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Tilores Studio desktop entity resolution
Entity resolution on your machine. No cloud, no signup.
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.
I'm Steven, one of the co-founders of Tilores.
Entity resolution (working out that "J. Smith", "John Smith" and "Jon Smyth" are the same person across messy datasets) has always been enterprise software. Book a demo, talk to sales, contract, months of procurement before you ever see it run on your own data. Which is backwards, because the teams who need it most (banks, compliance, fraud, healthcare) are exactly the ones who can't hand their data to a stranger's cloud just to evaluate a tool.
So we built Tilores Studio. It's the same real-time matching engine that runs our cloud product, packaged to run entirely on your own machine. Download it, drop in your own CSV, and watch it resolve duplicates in real time. Nothing leaves your laptop. It's free up to 100,000 records, enough to test it properly on real data rather than a toy sample.
It ships with two pre-configured use cases (people and companies), sample datasets, a golden-record view, an entity graph, and the same GraphQL API as our cloud product, so if you outgrow it there's no migration.
New in this build: Studio runs a local MCP server. So AI assistants like Claude Code and Codex can now drive it directly, searching your entities, exploring matches, importing and exporting data, and steering the app, all against your local data with nothing leaving your machine. If you're building agentic workflows over messy data, this gives your assistant a resolved entity layer it can actually query.
We built this because we were tired of telling curious engineers "book a demo" when they just wanted to try the thing. Now you can.
Happy to get into anything: the matching rules, the MCP integration, where it breaks, what's on the roadmap. I'll be here all day.
I’ve tested Tilores Studio and I was impressed by its matching accuracy. Running everything locally is also a huge advantage for organizations working with sensitive data. Congratulations to the entire team on the launch! 👏🏽👏🏽👏🏽
Local-only entity resolution is a smart angle - a lot of fraud/compliance teams can't send customer records to a cloud API no matter how good the matching is. How does the desktop version handle fuzzy matches (typos, name variants, merged addresses) without a model call, is it running everything through local heuristics/embeddings?
Honestly super useful, I hooked it up to a messy dataset and it caught duplicates our internal script kept missing. The real-time part actually feels real too, like queries came back fast even on bigger chunks of data.
honestly the concept is solid but it would help a ton if there was a visual diff or audit trail showing how two records were matched, like the reasoning behind the merge. right now it's kind of a black box which makes compliance teams nervous about trusting the resolved output without seeing the why.
A small thing that would honestly help a lot - a visual diff or explainability view for the matched records. Like, when Tilores merges two profiles, show me exactly which fields matched, which ones conflicted, and the confidence score per field. Right now it's basically a black box and I have to take the resolution on faith. Would make debugging way easier when something looks off.
@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?
Super useful this. I work with deploying ai agents across sales and marketing teams. Making them effective often means harmonising salesforce data on the sales side and hubspot and other systems that marketing uses. A lot of problems I didn’t think were problems and it got me into the entity resolution rabbit hole.
Just for fun. 😹
I like that you're exposing the entity resolution layer through MCP. Agents are only as reliable as the data they can access, and resolving duplicate records before they reason over them seems like a solid architectural approach.
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!
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?
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.
the way the site explains fuzzy matching logic in plain language is genuinely nice, like you can actually tell they obsessed over the small UX bits instead of just throwing jargon at you
honestly the speed is what got me, like linking messy records across a few csvs in basically seconds. pretty handy if your stack is full of half-synced data.
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.
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?
The docs page for the API is genuinely well done. Real request examples, clear error responses, and the sandbox lets you throw messy data at it without signing up first.
About Tilores Studio desktop entity resolution on Product Hunt
“Entity resolution on your machine. No cloud, no signup.”
Tilores Studio desktop entity resolution was submitted on Product Hunt and earned 38 upvotes and 39 comments, placing #14 on the daily leaderboard. 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.
Tilores Studio desktop entity resolution was featured in Data & Analytics (5.7k followers), Database (2.2k followers) and Data Science (3.9k followers) on Product Hunt. Together, these topics include over 5.5k products, making this a competitive space to launch in.
Who hunted Tilores Studio desktop entity resolution?
Tilores Studio desktop entity resolution was hunted by Steven Renwick. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Steven Renwick