AI Transformation · AI Product Engineering
Intelligent Process Automation
We map how a process really runs, decide which steps rules, AI and people should handle, then build the workflow and its integrations, with exceptions routed to the right person with everything needed to decide.
Example: supplier invoices
Who does each step
- Rules
- AI
- People
If matched: straight through
Capture invoice
Read fields
Three-way match
Suggest cause
Approve exception
Post to ERP
- 1Capture invoiceRules
- 2Read fieldsAI
- 3Three-way matchRules
- 4Suggest causeAI
- 5Approve exceptionPeople
- 6Post to ERPRules
Invoices that match post without anyone touching them. Mismatches reach a person with the suggested cause.
How to get started
Three steps to a plan for Intelligent Process Automation
- 1Tell us what you needUse the project form or book a call. A few sentences about the goal is enough to start.
- 2Free technical consultationWe go through your goals, users, existing systems and constraints with you.
- 3Your planA detailed plan covering the right tech stack, architecture, timeline and budget. Then you decide.
Before and after
The same process, redesigned
Automation starts with the process, not the tool. Switch between the two to see what changes at each step, and who or what does it.
Measured before and after
- Straight-through processing rate
- Cycle time
- Exception rate
- Cost per case
- Error and rework rate
Supplier invoices, from receipt to posting
- 1CaptureInvoices are captured from email, portal or scan, and AI reads their fields with a confidence score for each.AI
- 2MatchRules perform the three-way match; invoices that match post without anyone touching them.Rules
- 3ExceptionsEach mismatch gets a suggested cause and coding, and goes to the right queue with its documents attached.AI
- 4ApprovalPeople approve in the queue, within their approval limits, and every decision is logged.People
- 5PostingApproved invoices post to the ERP, and managers see volumes, exceptions and ageing on a dashboard.Rules
Tools
The right tool for each step
Most processes need several of these. We choose by step, not by platform.
Process mining
Finding how the process really runs, from the event logs of the systems involved, before anything is automated.
Intelligent document processing
Reading invoices, forms and letters, and extracting their fields with a confidence score for each.
Business rules and workflow
Decisions with clear rules, routing, queues, approval limits and service levels.
APIs and integration
Reading from and writing to your ERP, CRM and other systems directly.
Robotic process automation
Operating the screens of systems that have no API, as a person would. Used where an API isn't available.
Language models
Classifying requests, suggesting causes and drafting replies, where unstructured text needs judgement.
Exceptions
Designed in, not left over
Automation fails when every exception lands back on a person with no context. These are part of every workflow we build.
Confidence thresholds
Below an agreed confidence, a person checks the extracted value before it is used.
Full context in the queue
Each exception arrives with the document, the extracted data, the mismatch and the suggested action.
Approval limits
Who may approve what, by amount and type, enforced by the workflow.
Service levels on queues
Ageing alerts and escalation, so exceptions don't stall unnoticed.
An audit trail
Every automated and human decision recorded: what, who or which rule, and when.
Safe fallback
If a connected system is down, work waits in the queue and resumes; nothing is lost or posted twice.
Deliverables
What you receive
- A current-state process map, with its exceptions
- A future-state design: rules, AI and people for each step
- The automated workflow in production
- Integrations with the systems involved
- Exception queues with approval limits
- An audit trail for every case
- A process dashboard: volumes, exceptions and ageing
- Measures before and after
Alongside automation
Where the work goes next
- Enterprise AI IntegrationAI Integration & Legacy ModernizationReplace screen automation with APIs as older systems are modernized.
- AI Product EngineeringMachine Learning DevelopmentAdd predictions to a step, such as the risk of a duplicate or late payment.
- AI ConsultingAI Adoption & Change ManagementPrepare the teams whose work moves from keying data to handling exceptions.
Questions
Questions about
Intelligent Process Automation
Which processes are good candidates?
High-volume processes with clear rules for most cases and judgement for some, digital or digitizable inputs, and results you can measure. Supplier invoices, claims intake, customer onboarding checks, order entry and service request routing are common examples.
Is this the same as RPA?
RPA is one of the tools. We use APIs where they exist and RPA where they don't, together with document processing, workflow, rules and language models, each for the steps it suits.
Can you build on the automation platform we already have?
Yes, where it suits the process. We build on the workflow, RPA and integration platforms you already run, and explain any addition we recommend.
How do we know it worked?
By the measures agreed at the start, such as straight-through processing rate, cycle time, exception rate and cost per case, compared with the baseline taken before automation.
How is a project priced?
Well-defined scopes are delivered as fixed-price engagements; when requirements are still evolving, we provide a dedicated team instead. Either way, the free technical consultation ends with a plan covering tech stack, architecture, timeline and budget, so you know the cost before work starts.
AI Product Engineering
Other services in this line
- AI Proof of Concept & MVPA small working version with agreed success criteria, to test value and feasibility before a full build.
- Generative AI DevelopmentAssistants, search and agents built on large language models, grounded in your own content and tested before release.
- Machine Learning DevelopmentPrediction, classification and recommendation models trained on your data, validated on data held back and explained.
- MLOps & AI OperationsDeployment, monitoring, retraining and support for AI in production, with its cost and results tracked.
Ready to talk about Intelligent Process Automation?
Start with a free technical consultation: a plan covering the right approach, architecture, timeline and budget for your AI work.
Start a project