaining and renewing our qualification to secure funding for partner-originated deals.
Working with us grants access to significant AWS funding that offsets our engineering costs. For deals meeting the $100k USD ARR (Annual Recurring Revenue) threshold, this directly reduces your upfront capital investment.
Our Vetted, 4-Step Migration Methodology
Your migration follows a strict framework. Amazon's Partner Solution Architects audit it to ensure zero operational downtime.
Analyze
By auditing your spend, VM performance, and hidden dependencies, we equip you with a solid business case and an accurate TCO model.
Prepare
You get a clear technical roadmap, target performance metrics, and an optimized architecture outline tailored to your goals.
Engage
We align directly with AWS technical leadership to validate the new design and lock in your maximum project subsidies.
Execute
Your team gains a modernized, high-availability AWS environment as we handle the complete deployment, validation, and launch.
Claim Your Funding Before the Deadline
AWS MAP capital is highly competitive and tied to strict funding windows. Let’s audit your infrastructure, project your target AWS revenue, and claim your engineering subsidies before the timeline closes.
Ready to transform your Oil & Gas enterprise?
Let’s discuss your current data infrastructure, site historians, and high-impact automation targets. Speak directly with an STX Next industrial technology specialist.
FAQs
We rely heavily on legacy historians and on-premise SCADA. Do we need a complete cloud migration to start using AI?
No. While we specialize in cloud modernization (AWS, Azure, Snowflake, and Databricks), we explicitly support on-premise and edge deployments that bypass the cloud entirely. We build integration layers over your existing Purdue Model architectures and site historians (like OSI Pi) so you can deploy industrial intelligence without disrupting stable control-room networks.
How do you protect against AI "hallucinations" in high-risk plant environments?
We don't deploy unmonitored "black-box" systems on the plant floor. Our Process Advisor and Root Cause Agents operate strictly within your pre-defined safe envelopes and utilize Explainable AI (via SHAP values). Every single recommendation is logged with a visible, clear evidence trail detailing why an action was proposed, keeping your human operators firmly in control.
Our data is siloed across multiple facilities, and a centralized data team is already a bottleneck. How do you handle this?
We tackle this structural barrier by introducing a Data Mesh architecture. Instead of forcing a single central team to manage a massive data dump, we pivot to domain-aligned data products. Each operational unit owns the lifecycle, documentation, schemas, and quality SLAs of its own data. This eliminates pipeline queues, ensures reliable data ingestion, and makes your telemetry instantly usable for site engineers.
We handle highly sensitive infrastructure, compliance, and asset data. How do you guarantee data sovereignty?
We guarantee 100% data sovereignty. Our solution blueprints are built to run within your own enterprise environments. Whether implementing secure, isolated Azure-hosted RAG networks to parse policy PDFs or deploying enterprise self-hosted automation platforms (such as self-hosted n8n), your proprietary operational data never trains external vendor models.
How do you ensure our field crews and plant operators will actually adopt these tools?
We don't drop technology off at the door; we build organizational adoption directly into our deployment roadmap. In parallel with software integration, we run two dedicated enablement tracks: a 20-hour Technical Bootcamp to teach your engineers quality-first development with LLMs, and a 16-hour Business Track to show operations and finance teams how to build localized, low-code automations independently.
