AI integration and automation from Poland, built around a real process.
Automate a defined workflow with measurable baseline, approved data access, human controls and an operating owner. Westom does not sell a generic chatbot as transformation.
Which AI automations are worth building?
The best candidates have repeatable inputs, a clear owner, measurable handling cost or delay, acceptable error consequences and a practical human fallback.
We compare deterministic automation, search, rules and conventional software before adding a model. Generative AI is used where variation or unstructured information creates value and where outputs can be evaluated. Sensitive or high-impact decisions require stricter governance and may be unsuitable for automated action.
Good fit
- A named process owner can describe the current workflow and exceptions.
- Baseline volume, time, error or service metrics are available.
- Data sources, permissions and retention can be reviewed.
- Users can test outputs and own the post-launch process.
Not the right fit
- The objective is “add AI” without a process or user.
- Production data must be sent to an unapproved tool.
- No one can define a harmful or unacceptable output.
- A prototype is expected to operate without monitoring, fallback or maintenance.
An automation that can be evaluated and operated
The output includes the process change, controls and ownership—not only a prompt or API connection.
Process discovery
Actors, inputs, systems, decisions, exceptions, volume, delay, cost and failure impact.
Option assessment
Rules, workflow automation, search, model-assisted or custom-software options compared.
Data and risk design
Data classes, permissions, retention, vendors, human review and prohibited actions.
Prototype and evaluation
Representative test set, quality rubric, latency and cost measures, adversarial and edge cases.
Integration and rollout
Authentication, logging, queues, interfaces, fallback, user training and staged release.
Monitoring and ownership
Quality sampling, drift, cost, incident path, vendor changes and a named business owner.
Prove usefulness before expanding access
Baseline
Measure the current process and define the decision the pilot must support.
Design
Choose the simplest viable automation and document data and risk controls.
Evaluate
Test representative and difficult cases against explicit acceptance thresholds.
Roll out cautiously
Release to a limited group, monitor outcomes and expand only with evidence.
Quality controls
| Quality | A maintained evaluation set, scoring rubric and sampled human review. |
|---|---|
| Data | Approved sources, minimum access, retention rules and vendor terms. |
| Safety | Prohibited actions, confirmation steps, fallback and incident escalation. |
| Economics | Usage, latency, operating time and cost per completed business outcome. |
No autonomous-agent promise and no guaranteed productivity figure
Model behaviour, vendor features and prices can change. Westom documents the evaluated version, data route, thresholds and fallback. Legal classification under the EU AI Act, data protection or sector rules requires qualified review for the actual use case; a technical prototype is not that review.
Related guidance
Do you start with a chatbot?
Only if conversational interaction is the right interface. Many useful projects are document workflows, classification, extraction, search or assisted drafting.
Can we use our internal documents?
Possibly, after permissions, sensitivity, retention, indexing and deletion requirements are mapped. Access to a source does not automatically permit every AI use.
How do you measure accuracy?
Against a representative evaluation set and task-specific rubric. One generic percentage is usually misleading, so we also track harmful failures and human correction.
Can you guarantee ROI?
No. We establish a baseline, model implementation and operating costs, then measure the controlled pilot before scaling.
Bring one process, its evidence and its owner.
Describe the current workflow, volume, systems, sensitive data, cost of delay and unacceptable outcomes. We will assess whether AI, simpler automation or no build is the right answer.