Intertech

Intertech helps organizations modernize outdated software systems, build technology capabilities, integrate AI, and establish engineering practices for long-term innovation, resilience, and growth. Its consulting practice includes .NET, Java, front-end, and back-end software development leadership and co-development.

Intertech describes its work as end-to-end application delivery, covering architecture and feasibility studies, design, development, deployment, and ongoing support. Its clients include Fortune 500 companies, small businesses, medium-sized companies, government organizations, educational institutions, and healthcare organizations.

Customer feedback

• 99.55% of surveyed customers said they would use Intertech again.
• 99.70% of surveyed customers said they would refer Intertech to others.

Contact

• Phone: +1 651-288-7000
• Email: info@intertech.com
• Address: 860 Blue Gentian Road, Suite 200, Eagan, MN 55121

• Software planning and requirements analysis: Establishes the foundation for the software development process.
• Architecture and design: Creates a roadmap for successful software implementation and scalable, maintainable systems.
• Custom development: Selects languages, establishes coding standards, and develops or updates web-based and desktop applications.
• Web application and feature development: Builds secure, user-friendly web applications designed to scale and integrate with existing systems.
• Mobile application development: Develops responsive applications for iOS and Android.
• Quality assurance and testing: Uses an integrated QA and testing approach to support software quality and reliability.
• Cloud consulting, migration, and integration: Helps organizations migrate to and operate in AWS, Azure, and hybrid environments, including cloud infrastructure, data, application architecture, and development.
• System integration: Connects platforms and automates workflows across business systems.
• DevOps, Agile, and CI/CD: Designs delivery practices and CI/CD pipelines to support fast, reliable software updates.
• AI and machine learning: Implements AI-driven solutions for automation, predictive insights, personalized experiences, and intelligent automation.

Intertech also helps organizations modernize legacy .NET, Java, and open-source systems, build new applications, automate workflows, and integrate AI and low-code solutions.

Its AI transformation approach includes:

1. AI readiness and opportunity assessment — Identifies business opportunities and evaluates data, architecture, governance, and risk readiness.
2. Team alignment and AI adoption — Provides AI development training, workflow transformation, governance best practices, and adoption guidance.
3. Modernization of legacy systems for AI — Modernizes applications, platforms, and infrastructure, including APIs, cloud readiness, data pipelines, scalability, and observability.
4. Disciplined AI development — Addresses AI development governance, secure integration, testing, reliability, prompt management, and system management.
5. AI operationalization and scaling — Helps move AI systems from experimentation into reliable, observable, cost-conscious production environments, with monitoring, optimization, lifecycle management, reliability, and scalability.

Intertech also helps organizations identify appropriate AI opportunities, introduce AI capabilities strategically, prepare legacy systems and data for AI, turn pilots into production systems, add AI expertise without replacing existing teams, secure AI systems, and reduce AI strategy risk.

AI delivery concerns addressed

Intertech provides guidance for issues including rising AI costs, lack of trust and observability, AI technical debt, application latency, production scaling, and incorrect answers in production.

The framework treats AI as an organizational capability supported by strategy, governance, engineering discipline, operational controls, risk management, and workforce adoption.

Framework components

1. AI Delivery Maturity Model: Assesses organizational readiness, capability gaps, and improvement priorities.
2. AI Strategy Framework: Aligns AI investments with business objectives, priorities, and measurable outcomes.
3. Enterprise AI Operating Model: Defines ownership, responsibilities, decision-making, and collaboration between business and technology teams.
4. AI Center of Excellence Framework: Provides standards, reusable assets, training, and expertise to support consistent adoption.
5. AI Governance Framework: Establishes policies, decision rights, approval processes, accountability, and compliance mechanisms.
6. AI Risk Management Framework: Identifies, assesses, mitigates, and monitors operational, regulatory, security, and reputational risks.
7. Responsible AI Framework: Addresses fairness, transparency, explainability, accountability, values, and human oversight.
8. AI Controls Framework: Defines preventive, detective, corrective, security, compliance, and operational controls.
9. AI SDLC Framework: Extends software development practices to cover model selection, prompts, evaluation, retrieval, agents, testing, deployment, and monitoring.
10. AI Production Readiness Framework: Defines validation criteria and deployment gates for secure, reliable, supportable, compliant AI systems.
11. AI Reliability Framework: Focuses on accuracy, consistency, resilience, failure recovery, testing, validation, and operational dependability.
12. AI Trust and Observability Framework: Covers monitoring, tracing, auditability, explainability, performance measurement, and drift detection.
13. AI Cost Management Framework: Addresses token consumption, model utilization, agent spending, infrastructure costs, and operational efficiency.
14. AI Technical Debt Framework: Helps manage model sprawl, prompt sprawl, agent proliferation, governance drift, duplicated capabilities, and architectural complexity.
15. AI Adoption Framework: Focuses on change management, training, communication, workforce enablement, leadership engagement, and user adoption.

Together, these disciplines provide an integrated methodology for building, governing, deploying, monitoring, scaling, and continuously improving enterprise AI systems.