ENTERPRISE PROCESS AUTOMATION & AI

Reduce manual work. Introduce AI where it adds value.

We help enterprises automate manual work, connect existing systems and apply AI where it adds measurable value. From documents to cross-system workflows, we build around your technology.

Connected systems supporting process automation
Operational friction

Where manual processes slow operations

Manual steps between systems slow operations. Teams re-enter data, chase approvals and check documents instead of moving work forward.

01

Repeated data entry

Teams copy the same information between systems, increasing effort and the risk of mistakes.

02

Slow approvals and handoffs

Requests wait between teams because routing and approvals depend on manual follow-up.

03

Disconnected systems

Information sits across applications that do not communicate effectively.

04

Document-heavy work

Employees spend time reading, checking and transferring information from documents.

05

Inconsistent request handling

Similar requests are handled differently depending on the person or team involved.

06

Manual reporting

Teams consolidate information from multiple systems before they can act on it.

What we can automate

We combine workflow automation, system integration and AI to reduce manual work.

Enterprise AI platforms

We work with Claude, OpenAI, Amazon Bedrock and Microsoft Foundry, choosing platforms to fit your workflows, security requirements and existing architecture.

  • Claude logoClaude
  • OpenAI logoOpenAI
  • Amazon Bedrock logoAmazon Bedrock
  • Microsoft Foundry logoMicrosoft Foundry

From friction to a working process

We map the process, establish a baseline and test a focused workflow. Measurable results guide wider adoption.

  1. Map the process

    Understand systems, users, handoffs, exceptions and current bottlenecks.

  2. Prioritise opportunities

    Focus on workflows where automation can create measurable operational value.

  3. Pilot and integrate

    Build a controlled solution and connect it to the systems already in use.

  4. Measure and improve

    Track performance, exceptions and outcomes before expanding automation further.

Automation with accountable control

We build permissions, approvals, data controls and human review into each workflow, keeping automated actions traceable and exceptions manageable.

Keep execution controlled

Use permissions, validation and approval rules around automated actions.

Make exceptions manageable

Route uncertain or unusual cases to the right person instead of forcing automation.

Use AI selectively

Apply AI where interpretation is useful, while keeping critical decisions controlled and traceable.

Connected workflows in practice

We helped build a unified platform for 3,000+ Belron technicians worldwide, connecting existing systems and bringing technical information to frontline teams.

Process automation and AI adoption FAQs

Traditional process automation follows predefined rules. AI-assisted automation is useful when a workflow includes information that fixed rules cannot easily interpret, such as emails, documents or natural-language requests. In many enterprise workflows, the two approaches work together.Related reading: Process Automation vs AI Automation: Where Does AI Actually Add Value?

Start with repetitive, high-volume workflows where handling time, errors or operational cost can be measured clearly. Processes with frequent manual handoffs and repeated data entry are often strong candidates.

Yes. We can connect APIs, databases, SaaS platforms, internal applications and legacy systems so information can move between them without unnecessary manual work.Related reading: Introducing AI into Existing Enterprise Systems: Where to Start

AI is not always the right tool. Processes based on predictable rules are often better handled with traditional automation. AI should also not make consequential decisions autonomously where accuracy, accountability or regulation requires human control.

We design workflows with validation, access controls, auditability and clear exception handling. Cases that require judgement can be routed to a person rather than processed automatically.

We agree measurable outcomes before implementation. These may include handling time, error rates, rework, cost per case, throughput or escalation rates.

For a clearly defined workflow, an initial pilot can often be delivered within a few weeks. The exact timeline depends on system access, integration complexity and the scope being tested.

Let's talk automation and AI

Tell us where manual work slows your teams down. We can explore where automation would help.

You’ll hear directly from Mark and Leo, who lead delivery end to end.

Mark Avdi

Chief Technology Officer

Leo Lam

Operations Director