As AI moves from isolated experiments into day-to-day finance work, treasury has an opportunity to take ownership of its adoption across the enterprise, prove value, build support, and turn pilots into approaches the wider business can use. That's the focus of the latest episode of Invention Mode, where Tanya Kohen, Head of Finance Practice, speaks with Bojan Belejkovski, treasurer, board member and AI enthusiast.
AI coding assistants can read individual files, but large enterprise codebases are harder to understand through text search alone because the important question is often how the code is connected. WA-V-Index adds that context through semantic search, a knowledge graph, and AST-based indexing.
Associations want to be a career-long partner to their members, but the technology underneath often works against that. A practical route to modernization: improve the systems already in place instead of adding another platform.
Modernizing an operational voice platform meant changing its legacy call-processing stack while preserving the business rules embedded in it. WaveAccess used AI-assisted analysis and mandatory engineering validation to reconstruct the platform’s actual behavior before replacing the voice core built on AWS Lex and Amazon Connect with Telnyx and OpenAI Realtime. This validate-first approach preserved critical logic, excluded obsolete paths, and reduced migration risk, while the new architecture simplified maintenance, removed per-firm bot builds, and strengthened security controls.
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 can you accelerate the preparation of a locally installed .NET application for migration to an intranet web system without losing business logic, access rules, or the familiar interface structure? WaveAccess used AI to extract rules from the source code, validate them with the client, and build a web proof of concept. In the pilot, the requirements handoff to development was reduced from several weeks to several hours.
How do you move a business-critical application when the documentation is outdated and key logic depends on scarce platform expertise?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.
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.
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