Do we need AI for this process?
Maybe not. If deterministic rules and integrations solve it more reliably, we will recommend ordinary automation.
Automation and AI
Workflow automation and applied AI.
Automate the work, not the uncertainty.
We start with the repeatable process and its exceptions. Deterministic automation comes first. AI is added only where interpretation of unstructured information creates real value, with evaluation, human control and a manual fallback.
When this helps
Useful when people repeatedly move information between systems, copy data, prepare routine documents, route requests or make the same bounded interpretation over and over.
Typical work
- 01
Map inputs, outputs, owners, exceptions and the current manual path.
- 02
Automate deterministic steps across forms, documents, notifications, APIs and business systems with logging and error handling.
- 03
Where needed, add bounded AI for classification, extraction, recommendation or tool use within explicit rules.
Regional availability
Automation and AI · Poland
- Available
Available after scope and capacity confirmation.
What you get
What you get
- A process map separating deterministic workflow logic from the steps that genuinely require probabilistic interpretation.
- Defined data, tool and action boundaries, with human approval points and a manual fallback where risk requires them.
- An evaluation set and report covering task quality, known failure modes, latency and cost, plus deployable workflow artefacts and operating guidance.
How the engagement works
How the engagement works
- 01
Decompose the business process and prove why ordinary automation is insufficient for the selected step.
- 02
Define allowed data, outputs, actions, risk classes, evaluation cases and the human decision owner.
- 03
Build a bounded pilot or production workflow with deterministic orchestration around the AI component.
- 04
Evaluate the whole workflow, pass the readiness gate and hand over code, evaluation assets, controls and residual risks.
Boundaries
Boundaries
- We do not sell a universal autonomous AI employee or promise the absence of hallucinations.
- High-impact legal, financial, employment, medical or comparable decisions require a human owner and are not delegated by default.
- Production SLA, broader autonomy or 24/7 managed operations require separate evidence, capacity and operating agreements.
Acceptance & handover
Acceptance & handover
- Representative cases and grading criteria are agreed before production acceptance.
- Quality, latency and cost are measured against the agreed thresholds for the whole workflow.
- Permissions, human-review rules, exception paths, auditability and fallback are verified.
- Known failure modes, provider dependencies and residual risks are documented for the future owner.
Common questions
Common questions
Can the agent act without approval?
Only for explicitly bounded actions where risk, monitoring and rollback justify that level of autonomy.
Can you guarantee that the model will never be wrong?
No. We design evaluation, controls and fallback around the fact that model behaviour is probabilistic.
Evidence, not promises
Evidence, not promises
Our public engineering method uses the same sequence: establish facts, define the boundary, change safely and hand over a controlled state.
How we workThe result
A workflow that remains understandable
- A documented process with clear inputs, outputs, owners and exception paths.
- Deterministic steps automated with logging and error handling.
- AI, where used, bounded by explicit rules, evaluation and human control.
- A manual fallback and enough documentation to change or disable the automation safely.
Start with one repetitive process.
Show us what people do manually and where exceptions occur. We will tell you what should be deterministic, where AI may help and where it should not be used.
Discuss this with us