A major pharmaceutical company needed a faster way to search, review, and analyze a growing volume of scientific publications. An AI-based platform brought multi-source search, document processing, and a source-grounded RAG assistant into one workflow, helping specialists reach structured, verifiable insights faster. The same model may also be relevant to other document-intensive industries.
How do you move a business-critical application when the documentation is outdated and key logic depends on scarce platform expertise? WaveAccess used AI-assisted discovery to recover the application's business logic, data model, and user workflows, and then applied engineering validation to create a reliable foundation for migration to a modern platform.
WaveAccess automated two recurring sales and presales workflows using n8n, AI, and Microsoft Teams: preparing meeting requests for approval and prompting owners to update presales records. The automations reduced manual clarification and follow-up, improved handoff quality and data accuracy, and saved an estimated 8+ hours per week.
Most legacy modernization goes sideways early, before anyone writes new code: in discovery, where teams commit to a path without a reliable picture of how the current system works. You lock in an architecture, start building, and hit behavior the docs never captured. WaveAccess uses AI-assisted legacy discovery to reconstruct how a system actually runs, from the implementation itself, in days instead of weeks.
Let's explore how treasury teams can lead the design of a programmable liquidity layer that consolidates hundreds of bank accounts without disrupting existing banking relationships. We'll look at what it takes to connect blockchain infrastructure to your ERP and TMS, the red flags that stall most pilots, the performance standards for enterprise-level volumes, and the prerequisites to keep the CFO in control.
Multi-agent systems often show managerial problems: agents fail to share information, follow roles mechanically, or drift into unproductive chatting. Today let’s see why good engineering is more important than improvement of prompts.
By shifting from isolated AI pilots to a centralized enterprise framework, businesses can finally automate complex workflows and accelerate decision-making without compromising on governance or LLM independence. Here’s how we solve these scaling challenges with our enterprise-ready solution.
When every department builds its own AI agent with its own data, logic, and tools, organizations can find themselves with a "zoo" of disconnected systems. Instead of scaling, these silos cause the company to slow down. Paul Chayka, Integration and AI Solutions Expert, breaks down how to innovate responsibly by selecting the right initial use cases, and shifting from simple task automation to a coordinated multi-agent ecosystem.
For a Forbes Global 2000 client, we automated the process of matching adverse event descriptions from clinical reports with standardized MedDRA vocabulary — achieving over 95% automation, including terms that specialists previously failed to map manually. Faster and more accurate processing of clinical data reduces R&D costs, accelerates regulatory submissions, and ultimately supports faster delivery of new treatments.
The 2025 Iberian blackout showed how fragile our connected world is. When the power fails, the communication networks we rely on can go silent in a blink of an eye. There’s an ongoing discussion about resilient networks for such cases. LoRa Meshtastic seems like a strong fit for these scenarios.
Vibe coding — the practice of writing code through natural language interactions with AI — has become a hot topic across the corporate tech world. But in practice, it’s meeting a wall of cultural caution, productivity paradoxes, and real-world quality challenges. Here is our look at the current state of adoption, risks, and the emerging best practices for companies bringing AI-assisted coding into their development pipelines.
When people think of AgriTech, they often imagine drones, robotics, or biotech — but another critical frontier is preserving what’s already harvested. Despite digital advances, 10–15% of grain is still lost post-harvest. Using IoT monitoring systems offers a compelling path to reducing these losses — and that’s exactly what CropSave is built to do.
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