Process and Decision Automation
Decisions that hold up. Processes that move.
This is the layer underneath an operation, and it is two halves that belong together. Decisioning makes the consequential call the same way every time and can show why. Automation is the process, workflow, and orchestration that gets a case decision-ready and carries the outcome onward. A perfect decision trapped behind a broken process never moves; a smooth process making inconsistent calls is worse than useless in a regulated setting.
AI projects stall when too little thought goes into the processes and decisions around them. Adopt AI without getting those right first, and a return is hard to show.
That is why we start with the process and the decisions, then fit the right technology to them. Often that includes AI. Not always.
How it works
Understand, externalise, transform
It is not rocket science, but it takes pragmatism, care, curiosity, and experience to do well.
Understand what matters, and why
We start with how the business actually works: where decisions sit in the operation, the rules that determine outcomes, the handoffs that slow things down, the data that has to arrive before a decision can be made, and the manual workarounds that have grown up around the gaps. We map that reality, identify what should change, and tell you honestly whether the work is worth doing or whether you are chasing the wrong problem.
Externalise the logic
We know where to dig to find how things truly work inside an organisation. We cut through the tangle, sift through code, and break down policies, engaging the right people so facts, perception, and experience are all valued. We collect grounded data that informs the future-state design, so no one is left wondering how we got there.
Transform the process around the decision
With clarity in hand, we get to work. We are analysts and architects at heart, with a working grasp of the evolving methods and platforms available to get more done with less. We are also accustomed to heavily regulated environments, where every decision made must be transparent and auditable.
The work runs on governed decisioning and integration platforms. See the Decisioning & Automation Platforms behind it →
What changes
Efficiency in operations, confidence in decisions
Once the decision logic is externalised and the process is automated around it, three things change. You get a better understanding of what drives good outcomes, and your people focus on what matters most. Your business, optimised.
Decision and process agility
Crucial business rules live in one place, visible to the people responsible and changeable in days rather than quarters. The process around them moves at the same pace, so a change to the logic is not held up by the workflow that carries it.
Every decision traceable, every time
One correct answer, surfaced every time, with no drift. Even at full automation, every decision stays correct and reproducible, and it does not take a workforce to prove it.
Experiences, redefined
Employee engagement lifts when people focus on real value instead of manual workarounds. Customers get intuitive, efficient, modern service because the decision and the process behind it are fast and reliable.
Patterns we have shipped
Patterns we have defined, deployed, and proven
Patterns are a sign of repeatable work, and we are practised at seeing business models and processes through a repeatable lens. That repeatability makes us efficient, because we have done this before. It does not mean one size fits all: we shape the pattern to your context rather than forcing a square peg into a round hole.
Externalising decision logic from the system that runs it
Pricing, rating, eligibility, underwriting, approval, qualification. Rules pulled out of the systems they are trapped in, onto a platform the business can read, test, and change with confidence. Crucial where speed to market matters. Rating is one of the clearest examples of this pattern, and one of the largest bodies of work we run.
Where we have shipped thisSolution Underwriting (now CFC), Envest, Solvar, multiple state government departments.
High-volume operational decisioning at scale
Millions of cases a year. Real-time and batch validation and calculation processing in milliseconds with a full audit trail. New products, new channels, and new regulatory requirements absorbed without re-platforming.
Where we have shipped thisFidelity Life, Integrity Life, Incitec Pivot, multiple state government departments.
AI-augmented decisioning
AI applies semantic reasoning to find the facts you are looking for. Rules make the final decision. The interface between the probabilistic and the deterministic, where productivity matters but there is no room for error.
Where we have shipped thisOur AI agent platform in banking and customer service. Decisioning work in insurance underwriting and financial services credit.
Agentic process orchestration
Complex processes often span multiple systems, teams, and decision makers. Agents do the heavy lifting of coordination, gathering context, and isolating facts, then hand off to humans and to the rules that make the call.
Where this appliesOrigination journeys, multi-stage underwriting, claims first-notice-of-loss, high-volume government service delivery.
A reference pattern
The Decision Pyramid
This is our pattern for decomposing how AI and rules fit together in a decision process. Two layers, each doing what it is best at: AI clears the effort layer, and deterministic rules govern the decision layer. Drawing the boundary clearly between them, the interface, is what makes the whole thing work reliably and consistently.
Business workflows are made of two things: decisions and effort. Most business functions already have both layers operating in some form. The question is whether they are clearly separated. Is each layer doing what it is best at, or is one carrying the wrong responsibility?
Where this applies
Just some of the industries we support
No organisation should ignore the benefits of well-defined decisions and the processes that carry them. These are some of the industries where our process and decision automation work runs.
Insurance
Rating, underwriting, claims, and pricing decisions at the core of the business, with the workflow around them.
Financial Services
Credit decisioning, qualification, and high-volume risk assessment in lending and fund operations.
Government
Regulatory lodgement validation, eligibility, and the high-volume citizen-service decisioning underneath.
Logistics
Rating, routing, and validation decisions at volume, with the orchestration that moves each case through.
Industrial & Manufacturing
Recommendation logic and operational rules pulled out of ageing systems onto a platform the business can change.
Retail & Hospitality
Eligibility, pricing, and process rules that keep multi-site and multi-brand operations consistent.
Related thinking
Go deeper
Questions we get asked
What is the difference between decision automation and process automation?
Decisions are the consequential choices; process is how work flows between steps and systems. They are two halves of the same capability, and most stalled work needs both fixed.
What is rule externalisation and why does it matter?
Pulling business logic out of application code onto a platform the business can read, test, and change, so rule changes take days, not a release cycle, and do not need developer effort - the rules are in the hands of the people who understand them best.
Can business users change the rules safely?
Yes. That is the point of a no-code decisioning platform: owners change logic within guardrails, with a full audit trail.
How do you keep decisions auditable in a regulated environment?
Through versioned rules, immutable decision logs, and the ability to replay any decision against the rule version in force at the time.
Where does AI fit, and where does it not?
AI gathers information from unstructured data, cleans it up, and gets it ready for a decision; then deterministic rules make the decision. The two are kept separate, so consequential decisions stay correct and auditable.
How long before we see value?
Most engagements start with a scoped discovery and a first decision in production early, rather than a multi-quarter build before anything ships.
What does deterministic mean, and why does it matter for rules?
Deterministic means the same inputs always produce the same output, every time. Probabilistic means the output can vary, even on the same inputs. Rules are deterministic by design: if the driver is under 25 and the vehicle is a sports car, then apply the young-driver loading - the same facts always give the same answer. That is what makes a decision auditable, which is why the decision layer is built on rules, not on a probabilistic model.
What is the Decision Pyramid?
The Decision Pyramid is our pattern for decomposing a decision process into two layers: AI clears the effort of getting a case decision-ready, and deterministic rules govern the decision itself. The interface between them, where AI hands what it found to the rules, is what makes the whole thing reliable.