Six production-grade solutions.
Real commercial impact.

A single supplier pushing a property at €10 instead of €1,000 doesn't just lose you margin — it triggers customer complaints, erodes trust, and can spiral into chargebacks. Incorrect pricing, invalid availability windows, and currency mismatches enter your inventory silently and at scale. By the time your team spots them, the damage is done.

The system continuously compares live supplier data against historical pricing trends, nearby comparable properties, and seasonality patterns. Suspicious prices and invalid availability states are flagged in real time — before they ever reach a booking engine or customer-facing channel.

Too-short descriptions, outdated amenity lists, and missing images are silently costing you bookings every day. OTAs and GDSs reject low-quality listings outright. Vacation rentals and non-hotel inventory are chronically underrepresented — not because the supply isn't there, but because the content isn't good enough to pass muster.

The pipeline automatically generates SEO-optimised descriptions, conversion-focused sales copy, and concise summaries for every listing. It detects amenity mismatches, flags outdated or conflicting data, and supports multi-language auto-translation — giving every property the content quality it needs to perform across every channel.

A single property listed by five different suppliers — under different names, coordinates, and descriptions — creates pricing conflicts, confuses customers, and pollutes your platform with noise. The inconsistency leads to poor UX, bad reviews, and ultimately lost bookings. Manual deduplication doesn't scale. It never did.

The system automatically identifies and clusters identical properties using NLP for name similarity, computer vision for image matching, geo-spatial clustering, and text similarity for descriptions. Each match comes with a confidence score. Human review is optional — not mandatory.

Each supplier arrives with its own amenities taxonomy, property types, and field naming conventions. Your team spends weeks manually mapping data that changes every time a supplier updates their schema. It's slow, expensive, error-prone, and impossible to scale — and it gets worse with every new supplier you add.

The engine ingests raw supplier objects regardless of schema. It automatically maps amenities to a unified taxonomy, classifies property types, normalises object structures, and flags duplicates — all without manual intervention. New suppliers onboard in a fraction of the time.

Every new supplier means weeks of studying API documentation, reverse-engineering inconsistent schemas, building custom mappings, and running QA cycles. Your engineering team becomes the bottleneck to commercial growth. The more suppliers your BD team wins, the more backed up your backlog gets.

The agent reads and understands API documentation in any format — Swagger, PDF, HTML. It analyses endpoints and data models, suggests field mappings, generates test requests, surfaces edge cases, and detects integration issues before they reach production. Your engineers validate; the agent does the heavy lifting.

API errors, missing availability, booking amendments, incorrect rates — your support team handles thousands of tickets that follow the same patterns. Each one takes time to investigate, diagnose, and resolve. Multiply that across a large supplier network and the support operation grows linearly with your business — and never gets cheaper.

The agent understands logs, API errors, and client context — identifying what went wrong, whether the issue originated with the supplier or the platform, and recommending the right resolution. It integrates directly with Zendesk, Intercom, and Jira, handling the repetitive majority autonomously so your team focuses only on cases that genuinely need them.