See how the work is structured
Learn how we assess the process, business case, data security, and quality before development begins.
Explore the methodology →Expert guide
How to reduce AI errors and hallucinations
Reliable AI comes from architecture, trusted sources, validation, test sets, and production monitoring—not from a single prompt.
Separate error classes
Track fabricated facts, omitted facts, incorrect classifications, invalid formats, stale sources, instruction violations, and unsafe actions as separate error categories.
- Severity
- Frequency
- Detectability
- Business consequence
Define a source of truth
Factual answers should rely on approved and versioned sources. When evidence is missing, the system should say it cannot verify the answer, ask a clarifying question, or escalate to a person.
- Source identifiers
- Versioning
- Citations
- Abstention
Validate outside the model
Use schemas, allowed values, type checks, reference integrity, and business rules. Asking the model to check itself is not a deterministic control.
- JSON Schema
- Required fields
- Business constraints
- Permission checks
Run regression and production monitoring
Maintain normal, edge-case, conflicting, and adversarial examples. Re-run them after every change to the model, prompt, data, or tools.
- Acceptance test set
- Manual review
- Drift monitoring
- Error feedback
Choose your next step
Start at the stage that matches your situation
You do not need a detailed brief to begin.
Assess one business process
Determine whether the task calls for AI, rule-based automation, or a process redesign.
Assess a process →Share a defined task
Send the current process, constraints, and expected outcome, and we will suggest a practical first step.
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