I have spent the better part of three decades inside some of the world's largest automation programs. At IBM, at some of the largest financial institutions, and as an advisor to organisations trying to make automation work at scale. I have seen what succeeds. I have seen, far more often, what does not.
The failure point was almost always the same.
Automation performs well on the cases it was designed for. The structured inputs, the predictable formats, the workflows that follow a defined path every time. But real business processes are rarely that clean. Documents arrive in formats no one anticipated. Processes hit exceptions that the original design never accounted for. Systems change. Regulations shift. And the automation that was supposed to eliminate manual work quietly generates a new category of it: exception queues, maintenance cycles, retraining efforts, spread across teams in ways that rarely show up cleanly in a single budget line.
The productivity problem is bigger than it looks
There is a broader context that shaped how we thought about what to build.
Across developed economies, the factors that have historically driven productivity growth are under pressure. Workforce demographics are shifting. Regulatory complexity is increasing. The cost of skilled labour in knowledge-intensive industries continues to rise. And the operational processes that organisations depend on, the document-intensive, exception-heavy workflows at the core of financial services, insurance, healthcare, and supply chain, have not kept pace with the scale and complexity being asked of them.
The productivity gap is not primarily a technology gap. Organisations have invested heavily in automation for decades. The gap persists because the technology has been applied to the easy cases, while the hard cases, the ones that consume the most time, carry the most risk, and generate the most cost, have been left to people.
This is not a sustainable position. The organisations that figure out how to automate the hard cases will have a structural productivity advantage over those that do not. That was the market context that convinced us the timing was right to build something genuinely different.
The industry has known this for years
The response has been to build better templates, train better models, write more rules. Each generation of tooling improved on the last. But the fundamental architecture stayed the same: automation that works by matching inputs to patterns it has been taught to expect. The moment reality diverges from expectation, the system fails.
What we believed from the beginning
Before large language models became the dominant conversation in enterprise technology, we had already reached a conclusion about what the problem required.
The reason conventional automation fails on complex, variable work is not a data problem or a compute problem. It is a perception and understanding problem. Automation fails because it cannot read context the way a trained human does. It cannot understand what a missing field means in the context of the document it came from. It cannot recognise that an exception is routine versus genuinely anomalous. It cannot infer an implicit value or calculate a derived field because it does not understand what it is looking at well enough to know that the field is implicit.
The answer, we believed, was automation built on human-like reasoning: the capacity to observe, interpret, understand context, and act with the kind of judgment that experienced people bring to complex work. Not pattern matching. Not template filling. Genuine comprehension of what the document or process is actually saying.
That conviction predates the LLM moment. It shaped the architecture of Syncura from the beginning.
The agentic moment and its limits
The arrival of large language models and agentic AI validated part of our thesis and complicated another part of it.
The validation: agentic AI demonstrated that machines could reason across steps, handle ambiguity, and operate across systems in ways that rules-based automation never could. That is a genuine advance, and it confirmed that the market was ready for a more intelligent approach to automation.
The complication: LLM-based agentic systems, powerful as they are, have weaknesses that matter enormously in enterprise production environments. They can reason and act, but they cannot reliably explain what they did or why. Their behaviour is probabilistic rather than deterministic. In a regulated industry, an unexplained output is not just an operational inconvenience. It is a compliance risk. And in high-volume document and process automation, where decisions compound across thousands of cases per day, opacity is not acceptable.
We use LLMs as one tool in the Syncura architecture. But we do not rely on them as the foundation. We combine them with other techniques and technologies specifically chosen to address what LLM-based approaches get wrong, building a system that is not just capable of reasoning, but capable of being held accountable for it. The result is automation that is governed, auditable, and deterministic. Every extraction, every decision, every routing outcome can be traced and explained.
The operations leaders we work with are not looking for automation that can think. They are looking for automation that can think and be held accountable. That distinction is the foundation of everything we build.
The two things our customers actually care about
After years of running automation programs, and many more conversations with the operations leaders who live with the results, we have found that the outcome conversation almost always comes down to two things.
The first is straight-through processing. How much of the work gets through automatically, without anyone touching it, and how confident are we that what goes through is accurate, governed, and explainable. Not automation for its own sake, but automation that holds up under scrutiny.
The second is human-in-the-loop efficiency. Because humans will always be part of the picture. The question is whether the work that reaches them is the work that genuinely requires their judgment, presented with the context they need to act quickly and correctly. The goal is not to eliminate human review. It is to make it faster, more focused, and more effective.
Everything we build at Syncura is designed around those two outcomes. Not around technology benchmarks or automation rates. Around what the people running these operations actually need.
Why now
The market is at an inflection point. Rules-based automation has reached its ceiling. Agentic AI is creating new possibilities and new risks simultaneously. Organisations are being told that transformation is one implementation away, and they have heard that before.
We built Syncura because we believed there was a better way to approach this. Not by adding another layer on top of the same architecture. By starting from a different premise about what automation needs to understand before it acts.
The hardest processes, the ones full of variability, exceptions, and documents that were never meant to be standardised, are also the most valuable ones to automate. They are where the most time is lost, the most cost accumulates, and the most opportunity sits untouched.
That is what we are here for.
Douglas Heintzman, CEO & Co-Founder, Syncura