Tilores entity resolution
One trusted view of every customer, company, transaction
336 followers
One trusted view of every customer, company, transaction
336 followers
Tilores is real-time entity resolution: it links the duplicate, conflicting records scattered across your systems into one resolved record — so your fraud, compliance, Customer 360, and AI teams all work from the same truth.
This is the 2nd launch from Tilores entity resolution. View more
Tilores Studio desktop entity resolution
Launching today
Entity resolution has always been enterprise software: book a demo, sign a big contract before you see it run on your data. Tilores Studio changes that. The same real-time matching engine behind our cloud product, running entirely on your machine. Load your CSV, resolve duplicates live, nothing leaves your laptop. Now a local MCP server lets Claude Code, Codex and other AI assistants search, import and steer Studio against your data. Free up to 100k records. macOS, Windows, Linux.
Free
Launch Team / Built With
Tilores entity resolution
@major_grooves congrats Steven and team!
Entity resolution usually loses buyers at the data-sharing step, so running it fully local removes the biggest procurement blocker for regulated teams. The open question is match accuracy without cloud-scale reference data. How are you handling fuzzy matches and dedup thresholds on a single machine? That determines whether this replaces a pipeline or just supplements one.
Tilores entity resolution
@shivangit26 Currently the Tilores Studio uses optimized fixed rule sets for the two built-in use cases (person and company matching). While they are a good start to test the general process on customer side with the least efford, we typically fine tune the matching process together with the customers needs during onboarding or evaluation periods to get the best results. The result of that is a solution that can be either hosted by the customer themself or by us and can easily be implemented in existing pipeline. I'd recommend to look at a few of the use case studies on our website that provide further details.
We're planning to also offer customization options in the Tilores Studio directly. Expect further improvements comming soon. :)
Congrats team for shipping🙌 leveraging a local mcp setup to explore and sort matches without writing massive python scripts is pure leverage. qq does the local server support simultaneous concurrent client connections if we have both cursor and claude code hitting the database at the same time?
Tilores entity resolution
@vikramp7470 yes, concurrent data access is not an issue for most of the tools the server provides. There are a few tools though that wouldn't make sense to use concurrently: namely everthing that drives the UI state (switch pages, open diagram, etc.). But everything data related is safe.
Congrats on the launch. This is so needed, I work in an environment where we cannot upload our data to any vendor.
What are my options beyond 100k records? Is there an on-prem or bring-your-own-cloud version of Tilores as well?
Tilores entity resolution
@cschagen primarily our production deployments we run on AWS, but we can also do on-prem (which would mean we can deploy on any cloud) via a container. Just need to be able to handle the Devops side of things. Let's have a chat after the launch to discuss your use case.
@stefan_berkner @major_grooves Huge congrats on launching Tilores Studio! Bringing enterprise-grade entity resolution completely local via a desktop app is a massive win for privacy-conscious teams who can't ship data to third-party clouds.
Since entity resolution can be quite heavy on system resources, what are the recommended local hardware specs when processing close to the 100k record limit? Also, how does the local engine handle memory allocation during massive deduplication tasks?
Tilores entity resolution
@habib_daigency pretty much any potato should be able to handle 100k records - challenge usually starts at 1M or more records for badly optimized algorithms. Our SaaS/on-prem versions can easily handle hundreds of million records - by default they are built on a serverless stack and as such users wouldn't have to worry about machine requirements.
One thing that would help us a lot is a no-code rules builder where we can set custom matching thresholds per attribute. Right now our team has to ping engineering every time we tweak weights for something like email vs phone similarity, which slows things down when we spot a new fraud pattern.
Tilores entity resolution
@eymenyabasoqjc For our SaaS solution we offer exactly that. A visual editor for customizing all rules. We expect to have the same functionality in the studio comming soon.
Really cool - and I an definitely see this being handy as you deal with entity resolution. A problem I've run into many times in the past - "how many different variants of JP Morgan exist" - many more than you think!
Great to see!
Tilores entity resolution
@peadar_coyle3 Glad to hear that you like it. As a matter of fact, we have quite a sophisticated and well working approach for company name matching (see Exiger case study on our website). Let us know if you're struggling next time, we might be able to help.