Composable AI for Compliance-Critical Workflows

AI is reshaping how finance and accounting teams operate. Tasks that once relied on rigid rules and manual review are increasingly automated — not with templates, but with models that learn, adapt, and make decisions.

But while the technology has evolved, the design principles around where and how AI fits into product architectures haven’t always kept up.

In areas like accounts payable (AP), where compliance and traceability matter as much as speed, it’s not enough to build automation — we need modular, dependable components that slot into larger systems without taking over the whole show.

This is where composable AI becomes essential.

Platforms Create Value Through Orchestration — Not Reinventing the Core

ERP and AP platforms are responsible for guiding users, enforcing policies, and adapting to industry-specific needs. Much of the value in these platforms comes from their ability to orchestrate workflows:

• Deciding what actions to take, and in what order
• Combining rules, models, and human input
• Adapting to the expectations of a specific industry or customer segment

That orchestration layer — the glue between systems, rules, and people — is where differentiation happens. And it's where platforms should retain full control.

But within these orchestrated flows are certain tasks that remain remarkably consistent across domains.

Some Elements of Bookkeeping Are Universal — and High Stakes

No matter the industry, invoice processing always includes a few critical steps:

• Determining how to post invoice lines to the general ledger
• Ensuring the correct VAT treatment is applied
• Extracting structured data from invoice documents
• Reconciling payments with bank data
• Producing documentation that holds up under audit

These tasks don’t change much from one sector to another — and they also don’t leave much room for error.

Precision is not a nice-to-have here. It's fundamental to compliance, to reporting, and to the trust finance teams place in the systems they use.

These Core Tasks Can Be Automated — But They Need Guarantees

Many of these repeatable, rules-based tasks are excellent candidates for automation through AI. But not all AI approaches are equal, and understanding the difference between specialized and generalized AI is crucial when designing systems that must operate with precision.

Generalized models, like large language models, are trained to handle a wide range of tasks. They’re flexible, adaptable, and often impressive in open-ended contexts. But that breadth comes with trade-offs in control, predictability, and accuracy.

Specialized models, on the other hand, are designed to do one thing well - and to do it consistently. They’re trained on specific types of data, constrained to a well-defined task, and calibrated to return outputs with known confidence levels. In compliance-critical domains like finance, this kind of specialization isn’t just beneficial, it’s necessary.

For example, large language models (LLMs) are powerful tools for orchestrating flexible workflows, interpreting fuzzy input, or generating communication. But when it comes to tasks that require calibrated accuracy - like classifying expense types, determining VAT codes, or validating against tax rules - LLMs are not well-suited.

They can be overconfident, inconsistent, and difficult to monitor. And in bookkeeping, it’s not enough for an answer to sound reasonable. It needs to be right - and provably so.

This is where narrower, specialized AI services have a role to play. When well-calibrated and domain-trained, they can deliver high-confidence outputs with clear boundaries and predictable failure modes. That makes them easier to trust and easier to build into larger systems.

Composability Enables Flexibility Without Sacrificing Quality

In a composable model, platforms assemble their workflow using modular services — combining structured AI components, human approvals, business rules, and vertical logic.

The orchestration engine decides how everything connects. The AI services return judgments on very specific tasks — like how to code a line item or whether an invoice violates a compliance rule.

This separation of concerns makes the entire system more robust:

• Platforms retain control over the customer experience
• Core components can be reused, tested, and monitored independently
• Failures are isolated, and decision paths are traceable
• New technologies (like LLM-based agents) can be added around the edges without compromising accuracy at the core

A More Sustainable Approach to Automation

Not every part of an AP workflow needs to be solved by a generalist model. And not every platform needs to build its own intelligence from scratch.

By combining domain-specific AI services with strong orchestration, finance platforms can move faster — without compromising on compliance or quality. They can focus on adapting to customer needs, not rewriting the foundations of bookkeeping.

Composable architectures make this possible.

And for the tasks where precision matters most, composable AI services offer a dependable foundation to build on.