Avianca

Avianca, one of Latin America's largest airlines with over 21,000 employees and operations across 27 countries, faced a mounting contract management crisis in its in-house legal function. The team was responsible for reviewing and managing contracts spanning three languages — English, Spanish, and Portuguese — across multiple jurisdictions, all using manual review methods that were both slow and resource-intensive. When COVID-19 triggered government-enforced travel restrictions and severe revenue pressure, the financial case for modernization became unavoidable. Relying on external counsel for complex reviews added cost and reduced internal control, making the status quo unsustainable.

Avianca deployed Luminance's AI platform — which uniquely combines supervised and unsupervised machine learning — to automate contract reading, clause identification, and cross-document pattern recognition across its full contractual dataset. Unlike systems requiring extensive training before delivering value, Luminance provided actionable insight from day one: automatically surfacing key commercial clauses including Termination, Assignment, Confidentiality, and Indemnification without manual tagging of every example. Its supervised ML layer enabled rapid learning from attorney input — tagging a single Spanish-language 'Proveedor' clause instantly flagged five similar instances across the dataset. The platform's language- and jurisdiction-agnostic architecture was critical for Avianca's multilingual document environment, eliminating the need for separate workflows by language.

Avianca achieved a 90% reduction in contract review time on its first Luminance project, completing a 1,000-document review in just three hours — a task that would have consumed far more attorney time under the prior manual approach. Key outcomes:

• 30,000+ documents subsequently brought under Luminance management, spanning supplier contracts, NDAs, and employment agreements
• Reduced reliance on external counsel for complex reviews, shifting work in-house and improving cost control
• Legal team gained direct visibility into the full contractual landscape across languages and jurisdictions
• Faster response capability to legal risks and commercial clause deviations at scale

• Unsupervised ML removes the cold-start problem: platforms that deliver insight on day one — without lengthy attorney training — are better suited to in-house teams with limited AI implementation resources.
• Multilingual capability is a prerequisite, not a feature: legal teams operating across jurisdictions should evaluate AI tools on language coverage before any other criterion.
• AI shifts work in-house: by reducing dependence on external counsel, AI contract review can deliver both direct cost savings and greater institutional knowledge retention.
• Scale signals adoption health: expanding from a 1,000-document pilot to a 30,000-document program within the same deployment indicates strong practitioner confidence in accuracy and usability.