AI and workflow automation for operations that cannot stay manual

For operations teams whose SLAs are eaten by copy-paste between systems.

Remove the manual bottleneck in a live operation: rules first, models only where the input is messy.

You leave with

A workflow that survives exceptions

A process diagram that names owners, systems, and exception paths.
A running workflow with retries, observability, and a human queue.
Clear rules for when a model is allowed to propose versus commit.
A measurement plan against the original bottleneck.

See it done

Synopsys · LLM evaluationBenchmarking and comparing large language models Genie Platforms · AI SDRConversational AI running as production services Plat · Lead capture SaaSLeads captured, validated and routed in real time

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I design and implement automation for companies whose delivery is limited by manual steps – not by a lack of dashboards. The default path is still a workflow: trigger, validate, execute, observe, recover. Models enter when the input is unstructured or the decision is ambiguous. They do not enter because 'automation' and 'AI' are now the same slide. I have built automation backends, content-ops pipelines, and AI-assisted product flows. The result you should care about is a process that survives bad input, retries, and the night shift – with a human path when it does not.

Who this is for

Operations and product leaders whose SLAs are eaten by copy-paste between systems.
Companies drowning in unstructured documents, tickets, or content that still needs a human for the last 10%.
Teams that bought a no-code automation tool and hit the wall on failure handling.

Problems this is for

A process works when the happy path is followed and collapses on the second exception.
People are used as the integration layer between SaaS tools.
An LLM was inserted into a workflow that needed a form and a retry policy.
Nobody can tell whether automation saved hours or created silent errors.

What I put in place

Process mapping that finds the actual bottleneck, not the loudest one.
Deterministic orchestration with idempotency, retries, and dead-letter handling.
Extraction and classification with models only where the input is messy.
Human-in-the-loop queues for the cases that should not be auto-committed.
Measurement: cycle time, error rate, cost per run – against the original constraint.

How the work proceeds

Diagnose the constraint. Separate rules, integrations, and genuine ambiguity. Automate the boring path first so the model is not compensating for missing engineering. Validate on real cases, including the ugly ones. Then production: typed payloads, observability, and a stop button.

When a process should not get a model

Most 'AI automation' requests are workflow requests. That is not a lesser project. It is usually the correct one.

If the input is already structured, write code

A status change, a paid invoice, a filled form – these are jobs for queues and APIs. A model here is latency and a new error class.

If the output is a side effect, require a contract

Creating a ticket, moving money, or publishing content is not a chat completion. Validate, then execute. Never execute from prose.

If the exception rate is high, design the exception path first

Automation that only handles 70% and dumps the rest in a shared inbox is how you create a worse job. Queue, SLA, and owner.

If you cannot name the hour you are buying back, wait

Automation for narrative is a product feature. Automation for operations needs a number, even a rough one.

Failure modes

Zapier-shaped graphs that cannot retry safely.
Prompting a model to 'be the intern' across five tools with stored credentials.
No idempotency keys, so a timeout double-sends.
Success measured as 'the demo ran' rather than error rate in week two.

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Pytania, które padają

Is this RPA?

Not in the 'drive the GUI of a 1998 app' sense. I automate through APIs, queues, and validated model outputs. If the only interface is a brittle UI, we should talk about whether the system should be replaced rather than puppeted.

Map the bottleneck before picking a tool

Send the process as it actually runs, including the exceptions – not the version in the SOP.