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EXIQO Insights

Real stories. Sharp perspectives. Built for leaders who think ahead.

Featured Posts

Research Paper

Agentic AI 2026 | A Mid-Market Playbook for Adoption and Scale

From pilot ambition to enterprise-scale execution. ~200 mid-market leaders surveyed.

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Press Release

R Systems Unveils EXIQO to Enable Enterprise-Scale Agentic AI Integration and Accelerate Engineering Velocity

EXIQO, an AI Studio by R Systems, combines enterprise context, embedded guardrails, and human oversight to deliver governed, enterprise-grade execution at scale

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  • Case study

40% Faster Onboarding. 50%+ Less Support Overhead.

A legacy platform had millions of lines of code and almost no documentation. Agentic AI reverse-engineered the system in hours instead of weeks — and turned recurring support tickets into auto-generated pull requests.
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  • Point of view

AI Features Will Not Scale on Product Data You Cannot Trust

Your model isn’t the problem. The product data underneath it is and no catalogue or Lakehouse fixes what was never agreed on in the first place.
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  • Case study

75% Less Migration Effort. 97% Migration Completeness. 10 Days → 2.5 Days.

A global e-commerce platform was trapped inside a tangled PHP monolith. AI-driven semantic decomposition and code generation turned a months-long manual rewrite into a repeatable, automated migration pipeline.
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  • Case study

3-Month Backlog. 4-Day Resolution. ~120 Engineering Hours Saved Per Week.

Broken importers were piling up faster than engineers could fix them. RSI built a self-healing agentic workflow that diagnoses failures, patches code, verifies the fix in Docker, and opens the pull request — automatically.
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  • Case study

~70% Faster Deployments. 3–5x Throughput. 99.9% Uptime.

A legacy Java platform on end-of-life JDK 1.8 was holding the team back. RSI modernized the full stack — runtime, framework, infrastructure, and deployment — without replacing the application itself.
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  • Case study

6M+ Lines of Code. 8 Months. Zero Downtime.

A legacy platform serving 7,000+ daily users needed a full modernization without anyone noticing. Multi-agent AI engineering made it possible, with zero downtime and not a single stored procedure rewritten.
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  • Case study

80+ Plants. AI-Powered. <60 Seconds to Insight.

A global manufacturer had plenty of data and zero fast answers. An AI-powered Factory CoPilot changed that, $3.3M in measurable impact, and every employee asking questions in plain language.
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  • Point of view

LLM & Agent Observability and Evaluations

Building an impressive demo is easy. Knowing whether it still works next week, at scale, across real workflows, that’s the part most teams skip.
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  • Point of view

When Fine-Tuning Earns Its Cost: A decision framework for owning a model vs. renting frontier intelligence

Fine-tuning is sold as the grown-up move. The data says most teams shouldn’t, unless four conditions hold at once.
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  • Point of view

The Agent Governance Operating Model

Knowing what to control is the easy part. This is about who owns it, on what cadence, and what happens when something breaks.
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  • Point of view

Governing Agents That Never Pause

Every control you trust needs a pause to work. Agents don’t pause. Five gates close the gap between what’s authorized and what’s actually safe.
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  • Point of view

Cheaper Tokens, Bigger Bills: Why enterprise AI cost is a routing-architecture problem, not a procurement one

Token prices keep dropping. Your AI bill keeps climbing. The fix isn’t a better contract, its deciding which model handles which task.
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From the team

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