Tools built for the end user
We build software where we see a gap: tools that integrate into existing stacks, don't require vendor lock-in, and are designed to be owned by the engineers using them.
4 published, from live tools to research in progress
| Project | What it is | Status |
|---|---|---|
| generate-data.com | Synthetic test data for systems that fail on tidy demos. | Live |
| entitymodel.com | Free, open-source data modeling and ER diagrams in your browser. | Live |
| skailr.io | Your work, in a folder you own. | Live |
| olios.io | Agentic financial intelligence. | In development |
generate-data.com
Synthetic test data for systems that fail on tidy demos.
generate-data.com is a synthetic test data platform for teams evaluating matching, scoring, and other intelligent systems. Unlike generic faker tools, you can configure quality characteristics such as exact duplicates, fuzzy duplicates, and realistic field-level variation, so test datasets look like the messy production inputs your models and pipelines will see. That makes regression suites and QA datasets honest about failure modes tidy unique rows would hide.
- The problem it solves
Most synthetic generators invent clean, unique rows. Matching systems, quality scorers, and evaluation harnesses fail on duplicates, typos, and field-level drift. If your tests never inject that mess, they pass while production still breaks. generate-data treats input quality as configuration, not an afterthought script.
- Key capabilities
- Schema modeling with configurable field types
- Adjustable data quality levels per field
- Exact and fuzzy duplicate injection with Master ID and Duplicate Type columns for record-linkage testing
- Export to CSV, JSON, XML, Parquet, JSONL, and HuggingFace dataset formats
- API access for programmatic data generation, with API keys
- AI-assisted schema generation, up to 1,000 AI-generated rows per batch (Premium)
- Catalog of free pre-built datasets
- Free and Premium tiers
- Who it's for
Engineers and researchers testing matching and quality systems. QA teams building regression datasets for intelligent pipelines.
entitymodel.com
Free, open-source data modeling and ER diagrams in your browser.
EntityModel.com is a free, MIT-licensed data modeling tool that runs in the browser. You draw entity-relationship diagrams in crow's foot notation, then generate SQL DDL for PostgreSQL, MySQL, Snowflake, and Databricks, along with DBML, Mermaid, dbt, and PDF design documents from the same model. It needs no account and no install, works offline, and keeps your models in your browser unless you export or share them.
- The problem it solves
Desktop modeling suites are expensive and heavy, and lightweight diagram tools stop at the picture. EntityModel keeps conceptual, logical, and physical models in one file, generates the DDL and migrations, and saves models as plain JSON you can review in a pull request.
- Key capabilities
- Conceptual, logical, and physical models as tabs, with derive, compare, and generated migration SQL
- Import from SQL DDL or DBML; reverse-engineer from Databricks, Snowflake, PostgreSQL, and MySQL
- Export SQL DDL, DBML, Mermaid, dbt schema.yml, ODCS data contracts, JSON Schema, Avro, PDF, PNG, and SVG
- Reference data, glossary, naming standards, and live validation in an Issues panel
- Models saved as plain JSON for Git and pull-request review
- CLI and MCP server so AI agents such as Claude can design and edit models
- Tested on models with 2,000 and 5,000 tables; installable as an offline app
- Free with no usage limits; shared workspaces with a free account
- Who it's for
Data architects and modelers who want a lighter alternative to desktop suites, analytics and data engineers who need logical models, DDL, and dbt tests from one place, and software engineers documenting a database.
skailr.io
Your work, in a folder you own.
Skailr keeps tasks, projects, meeting notes, and drafts as plain Markdown files in a folder on your own computer, and gives you one board to run your week from. The folder carries its own instructions, so Claude and ChatGPT can read it and keep it current, and Skailr shows you everything they change. It runs in the browser with no account and no install. Your files never leave your machine, and personal use is free.
- The problem it solves
Work gets split across a task app, a notes app, and a chat window, and none of them is somewhere an AI assistant can safely read from or write back to. Skailr turns one folder of plain files into the shared record for you and your AI. You can open every file, version it, and keep it after you stop using the app.
- Key capabilities
- Board views (Now, Next, Waiting, Later) over plain TASKS.md and PROJECTS.md files
- Projects, milestones, and deliverables whose health updates itself
- Meeting notes and docs stored next to the work they describe; turn a line into a linked task
- Do it, Process, Tidy, and Ask hand a task to Claude Cowork, Claude Code, ChatGPT, or Codex in the same folder
- Workspaces ship CLAUDE.md, AGENTS.md, and skills so AI tools already know how the folder works
- Skailr for desktop (Mac, Windows, Linux) runs Claude work with the Claude Code already on your computer, on your own Claude plan
- Free for personal use; Skailr Teams (sharing, sync, hosted Claude) is on a waitlist
- Who it's for
People who already use Claude or ChatGPT for their work and want tasks, projects, and notes in files they own, with an AI that drafts while they decide.
olios.io
Agentic financial intelligence.
Olios is an agentic, ontology-based financial intelligence platform in development. The first planned products are ontology-linked intelligence subscriptions: structured, queryable insight feeds meant to plug into research and decision workflows. Portfolio management and trading strategies are planned to build on the same foundation. Pre-launch, public waitlist.
- Key capabilities
- Ontology-based financial intelligence and insight feeds
- Structured, queryable data subscription products, planned first
- Portfolio management and trading strategy execution on the same foundation, planned next
- Who it's for
Research and investment teams who want structured, queryable financial intelligence they can subscribe to and build on.
Tools that belong in the stack, not on top of it. Every project starts from the same principle: it should integrate into the systems teams already run rather than forcing a rebuild around a black box. We expose the logic so teams can own it, we design for operators first, and we ship research tools and software meant to be adopted like any other piece of stack they control.