Profound Logic Software

Western Power Sports did not separate the modern front-end from IBM i. They embedded Vue.js directly into the Profound AppDev web applications, with assets deployed through IBM i / IFS. The result?

The team updates and refines the user experience without separating the application from the system of record. IBM i performance and security controls stay in place. The customer-facing side looks and behaves like modern software.

80% of the platform runs on IBM i this way. $3 million a day in e-commerce revenue runs through this architecture, handled by a team of 3.5 people.

Running Vue.js on IBM i was not a workaround. It was a deliberate architectural decision that made the whole operation more stable.

Read the full case study: https://hubs.la/Q04x82Ps0

Hundreds of store locations. Farming, processing, distribution, and retail all under one roof. IBM i systems running operations the business could not afford to interrupt. Profound AppDev's coexistence model made that possible.

Legacy and futurized applications ran in parallel throughout the rollout. Users accessed both through consistent browser-based interfaces. No location was cut over while another was still on the legacy system.

For an operation this size, coexistence wasn't a preference. A hard cutover across hundreds of locations at once was not an option the business could accept. Incremental rollout with full operational continuity was the requirement, and the architecture delivered it.

Read the full case study: https://hubs.la/Q04x86D80

IBM i transformation projects stall for a lot of reasons. One of the most common: the assumption that everything has to be figured out before anything can begin. Agentic Futurization is structured around multiple entry points because organizations start from different places with different constraints.

If the priority is understanding the environment before committing to a path, our Discovery maps dependencies, surfaces hidden risk, and produces a realistic effort estimate. Right for organizations that need clarity before budget.

If green screen futurization is the most urgent need, that's the starting point. CoderFlow agents write screens in HTML, CSS, and JavaScript while the RPG stays in place.

If getting off RPG or COBOL entirely is the goal, the Convert path handles two-phase algorithmic plus AI conversion with Git migration included.

If the team wants to start moving faster with AI before a full transformation program, the Agentic Coding Starter Engagement has CoderFlow live and the team running autonomously in ten weeks.

Different starting points. The platform stays with you wherever you begin and however far you go. See the full Agentic Futurization journey: https://hubs.la/Q04x83BT0

We've noticed something in almost every team running agentic coding at scale. Nobody wants to be the one who picked the wrong model on something that mattered. So everything gets routed to the top-tier model. The one-line config fix. The routine refactor. The simple test. All of it.

CoderFlow's Auto model selection addresses this directly. Set the model to Auto and CoderFlow sends the task to a lightweight model that rates its complexity, then routes it to the right tier: a fast, cheap model for routine fixes, a mid-tier for well-defined features and refactors, the heavyweight for multi-repo work, architecture changes, or the murky bug nobody can quite diagnose.

The routing table is visible and documented. If a task lands somewhere you wouldn't have chosen, you can see the reasoning and override it manually on any individual task. See how it works: https://hubs.la/Q04wJzDp0

Most staff augmentation solves the immediate problem and leaves everything else the same. Someone comes in, fixes what's broken, and leaves. The team is back where it started, minus the problem, plus no new capability to handle the next one.

Strategic augmentation works differently. Experts who work alongside your team, not in place of them. Who document what they're doing and why. Who free your senior people to focus on the institutional knowledge only they carry, while the augmented resources handle the ex*****on work that's been consuming everyone.

The outcome isn't just a problem solved. It's a team that can handle what comes next without bringing someone back in. Schedule a conversation: https://hubs.la/Q04x814m0

Futurization has four dimensions, and most organizations are further along in some than others. Knowing which dimension is most constrained tells you where to focus first.

🔹 AI readiness and agentic integration. Can your business logic be reached by AI tools? Are agents able to operate inside your environment?
🔹 Modern API architecture. Is your IBM i data and logic accessible to the systems around it, or does every new integration become a custom project?
🔹 Intelligent application transformation. Do you have a clear picture of what to refactor, replace, retain, or retire, and on what timeline?
🔹 Platform and workforce evolution. Are your interfaces modern enough to attract developers outside the IBM i talent pool? Is your database structured in a way that supports what the business actually needs from it?

Most organizations we talk to are somewhere along all four, at different stages. Where is the gap most constraining for your team right now?

Ready to make moves towards futurization? Let's connect.

IBM i teams keep telling us the same thing...they're feeling pressure to pick an AI platform before anyone on the team really knows how to evaluate one. So, we decided to build a training course to fix exactly that!

Our AI Foundations is three 90-minute sessions over two weeks. No prior AI experience needed. No sales pitch at the end.

1️⃣ The first session covers the vocabulary RPG and IBM i developers actually need: what prompts, context windows, grounding, and hallucination mean in practice, and how to read AI documentation without taking it at face value.
2️⃣ The second session is hands-on prompt engineering using real RPGLE, SQL, and Node.js examples. How to explain legacy code to an AI, refactor RPG, generate unit tests, and write follow-up prompts that catch errors before they ship.
3️⃣ The third session covers context engineering: why well-structured RPG makes AI more accurate, and how RAG, agents, and skills fit together in actual IBM i development work.

After three sessions, your team can evaluate any AI tool on its merits, including ours, before committing to anything. Schedule a kickoff call: https://hubs.la/Q04wJDwB0

Task Force IT-Solutions, a member of the Profound Logic Futurization Alliance, had the customer relationships and understood the IBM i environment. What they needed was tooling that could connect a high-tech automated sorting system to an IBM Power i platform in real time, without touching operations already running reliably.

Profound API provided that layer. REST interfaces between the sorting system and IBM i. RPG and Node.js integration. Real-time data exchange with no disruption to the core systems.

The result for Witra Logistics and LERROS Moden: daily processing capacity doubled from 6,000 to 12,000 SKUs. Delivery volume up 30%. Delivery times to point of sale cut significantly.

The capability gap in IBM i work is rarely about intent. It's about what the partner can actually bring.
Read the full case study:https://hubs.la/Q04x805Z0