Our team brings AI, machine learning, and data engineering into enterprise planning — from intelligent forecasting and variance agents to the data architecture that makes it all reliable. Built with governance, not just demos.
“The gap between an AI demo and an AI system finance can rely on is engineering discipline — evals, governance, human oversight, and audit trails. That discipline, applied to enterprise planning, is what we bring.”
Variance diagnosis agents, agentic close and forecast workflows, and native platform AI — all built with human-in-the-loop approval, evals, and full audit trails. We start with an AI readiness assessment to find where AI genuinely fits before building anything custom.
The data layer that makes planning and AI reliable — plan/actual/forecast modelling, hierarchy governance, and dimensional design, built on Azure Data Factory, Microsoft Fabric, dbt, SQL, and Python with reconciled, governed pipelines.
Production-grade AI engineering for finance — LLM APIs, LangGraph orchestration, MCP servers connecting agents to planning data, RAG, and eval frameworks. Governance, oversight, and audit built in from the start, not bolted on.
Artificial intelligence and machine learning are reshaping enterprise performance management — but the gap between a compelling AI demo and a system finance teams can trust in production is enormous. Our work focuses on closing that gap: bringing AI, ML, and data engineering into planning with the governance and discipline that finance requires.
We start with honesty about where AI fits. Many finance teams are sold custom builds they do not need — when the right first step is operationalising the AI features already in their EPM platform, such as Workday Illuminate, Adaptive Predictive Forecaster, or OneStream Sensible ML. We assess what your data and processes genuinely support before recommending any build.
Where custom AI is justified, we build it properly: variance diagnosis agents that rank drivers and draft commentary, agentic workflows for close and forecast review, and the AI engineering behind them — LLM integration, LangGraph orchestration, MCP servers, RAG, and eval frameworks. Every build includes human-in-the-loop approval, audit logging, and cost controls.
Underneath all of it sits data. Reliable planning and trustworthy AI both depend on a well-designed finance data layer — plan, actual, and forecast modelling, hierarchy and reorganisation governance, and dimensional design. Our team engineers that layer using modern data tooling, so everything built on top of it stays robust as the organisation scales.
This combined capability — across Workday Adaptive Planning, OneStream, Oracle EPM, and Anaplan — is what lets us design intelligent planning systems rather than just implement tools. AI, ML, and data are not separate offerings bolted onto planning; they are integral to the architecture from the start.
Exploring AI for your finance function?
Talk to our team →Our architects will assess where AI genuinely fits your planning and close processes — and give you an honest, governance-aware roadmap.