AI and intelligent automation
Practical AI, built into the way your business runs.
Where AI helps
Automation for the work that slows your teams down.
- 01
Document processing and data extraction
Capture structured data from invoices, forms, contracts, certificates and correspondence, with validation rules and review queues for exceptions.
- 02
Classification and routing
Categorise incoming requests, emails and documents and route them to the right team, queue or system, with uncertain cases sent for review.
- 03
Workflow automation
Automate repetitive multi-step processes across systems, with clear rules for when a person needs to review or approve.
- 04
Data analysis and summarisation
Summarise large volumes of information, surface patterns and prepare draft outputs for people to review and act on.
- 05
Knowledge retrieval
Search and question internal documentation and records in plain language, with answers linked back to their sources.
- 06
AI features in your applications
Embed AI capabilities, drafting, extraction, classification and search, inside the bespoke applications your teams already use.
Governed by design
Automation you can trust, explain and control.
AI systems should support people, not operate unchecked. Every automation we build includes controls for accuracy, accountability and safety.
- 01Oversight
Human review where it matters
Defined points where people review, approve or correct outputs, particularly for decisions with financial, legal or customer impact.
- 02Validation
Confidence thresholds and business rules
Outputs are checked against business rules, and uncertain results are routed for review rather than processed automatically.
- 03Audit
A record of what was automated
Inputs, outputs and decisions are logged, so you can see what the system did, when, and why.
- 04Data
Data protection by design
We establish what data is processed, where and by which providers, and design around your data protection obligations.
- 05Measurement
Evaluated before it is relied upon
Automation is tested against agreed measures, such as accuracy and processing time, before it operates at scale.
How we deliver automation
From candidate process to dependable automation.
- 01
Identify the right processes
We map the candidate process, its volumes, inputs and exceptions, and establish where automation will genuinely help, and where it won’t.
- 02
Prove it with real data
A focused proof of concept measures accuracy against representative examples of your own documents and data before wider investment.
- 03
Integrate and govern
The automation is connected to your systems, with review queues, audit logging and controls built in from the start.
- 04
Monitor and improve
Performance is monitored in operation, and the automation is refined as your data, processes and the available models evolve.
Questions
Common questions.
Which AI models do you use?
We select models and providers to suit each use case, weighing accuracy, cost, data residency and each provider’s data-handling terms. Solutions are designed so that models can be changed as the technology evolves.
Is our data used to train AI models?
We establish each provider’s data-handling and retention terms as part of the design, and select services and configurations that are appropriate for your data and obligations.
Will automation make decisions without anyone checking?
Only where you decide that is appropriate. We design review points, confidence thresholds and audit logging so that consequential decisions remain under human control.
Can automation work with our existing systems?
Yes. Automation is most valuable when connected to the systems where work already happens, such as email, document stores, line-of-business applications and databases.
Let’s build something that works for your business.
Tell us what you’re trying to achieve. We’ll help establish the requirements, technical approach and most appropriate route to delivery.