Automation and AI Integration
Taking the repetitive work off your people, with a human still holding the approval.
What is worth automating
The work worth automating is usually the work nobody wants to describe: someone downloads a report, cross-checks it against a second system, retypes the differences into a third, and does it again next week. It is invisible in a job description and expensive in aggregate.
That kind of process automates well because the rules are real, even when they are only in somebody's head. Getting them written down is half the engagement.
Proof
For a parcel logistics provider we built an automated audit and refund-claim system that catches late shipments and files claims with UPS and FedEx on its own, combining robotic process automation with AI document processing. It removed the bulk of the manual claims and invoice-auditing work and recovered significant annual carrier refunds, shared between the company and its clients.
On the same platform we integrated Google Cloud Vision for document data extraction, which shortened delivery cycles on document-heavy work, and adopted an LLM across the development lifecycle.
We have also specified and delivered a production LLM analysis pipeline: multi-source ingestion, classification into schema-validated structured output, semantic deduplication and clustering, and automated synthesis reporting, with a human-approved loop that evolves its own sources and taxonomy each cycle.
How we work with AI, and where we stop
We use AI agents as real engineering tooling, not as a shortcut. Every change an agent makes is reviewed and approved by a person, driven end to end in a running system, and backed by a rollback plan before it goes live.
That discipline is not something the current wave of AI taught us. On optical telecom systems we set a static-analysis standard that failed the build when code violated it, and drove measured complexity down across thousands of functions. Automated enforcement, a baseline in version control, and a tracked trend. The habit of not trusting output until it has been checked is considerably older than the tooling.
What we will not do is point an unattended agent at a system a business cannot afford to break. If someone offers you that, the interesting question is who is accountable when it is wrong.
What we build
- Robotic process automation. Removing the retyping and reconciliation between systems that do not talk to each other.
- Document data extraction. Getting structured data out of invoices, statements, and scanned paperwork.
- LLM integration. Classification, extraction, and synthesis where the output is schema-validated rather than trusted as prose.
- Approval gates. Workflows where the automation prepares and a person commits.
- Verification and release discipline. Tests and type checks enforced on every push, so the automation does not quietly drift.
Who this is for
Businesses where a specific, repetitive, rules-driven process is consuming real hours every week, and where the cost of that process is now visible enough to be worth engineering away. It works best when the process has a measurable output, like recovered refunds, cycle time, or error rate, so the automation can be judged against a number instead of a feeling.
Common questions
Will an AI make decisions without a person involved?
Not on anything that matters. We build automation that prepares work and a person who commits it. Where an agent writes code or changes a system, the change is reviewed, driven end to end in a running system, and backed by a rollback plan before it goes live.
What kinds of processes are worth automating?
Repetitive, rules-driven work with a measurable output. If someone downloads a report, checks it against a second system, and retypes the differences into a third, that is the shape. It works best when there is a number attached, like recovered refunds or cycle time, so the result can be judged rather than felt.
How do you stop an automation from breaking silently?
Verification gates enforced on every change, and output validated against a schema rather than trusted because it looks right. Silent drift is the main failure mode of automation, so it gets designed against from the start.
Do we have to replace our existing systems first?
No. Most of this work sits on top of what you already run, which is usually the point: the systems do not talk to each other and a person is currently the integration.
Talk to us about your system
Every engagement starts with an assessment and a design you approve before anything changes. If you would rather talk it through first, that is free and there is no obligation attached to it.