SkyUniTechDiscuss AtlasMatch SkyUniTech / Products / AtlasMatch
Hotel identity intelligence
Every hotel.One trusted identity.
AtlasMatch reconciles fragmented hotel catalogs across suppliers, master data, local contracts, content sources, and languages—using explainable, confidence-driven entity resolution built for travel.
IDENTITY GRAPH / ACTIVEMULTI-SOURCE
Built for
OTAs
Bed banks
Aggregators
Wholesalers
DMCs
Connectivity teams
The identity problem
One property can arrive as many different records.
Names change by language and supplier. Addresses are incomplete. Coordinates move. Brands reflag. Cities and districts are modeled differently. Provider codes never agree. AtlasMatch brings those conflicting signals together so travel platforms can recognize the same physical hotel with measurable confidence.
Duplicate resultsFragmented contentMissed comparisonManual mapping queues
Built around travel data
One identity layer for every business using hotel supply.
AtlasMatch can support a focused supplier onboarding program or become the governed mapping layer across a wider distribution ecosystem.
OTA
Online travel agencies
Cleaner multi-supplier searchUnify duplicate supplier properties before they reach results, pricing comparisons, content, and booking journeys.
Better choice without duplicate noise.BB
Bed banks & wholesalers
A distributable master catalogConnect provider codes to governed property identities and expose consistent downstream mapping across partners.
One reliable identity layer for distribution.AGG
Travel aggregators
Scalable entity resolutionIngest large catalogs, generate candidates efficiently, combine multiple evidence signals, and process mapping asynchronously.
Coverage that grows without uncontrolled manual effort.DMC
DMCs & local suppliers
Local inventory beside global supplyResolve negotiated hotels and locally maintained records against global master and supplier catalogs.
Local advantage without catalog fragmentation.CON
Connectivity teams
Faster supplier onboardingStructured imports, field normalization, resumable pipelines, match review, exports, and API-ready mapping outputs.
Shorter time from catalog delivery to usable supply.DQ
Content & data operations
Measurable mapping qualityConfidence bands, explainable evidence, benchmark sets, calibration, drift monitoring, overrides, and audit history.
Quality decisions teams can defend.Multi-strategy matching
No single field decides whether two hotels are the same.
AtlasMatch combines independent evidence signals, penalizes conflicts, and keeps the reasoning behind each candidate visible.
Identifiers
Known provider codes, master IDs, crosswalks, exact references, and previously confirmed relationships.
Names & language
Normalized names, aliases, token similarity, phonetic evidence, fuzzy comparison, transliteration, and multilingual variants.
Geography
Country, city, district, address, postal information, coordinates, distance thresholds, and geographic consistency.
Property attributes
Brand, chain, category, property type, contact details, facilities, images, and structured content evidence.
Semantic similarity
Vector-assisted and language-aware comparison helps recognize equivalent properties when text differs significantly.
Graph & consensus
Existing supplier relationships, neighboring mappings, cross-provider agreement, and graph evidence strengthen decisions.
End-to-end workflow
From raw supplier file to governed mapping output.
Candidate generation is kept efficient, scoring remains explainable, and automation is constrained by confidence and quality rules.
Ingest
Load master and supplier countries, cities, hotels, provider codes, coordinates, addresses, brands, and content.
Normalize
Standardize encodings, names, language, geography, phone, address, coordinates, and supplier-specific structures.
Generate
Build a bounded candidate set using exact keys, search indexes, geographic filters, text retrieval, vectors, and graph context.
Score
Combine evidence into calibrated confidence with reasons, component scores, conflicts, and alternative candidates.
Govern
Auto-accept high-confidence decisions, route ambiguous cases to review, and reject unsafe suggestions.
Publish
Expose approved mappings through structured exports, integrations, APIs, and connected SkyUniTech platforms.
Confidence governance
Automate the obvious. Review the ambiguous. Reject the unsafe.
Confidence bands translate matching evidence into controlled actions. Thresholds can be calibrated to the supplier, market, catalog condition, and business risk.
Strong, consistent evidence and no material conflict. Publish automatically within configured guardrails.
Present ranked candidates, evidence, conflicts, alternatives, and history for an informed decision.
Prevent weak suggestions from becoming production mappings and retain the record for improved evidence.
Every suggestion can show its work.
Candidate rank · component scores · name evidence · distance · address consistency · identifier evidence · content similarity · graph consensus · conflicts · decision history
Quality as a system
Mapping accuracy must be measured, not assumed.
Benchmarks, sampling, calibration, and drift monitoring turn entity resolution from a black box into an operational quality program.
Coverage
How much of each supplier catalog is mapped to a trusted master identity.
Precision
How reliably accepted mappings represent the same real-world property.
Review yield
How efficiently the review queue turns ambiguous candidates into governed decisions.
Confidence calibration
Whether observed correctness remains aligned with the confidence bands used for automation.
Drift
Whether new suppliers, markets, data formats, or model changes alter mapping quality over time.
Operational throughput
Import, candidate, match, review, graph, and export progress across large asynchronous workloads.
Enterprise processing
Built for catalogs that do not fit in a spreadsheet.
Large imports, mapping workloads, review queues, graph updates, and search indexes run through observable, recoverable processing pipelines.
Resumable imports
Checkpointed catalog ingestion and deterministic upserts support safe continuation after large or interrupted supplier loads.
Queue-based mapping
Dedicated import, mapping, and graph workloads process supplier catalogs asynchronously with retries and backoff.
Search acceleration
Purpose-built indexes, vector or ANN retrieval, bounded candidate generation, and geographic filters keep matching efficient.
Graph synchronization
Approved relationships can update the identity graph without blocking the core mapping workflow.
Task monitoring
Progress, throughput, failures, retries, exceptions, and worker health remain visible to operations teams.
Controlled overrides
Human decisions, locks, exclusions, remaps, and supplier-specific exceptions are preserved with audit context.
IMPORT→INDEX→CANDIDATES→MATCH→REVIEW→GRAPH→PUBLISH
Business outcomes
Clean hotel identity strengthens every downstream journey.
Search quality
Reduce duplicate properties and present supplier prices under one coherent hotel result.
Supplier coverage
Onboard more supply without multiplying catalog inconsistency and manual maintenance.
Content consistency
Bring names, location, descriptions, images, attributes, and provider relationships around a trusted property identity.
Operational control
Make mapping decisions explainable, reviewable, measurable, and reusable across teams and channels.
Connected or independent
A mapping service that fits the operating model.
Use AtlasMatch as a focused hotel identity service, connect its outputs to existing platforms, or integrate it with SUTRA so supplier onboarding, search, content, pricing comparison, and booking operate on the same governed identities.
Explore the SUTRA platformBuild a trusted hotel catalog
How much stronger could your supply become with one identity layer?
Let's define the master catalog, supplier sources, import model, confidence policy, review process, quality targets, integrations, and rollout sequence.
Start an AtlasMatch conversation