STX Next

Automate operational work, turn enterprise data into insights, and deploy secure AI and machine learning systems with control over code and infrastructure.

Unify and govern enterprise data to improve reporting, scale analytics, modernize platforms, and prepare reliable foundations for AI.

Improve cloud performance, manage costs, and migrate or modernize workloads with infrastructure aligned to business needs and operational requirements.

Turn product ideas into market-ready products through discovery, user-centered design, engineering, testing, deployment, and ongoing support.

Build secure, scalable Python solutions for web, data, AI, cloud, and IoT needs with experienced engineers and production-focused practices.

Build secure, scalable web applications that fit business processes, integrate with existing systems, and grow with changing needs.

Make complex data easier to interpret and act on with decision-focused dashboards designed for clarity, performance, and usability.

Automate and scale business workflows with enterprise n8n engineering, flexible deployment, migration support, and team enablement.

Build practical AI skills, automate real workflows, strengthen safe adoption, and leave with reusable assistants, agents, templates, and source files.

Automate process-heavy work with secure AI agents, validate business value quickly, and scale solutions across AWS environments.

Automate workflows and deliver secure, data-grounded AI solutions that can grow from a focused proof of concept into production applications.

Automate claims workflows to reduce intake time, errors, and re-inspections while retaining control of software and data.

Detect equipment issues early, schedule maintenance based on asset condition, and reduce unplanned downtime and unnecessary maintenance.

AI Development & Consulting Services

STX Next designs, validates, and deploys AI and machine learning solutions for business operations, enterprise data, and custom software. Services include AI agents, RAG systems, document intelligence, predictive machine learning, computer vision, search and recommendations, and custom AI/ML platforms. Work can start with fixed-price proofs of concept using client data, then progress to integration and production deployment on cloud, hybrid, or on-premises infrastructure. Machine learning services also cover predictive maintenance, forecasting, MLOps, and production model delivery. Clients retain ownership of delivered code.

Category: AI Development Services

• Automate Repetitive Workflows
  Agents carry out process-heavy tasks and interact with business systems, helping teams automate work beyond answering queries.
• Reduce Unplanned Downtime
  Use asset failure predictions to plan maintenance around likely failure windows rather than relying only on scheduled maintenance.
• Operate And Maintain Models In Production
  Lifecycle pipelines help teams monitor models, detect drift, manage versions, trigger retraining, and support ongoing production use.
• Find Answers In Enterprise Documents
  Makes internal documents usable for answering questions and retrieving information, with access controls and audit trails available for enterprise implementations.
• Forecast Outcomes And Guide Decisions
  Use models to anticipate business outcomes and support forward-looking decisions, with explainability and documentation to help teams understand model outputs.
• Validate Model Potential Before Full Investment
  Test whether historical data can support a useful prediction before committing to a larger implementation.
• Automate Visual Analysis And Improve Search
  Task-specific vision pipelines automate image analysis, while semantic search and behavior-informed ranking help users find relevant results.
• Connect AI To Existing Systems
  Integrate AI into established applications and infrastructure, with deployment options that support client data-control requirements.
• Build Custom AI Platforms
  A dedicated team develops AI/ML software for problems that do not fit a fixed-price offering.
• Retain Code Ownership And Operate AI
  Production engineering supports deployment and ongoing operation, while repository delivery gives clients control of their implementation.
• Extract Structured Data From Documents
  Turns scanned and digital documents into structured outputs while offering deployment options for organizations with data sovereignty requirements.

Data Engineering Services

STX Next provides end-to-end data engineering, including data platform architecture, lakehouse implementation, pipeline development, analytics engineering, and migration. Services cover ingestion, batch and real-time processing, transformation, quality, observability, governance, BI, and AI/ML data infrastructure. Lakehouse and platform-specific engagements can use Snowflake, Databricks, Microsoft Fabric, AWS-native architectures, or on-premises environments. Work can include architecture assessments, roadmaps, proofs of concept, system integration, DataOps, and iterative delivery.

• Build A Governed Data Foundation
  Establish a platform and architecture for consolidating data and supporting analytics, reporting, and AI workloads.
• Connect And Process Data From Multiple Sources
  Move data from fragmented systems into analytics workflows, with pipeline approaches suited to source and latency requirements.
• Standardize Data For Consistent Reporting
  Create reusable, validated models and metric definitions so teams can work from consistent data.
• Validate And Govern Data
  Build checks, lineage, and access controls into data systems to help teams identify issues, understand data origins, and support compliance requirements.
• Prepare Reliable Data For AI
  Provide structured data foundations and pipelines for machine learning, AI, and retrieval use cases.
• Modernize Legacy Data Platforms
  Move data infrastructure toward a unified platform through phased migration and modernization.
• Deliver Consistent Business Insights
  Shared metrics, semantic models, and reporting foundations help reduce conflicting reports and support more consistent analysis.
• Process Data As Events Occur
  Support use cases that require timely responses to events, sensor data, or transactions.
• Automate And Monitor Data Operations
  Standardized, monitored workflows help replace fragile ad-hoc processes and support consistent platform operation.
• Plan And Validate A Data Roadmap
  Assess existing capabilities, evaluate technology choices, and test proposed approaches before full implementation.

Cloud strategy and infrastructure services

STX Next provides architect-led cloud strategy, consulting, application development, migration, infrastructure design, cost optimization, and operational services across AWS, Azure, and Google Cloud, as well as hybrid, multi-cloud, and sovereign environments. AWS migration work covers assessment, dependency mapping, funding application support where eligible, migration planning, execution, validation, and modernization. Delivery can include cloud-native development, infrastructure as code, CI/CD, monitoring, FinOps governance, and ongoing support.

• Prioritize Cloud Improvements
  Get a workload-specific roadmap and architecture recommendations grounded in business needs.
• Migrate Workloads With Care
  Plan and execute workload transitions with validation and cutover practices designed to manage production risk.
• Move And Modernize Workloads On AWS
  Transition infrastructure, applications, and databases to AWS using workload-specific migration approaches and validation.
• Plan Costs And Assess Funding Options
  Understand likely migration costs and potential funding eligibility before committing to execution.
• Reduce And Govern Cloud Costs
  Identify cost drivers and implement infrastructure and governance changes to support sustained cost management.
• Build Cloud-Native Applications
  Use managed services and cloud-native patterns to support application delivery and operations.
• Automate Cloud Delivery
  Automated and reproducible workflows reduce manual deployment effort and support consistent environments.
• Match Deployment To Control Requirements
  Place workloads across cloud and on-premises environments according to technical, regulatory, and data sovereignty requirements.
• Migrate Regulated Workloads With Controls
  Move suitable workloads while retaining appropriate controls and managing production transition risks.
• Coordinate Cloud And Contact Center Operations
  Establish cloud governance practices or implement cloud-based contact center infrastructure suited to organizational needs.

Product Development Services

STX Next provides end-to-end product development, from consulting and ideation through design, development, quality assurance, cloud and DevOps, and ongoing maintenance and support. Multidisciplinary teams include designers, consultants, developers, QA engineers, product owners, and scrum masters. Design work can include user research, concept mapping, interactive prototypes, usability testing, design audits, interface design, and production-ready handoff. Quality assurance covers code, integrations, deployments, and end-to-end testing.

• Shape Products Around User Needs
  Research and concept mapping help teams make informed design decisions and clarify product direction.
• Test Concepts Before Building
  Make concepts tangible for testing and refinement, helping development teams start with less guesswork.
• Improve Product Usability And Clarity
  User-centered interface and information design helps make complex products and data easier to use.
• Connect Design With Development
  Implementation-oriented design assets and collaboration help align design work with development needs.
• Build Products With Modern Engineering Expertise
  Combine software, data, and machine learning expertise to develop products suited to business needs.
• Validate Product Direction Before Development
  Assess product direction and test concepts before committing to full-scale development.
• Verify Product Quality And Reliability
  Structured testing and review helps identify issues, verify integrations and deployments, and assess production readiness.
• Support Availability And Scalability
  Cloud and DevOps capabilities help products grow while supporting availability and security.
• Strengthen Design Consistency
  Expert review and design system support help teams improve products and maintain consistency.
• Maintain Products And Support Operations
  Ongoing maintenance and support help keep products operating.

Python Development Services

STX Next provides Python development services spanning web applications, APIs, custom software, data science and analytics, machine learning, cloud systems, IoT applications, and legacy migrations. The team builds secure, scalable solutions using production-focused practices and can add developers to a project in under two weeks.

Category: Custom Software Development Services

• Turn Complex Data Into Actionable Intelligence
  ETL pipelines, statistical modeling, and distributed computing support help customers analyze large datasets.
• Deploy And Maintain Production ML
  Production-oriented pipelines support model monitoring, versioning, testing, and retraining.
• Build Responsive Software For Business Needs
  Custom engineering and performance practices support business-specific applications and responsive web services.
• Add Experienced Python Engineers Quickly
  Access Python-focused developers who can join projects in under two weeks and work with architecture, testing, and security practices.
• Connect Systems With Secure APIs
  Documented endpoints and data schemas support communication across systems, with security and performance mechanisms.
• Scale Cloud Applications
  Container orchestration, serverless computing, and infrastructure as code support scalable cloud applications.
• Modernize Systems And Improve Resilience
  Migration and service architecture practices support maintainability, compatibility, resilience, and observability.
• Process Sensor Data With Lower Latency
  Custom processing and edge computing support sensor stream analysis and lower-latency operations.
• Validate Concepts With Scalable Prototypes
  Production-quality code and modular design provide a path from prototype to larger application.
• Explore Large Datasets Responsively
  Query optimization and visualization techniques support interactive exploration of large datasets.

Web App Development Services

STX Next designs and builds custom web applications tailored to clients’ processes and business goals, including SaaS platforms, eCommerce systems, and high-availability applications. Services cover legacy system modernization, API ecosystems, and AI and machine learning integration. The team emphasizes secure, scalable, maintainable solutions that fit existing technology environments.

• Build Applications Around Your Business
  Applications are designed around specific processes and goals, including the data models and business logic needed for complex operations.
• Scale SaaS Products Securely
  Tenant isolation and scalable architecture support a growing client base and feature set.
• Modernize Legacy Systems Gradually
  Phased migration helps maintain business continuity while improving maintainability, scalability, and security.
• Streamline High-Volume Commerce
  Custom capabilities support retail operations and connections to business systems.
• Add Intelligent App Features
  Enhance applications with capabilities such as recommendations and predictive analytics.
• Connect Applications To Existing Systems
  Secure APIs and integration design support reliable data exchange and fit within existing technology environments.
• Reach Users With Accessible Design
  Accessibility practices support applications designed for users with different abilities.
• Add Developers In Under Two Weeks
  Bring developers into a project quickly to begin work on web applications.

Data Visualization & Dashboard Design Services

STX Next combines data experience design with data and cloud engineering to design and build executive dashboards, operational control rooms, and data-intensive interfaces. Services cover decision workflow audits, interface design, supporting data models and pipelines, performance optimization, and deployment within existing BI and cloud environments. Work can include new dashboards, control rooms, and redesigns of existing BI environments, with governance and single sign-on integrated as needed.

• Turn Data Into Actionable Views
  Bring relevant information into decision-ready views for executive and operational use.
• Clarify Decisions Before Development
  Define the information and relationships an interface needs to represent, shaping dashboard layouts around business decisions.
• Improve Existing BI Experiences
  Refine dashboards and supporting components while continuing to use the current BI environment.
• Surface Key Information And Exceptions
  Structured views help users interpret operational data, distinguish deviations from routine readings, and identify important changes.
• Work Within Your Existing Stack
  Implement or redesign dashboards in the current environment rather than introducing a separate platform.
• Support Responsive Dashboard Performance
  Engineering the supporting data and infrastructure around interface needs helps address performance issues that affect usability.
• Validate And Refine Interfaces
  Test layouts and interaction flows before implementation and refine dashboards as requirements evolve.
• Connect Metrics To Team Decisions
  Decision-focused views organize relevant KPIs around team priorities and support shared interpretation.

Enterprise n8n Implementation Services

STX Next provides end-to-end engineering for enterprise n8n automation as an official n8n partner. Services include process audits, AI orchestration, custom workflow and node development, migration from Zapier, Make, or Power Automate, deployment on n8n Cloud or self-hosted infrastructure, and training. Production workflows can include error handling, logging, and monitoring. Self-hosted deployments can run in a client’s VPC or on-premises, with optional infrastructure hardening and monitoring.

• Automate LLM-Enabled Business Processes
  Workflows can use LLMs and agents for tasks such as preparing proposals, generating project briefs, or converting emails into structured tasks.
• Build Reliable Connected Workflows
  Production controls support reliable automation, while custom nodes connect workflows to systems without standard APIs.
• Migrate Automations With Validated Cutovers
  Validate outputs before transition and use phased cutovers to manage migration.
• Choose Cloud Or Self-Hosted Deployment
  Select a managed cloud option or host n8n in a VPC or on-premises to address deployment and data residency needs.
• Enable Teams To Own Automation
  Training and documented handoff help teams operate, maintain, and evolve their automation stack.
• Identify Automation Opportunities
  Process audits help teams identify workflows suited to n8n development or migration.

AI Excellence Program

The AI Excellence Program provides hands-on AI and automation training through a 20-hour technical bootcamp for developers and a 16-hour business enablement masterclass. Participants use real tools and company processes to build assistants, agents, automations, and AI-supported software workflows. The program is tailored to the organization’s technology stack and industry context, with security practices and knowledge transfer. Teams keep templates and source files for the solutions they create.

Category: AI Training Services

• Practice On Real Company Workflows
  Apply concepts directly to relevant organizational tools and processes.
• Apply AI Tools In Software Delivery
  Practice using AI developer tools across planning, implementation, testing, debugging, and review.
• Improve Team Work With AI
  Learn to use AI tools, communicate requirements to IT, evaluate proposed solutions, and improve team processes.
• Keep Templates And Source Files
  Leave with reusable workshop materials that teams can adapt after the program.
• Build Assistants And Multi-Step Agents
  Create task-specific assistants and agents grounded in organizational work and safeguards.
• Automate Repetitive Team Work
  Build practical automations around organizational workflows and manual tasks.
• Adopt AI With Security Safeguards
  Learn security practices suited to the organization, with options for isolated environments, approved models, and flexible delivery.

Amazon Bedrock and AgentCore Development Services

STX Next designs and deploys generative AI applications and autonomous agents using Amazon Bedrock and Bedrock AgentCore. Services include proofs of concept, custom agents for complex workflows, secure connections to enterprise data and tools, and production scaling with CI/CD, logging, monitoring, security reviews, and performance optimization.

Category: AI Development Services

• Automate Repetitive Business Work
  Agents handle recurring processes, interact with systems, and execute multi-step workflows.
• Validate Agent Value In 6–8 Weeks
  A functional agent connected to data helps demonstrate business impact before extension into a scalable MVP.
• Deploy Custom Long-Running Agents
  Use open-source agent frameworks for complex workflows, including tasks that run for up to eight hours.
• Connect Agents Through A Secure Gateway
  Connect agents to business systems and tools with access governed through IAM roles.
• Remember Preferences Across Sessions
  Recall user preferences and previous interactions to support personalized experiences.
• Embed And Scale AI In Existing Workflows
  Integrate agents into existing work environments and move validated solutions toward production with operational controls.

Copilot Studio and Microsoft Foundry Development Services

STX Next builds secure, production-ready AI agents and applications with Copilot Studio and Microsoft Foundry. Services cover functional proofs of concept connected to client data, MVP development, complex orchestration, model fine-tuning, evaluation, and traditional machine learning. Solutions can use company data and integrate with Microsoft tools and other business systems.

Category: AI Development Services

• Automate Repetitive Workflows
  Agents execute operational tasks using consistent logic to help reduce bottlenecks.
• Validate And Scale AI Solutions
  Test a data-connected agent against a real use case, then extend it with cross-system actions and managed updates.
• Ground Answers In Protected Company Data
  Retrieval and access controls help teams find relevant approved information while maintaining organizational security boundaries.
• Connect AI To Existing Workflows
  Place AI in applications teams already use and connect it to business workflows.
• Extend AI Model Capabilities
  Support tailored models, complex orchestration, and evaluation as solutions grow.
• Build And Scale Securely
  Incremental development supports early testing and ongoing delivery, with attention to quality, security, and deployment.
• Combine Prediction With Generative AI
  Add predictive and classification capabilities alongside generative agents.

AI Claims Automation Accelerators

STX Next’s custom-built, pattern-based accelerators automate insurance claims intake, verification, field operations, and fraud detection. Configured for each operation’s carriers, workflows, and systems, they integrate with existing claims platforms without replacing core systems. Deployment is designed to take 8–16 weeks, with customers retaining full source code ownership. The solution has no platform subscription or per-claim pricing and can run on-premises, in a hybrid environment, or in the cloud, with data kept in the customer’s infrastructure.

• Route Claims Without Manual Triage
  Automated extraction, classification, missing-document checks, ticket creation, and routing reduce manual handling.
• Flag Estimate Discrepancies Automatically
  Automated comparisons help identify estimate violations and outliers for review.
• Validate Field Evidence On Site
  On-site checks and structured documentation help address incomplete evidence and reduce re-inspections.
• Automate Claims Within Existing Systems
  Integration addresses operational bottlenecks while supporting customer control over where claims data is processed and stored.
• Own The Code And Control Data
  Source code ownership, deployment choices, and pricing without per-claim fees support customer control over implementation and data.
• Escalate SLA Risks Before Violations
  Tracking, countdowns, and alerts help teams identify and act on potential SLA violations.
• Surface Cross-Claim Patterns
  Pattern analysis helps fraud teams identify relationships and signals for further investigation.
• Validate Automation On Claims Data
  Evaluate extraction results and workflow fit using customer documents before proceeding with a full build.

Predictive Maintenance Solutions

STX Next builds predictive maintenance systems that use real-time and historical sensor and process data to detect equipment anomalies, forecast failures, and estimate remaining useful life. Solutions support condition-based maintenance by helping teams identify when assets need attention instead of relying on fixed schedules. Delivery can include data engineering, feature engineering, model development and validation, and production deployment integrated with industrial data and maintenance systems. Solutions are designed for high-volume IoT and time-series data and can use explainable, physics-informed models.

• Detect Equipment Problems Earlier
  Early detection helps teams respond to emerging issues before they escalate into failures or unplanned shutdowns.
• Schedule Service Around Asset Condition
  Plan maintenance around predicted equipment condition to help avoid unnecessary part replacement and emergency repairs.
• Forecast Asset Maintenance Needs
  Predictions give maintenance teams a forward-looking view of asset condition to help plan service.
• Analyze Industrial Telemetry At Scale
  Analyze operational signals from real-time and historical sources, including data-intensive industrial environments.
• Improve Predictions With Equipment Context
  Operational context supports models tailored to the behavior of specific industrial equipment.
• Understand Prediction Drivers
  Clearer explanations help engineers review model factors when making maintenance decisions.
• Connect Predictions To Existing Systems
  Using existing data and maintenance systems helps teams apply predictive insights in established workflows.
• Deploy Models Into Operations
  End-to-end delivery takes predictive maintenance models beyond development and into production.
• Optimize Maintenance And Asset Life
  Timely maintenance can reduce avoidable service work and help prevent minor issues from developing into damage.
• Address Equipment Risks Earlier
  Identify potential failures earlier to help teams plan maintenance before hazardous conditions arise.