AI data readiness

Is your data AI ready?

Hornbill Flight Check is a market-leading, AI-powered data analysis service designed to ensure your organization’s data is fully agentic ready. Delivered by Hornbill Professional Services and leveraging HAi Labs' proprietary Machine Learning (ML) and AI models, this service provides the essential foundation for successful AI implementation.

Data quality is the key to AI success

Data quality is the number one factor for success with AI, outranking all other implementation concerns. Without a clean, consistent, and structured foundation, organizations face implementation delays and the risk of AI hallucinations, which can destroy user trust in new capabilities.

Hornbill Flight Check eliminates these risks by using advanced LLM tooling to analyze, cleanse, and optimize historical service management data and existing knowledge content.

Why Hornbill…

We’re saving thousands of hours each year by automating low-value interactions.”

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How Flight Check works

The process requires a data export providing details of service request, incidents, problems, KPIs, current taxonomy used to categorise data. Our data team is covered by stringent ISMS and ISO certified data governance procedures and can advise on removing PII.

Our AI Lab environment uses the latest processing power available to cycle through large volumes of request data leveraging Flight Check’s algorithm and Machine Learning models. Your data is not shared with any external models.

A proven, secure process

The entire Flight Check process is designed to take no more than five days, minimizing disruption to your daily operations. The analysis is conducted within a secure AI Lab environment where your data is never shared with external models.

Phase 1: Data profiling and quality assessment

The goal of this phase is to interrogate your existing ticket records to surface anomalies and inefficiencies.

ML-driven ticket auditing: Consultants use ML models to identify poor descriptions and lifecycle patterns that may hinder automation.

Categorization accuracy check: This process benchmarks your current category usage against insights derived from Natural Language Processing (NLP).

Semantic Clustering: Tickets are grouped based on natural language similarity, which helps expose hidden trends and frequent misclassifications that traditional reporting might miss.

Phase 2: Data cleaning & normalization

Once the data is profiled, Phase 2 focuses on standardizing it for the Hornbill platform and optimizing it for AI performance.

Field mapping & standardization: The service uses AI-generated mappings to transition data from legacy fields into Hornbill’s structured schema.

Category normalization: Your category tree is rationalized and streamlined to ensure clarity and improve the reliability of automated routing.

Text enrichment: For records with sparse information, LLMs are used to extract intent, generate tags, and add resolution context, making the data more useful for future AI training and retrieval.

Phase 3: Knowledge alignment & generation

The final phase ensures your knowledge base is robust enough to support self-service and automated deflection.

Knowledge coverage mapping: This involves evaluating existing knowledge articles against historical ticket patterns to see how well they address common issues.
Gap analysis: Consultants identify high-volume request types that currently lack adequate knowledge support.
LLM-generated articles: To fill identified gaps, the service automatically produces new seed articles based on proven resolution patterns found in your historical data.

Build an AI-ready data foundation

Hornbill Flight Check leverages proprietary Machine Learning and AI models to analyze and optimize your historical ticket data, ensuring your environment is AI-ready from day one.