In practice · AI

Great AI needs great data.

Firms are building AI workflows on Copilot, ChatGPT, Claude and internal LLMs. Agents are moving from suggesting to acting. The model was never the bottleneck. The financial data feeding it is.

Validis extracts and standardizes accounting data straight from your clients ERP into any LLM your team chooses.

WORKFLOW

Built into a workflow, not just a prompt.

An LLM on its own is unpredictable. Run it inside a workflow, with Validis doing the math underneath, and three things change.

Consistency

"Will I get the same answer next time?"

Ask an LLM the same question twice and the answer can move. Validis returns the same numbers on every run, because the calculation works the same way every time. Reviewers and re-runs land in the same place.

Defensibility

"Can I defend it if someone checks?"

An LLM can’t reliably show its working, which is hard to stand behind in a regulated review. Every Validis step is logged and can be replayed exactly as it stood.

Predictable cost

"What does it cost to run at scale?"

Each AI retry and re-prompt burns more tokens. Validis runs the math once, for a cost you can put in a budget.

OUR ROLE

Where AI works. Where it doesn't.

Validis isn’t a model. It isn’t a workflow. It’s the financial data layer your AI agents and copilots run on. Pulling transaction-level GL, TB, AR and AP and delivering it ready to use.

The LLM does the language. Validis does the math and the data underneath. In an agent workflow, the model is a broker, moving trusted data from one step to the next. It never invents it.

Whatever LLM your firm uses today and in three years — everything runs on the same data. Connect once. Standardize once. Govern once.

Some tasks need interpretation. Others need exactness. The firms getting AI right are the ones who know the difference.

NON- DETERMINISTIC

AI & LLMs

AI is built to interpret, not to record. Ask the same question twice and you can get two different answers. That’s powerful for reasoning. It’s the wrong tool for anything that has to be exact.

Right tool for Drafting reports. Summarizing findings. Surfacing patterns. Generating first-pass narratives across audit, advisory and lending workflows.
✕ Wrong tool for Collecting client financial data. Evidencing audit conclusions. Calculating covenant ratios. Anything that has to be exact and the same every time.

DETERMINISTIC

Financial data extracation

Financial data has one correct version. It must be exact, traceable, and the same every time the AI asks for it. That’s a job for Validis, not an LLM.

Right tool for Collecting, standardising and governing client financial data — straight from the ERP.
✕ Not A chat model. Not a generator. The deterministic data layer your AI workflows sit on top of.

Getting the data right is a deterministic job.

Financial data has one correct answer. It must be the same every time the AI asks for it.

Validis sits beneath the AI layer. We do the deterministic work, so the AI on top can do what it does best.

What AI workflows actually need

Six things every AI workflow needs to work.

Drawn directly from what firms have told us in the last six months. Each one is a data problem, not a model problem. Each one is what Validis was built to solve.

Deterministic math, not AI math. Analytics run inside Validis, so the numbers don’t drift between runs.
Structured accounting data, not just documents. GL, TB, AR, AP , directly from the ERP.
Clean, standardized data across every client and every system. So one prompt works everywhere.
Refreshable, traceable data. So AI outputs can be audited and re-run.
Compatibility with the LLMs your firm already approves. Copilot, ChatGPT, Claude, internal stacks.
Governance built in. Privacy, retention, audit logging, regional residency.

Great AI needs great data

What that looks like in production.

Below is what firms are running today on their own LLM stack, with Validis as the trusted data layer underneath. Same prompt, every client, every system. That’s what great data does for AI.