Tilores

Overview

Tilores is a developer-oriented platform for resolving records that refer to the same person or company across systems. It is best suited to teams that need unified profiles in live applications, rather than a one-off cleanup tool. Its combination of record preparation, explainable matching and real-time GraphQL access is compelling, though production requires custom pricing and deployment decisions.

Key features

Tilores prepares data before matching by mapping, normalizing and enriching fields such as names, phone numbers and addresses. It then combines deterministic rules with fuzzy matching, and provides explainable links, a quality score and an audit trail. That makes it a stronger fit for workflows where teams need both entity matches and a way to understand how records were linked.

The real-time GraphQL API supports entity retrieval, ranked fuzzy searches and aggregation of source records. Automatic merging and duplicate prevention support ongoing resolution rather than just periodic cleanup; the merge review workflow is automatic, so teams needing a manual approval step should look elsewhere.

Prebuilt connectors cover Salesforce, HubSpot and Snowflake, with webhooks and event streaming also available. IdentityRAG connects Tilores entity resolution to LangChain and Amazon Bedrock, and is described as working with any LLM through LangChain. Use cases include fraud, compliance, Customer 360 and AI.

Deployment can be managed on AWS, run in a customer's AWS account or another cloud, or installed on-premise. The pricing page describes managed AWS infrastructure as SOC 2 certified and includes GDPR-compliant data processing. Production quotes can scope support from standard technical support to a dedicated customer success engineer.

Pricing

Tilores uses a freemium model, with local evaluation available at no cost and production pricing by custom quote. The free Tilores Studio Local Mode plan costs nothing and supports person and company matching for up to 100,000 records per local deployment. It needs no account or licence key, making it a practical way to evaluate the workflow on a desktop. It is capped at that local deployment limit; connecting Studio to Tilores Cloud requires a cloud plan.

Tilores Production has custom pricing based on data volume, deployment model, SLA and compliance needs, and support requirements. It is priced per Unified Customer Record (UCR), not per raw record or seat, and includes the full platform across deployment models. This structure makes sense for teams that need production deployment choices and scoped support, but the custom quote means costs depend on the intended scale and service requirements. There is no free trial.

Platforms

The platform supports API, Linux, macOS, self-hosted, web and Windows environments. Tilores Studio runs and stores data locally, with data remaining on the user's computer. Its desktop builds support macOS Apple Silicon, Windows x64 and Linux x86_64; the macOS build is limited to Apple Silicon.

Who it's for

Tilores is aimed at developers building identity resolution into products and operational systems. It is particularly relevant when fraud, compliance, Customer 360 or AI workflows need unified entity profiles and real-time access. Teams seeking desktop-only data preparation can start with Studio Local Mode, while production users can choose among managed cloud, their own cloud account or on-premise deployment.

Pros and cons

• Pros: Data preparation, deterministic and fuzzy matching, explainability and real-time GraphQL access form a connected resolution workflow.
• Pros: Local Studio keeps data on the computer and permits evaluation without an account or licence key, up to 100,000 records per deployment.
• Pros: Production can be deployed across managed AWS, customer cloud and on-premise options, with support scoped to requirements.
• Cons: Studio's 100,000-record local cap and cloud-plan requirement for connecting to Tilores Cloud limit what the free option can evaluate.
• Cons: Production is custom-priced, so teams cannot assess a standard public price for their expected scale.
• Cons: Automatic merge review is not suited to teams that require a manual review gate.
• Cons: The macOS Studio build supports Apple Silicon only.

Alternatives

For broader comparison, see Data Deduplication Software. Choose R instead if you want a free software environment for macOS or Windows rather than a dedicated real-time entity-resolution platform. Workbooks is worth considering when a CRM is the priority: it offers a 30-day free trial, followed by a CRM plan at $47 a month, billed on a 1-, 2- or 3-year subscription.

Plauti Deduplicate may suit Salesforce users who want a free AppExchange version and a trial or custom-priced full edition. Choose Zingg when open-source batch probabilistic matching on millions of records is the priority; its Enterprise plan adds batch and real-time capabilities. DupeCatcher is another free option for web, iOS and Android. Consider DQE One Unify if hosted curative processing in DQE's cloud is a better fit, or DataMatch Enterprise for enterprise-grade cleansing and fuzzy matching on millions of records, with built-in name verification and data standardization. Koalify is a freemium web option.

Verdict

Tilores is a strong choice for developers who need ongoing entity resolution, explainable matching and real-time access across systems, with flexibility over where production runs. Its local Studio option gives a defined, no-account evaluation path. Look elsewhere if you need publicly priced production, a manual merge-review workflow or macOS support on Intel hardware.