The SQL IDE built for teams that cannot trust the cloud with their data.
Local-first · Privacy-native · Audit-ready
Modern SQL tools are adding AI. Every major IDE now connects to GPT-4 or Claude via the cloud. For most developers, this is a productivity upgrade.
For developers in healthcare, finance, government, and legal — it is unusable. Sending a database schema or query to an external API is a compliance violation. So they get nothing.
Every NL2SQL tool on the market sends your schema to an external server. In regulated industries, that's a hard no.
Who asked what? What SQL ran? What changed? No tool tracks the full chain from natural language to database execution.
Every session starts from zero. Complex analytical workflows require rebuilding context every time.
Existing tools added AI as a layer on top of cloud infrastructure. Privacy can't be retrofitted onto that architecture — it has to be the foundation.
Academic datasets — patient records, financial data, sensitive survey responses — can't be sent to OpenAI. Faculty revert to manual SQL writing despite AI being available.
Firsthand confirmation from a Databricks employee: cloud LLM restrictions are standard practice in regulated enterprise environments, domain-dependent but common.
The most popular open-source SQL IDE has not shipped local inference. Not because they can't — because their roadmap is cloud-first. This is a deliberate architectural gap.
Incumbents cannot retrofit local inference onto cloud-first systems without dismantling their core business model. This is a window that won't stay open.
Truncate IDE is a desktop SQL tool built in Rust + Tauri, with a locally-running language model that understands your schema and generates validated SQL — entirely offline.
Quantized Arctic model via llama.cpp runs on-device. You describe what you want in plain English. SQL is generated and schema-validated before execution.
Every natural language query, generated SQL, and execution result is logged with timestamp and user. Full audit trail for compliance review.
Context is saved across sessions. Complex analytical workflows don't restart from zero every time you close the tool.
Merge and standardize multiple datasets into a canonical schema with AI guidance — inside the IDE, without exporting data.
Not a plugin. Built from the ground up for local-first AI. The privacy guarantee is architectural, not a setting you can accidentally turn off.
Rust + Tauri for the desktop shell. React for UI. llama.cpp for local inference. Arctic model (quantized) for SQL generation. All open-source dependencies.
Schema, queries, and data never leave the machine. Verifiable at the network level — no hidden telemetry, no API calls to external AI services.
Not "developers." A specific person with a specific pain, in a specific context.
Healthcare, finance, legal, government. They write SQL daily. Their companies have blocked cloud AI tools. They are actively looking for alternatives and have budget.
Reachable via: dev.to, Hacker News, LinkedIn data communities, compliance-focused Slack groups
University faculty and PhD researchers working with IRB-restricted data (patient records, survey data, financial records). Cannot use SaaS tools. Currently using manual SQL.
Reachable via: university data science departments, research computing mailing lists
Developers who philosophically oppose sending data to cloud AI, regardless of compliance requirement. Smaller segment, but early adopters who will promote it.
Reachable via: open-source communities, privacy forums, HN
llama.cpp and quantized models now run SQL-capable AI on consumer hardware. A MacBook M2 can run a competitive NL2SQL model. This was not true 18 months ago.
EU AI Act, HIPAA enforcement actions on AI tools, and SEC data governance requirements are creating new compliance urgency. IT departments are blocking cloud AI tools en masse.
Developers now expect AI assistance in every tool. Those who can't use it due to compliance are actively seeking alternatives — the demand is already there, unsatisfied.
DBeaver recently raised funding. JetBrains is investing in AI. If either ships a credible local inference plugin in the next 18 months, the architectural moat erodes. Speed matters.
Local inference being viable is the unlock. Everything else — compliance pressure, developer expectations — has been building for years. The infrastructure just caught up.
If Truncate wins the privacy-first SQL IDE category, the next move is the provenance graph as a compliance API — the layer regulated teams build their data governance workflows on.
Phase 1 is a product. Phase 2 is infrastructure. Phase 3 is the standard that compliance teams reference when they ask "who queried what, when, and how was that SQL generated."
Three research buckets underpin the market claims in this pitch. If asked, here is exactly where each number comes from.
| Bucket | Stat | Source + Link | What It Measures |
|---|---|---|---|
| GenAI Bans | 27% |
Cisco 2024 Data Privacy Benchmark Study 2,600 security & privacy professionals · 12 countries · Jan 25, 2024 ↗️ Cisco Investor Press Release ↗️ Full PDF Report |
27% of enterprises banned GenAI use at least temporarily due to data privacy and security concerns. |
| Shadow Risk | 48% |
Cisco 2024 Data Privacy Benchmark Study Same study — same date · Same link above ↗️ Cisco Investor Press Release |
48% of employees admit entering non-public company information into GenAI tools — even where restrictions exist. The ban doesn't stop behaviour. |
| App Blocking | 75% |
Netskope Threat Labs — Cloud & Threat Report: AI Apps in the Enterprise July 17, 2024 · live enterprise traffic data · global dataset ↗️ Netskope Press Release ↗️ Full Report |
75% of enterprises completely block at least one GenAI app. Also: more than a third of sensitive data shared with GenAI apps is regulated data organisations have a legal duty to protect. |
| DBeaver Users | 7M+ |
DBeaver official website Publicly stated · verifiable directly ↗️ dbeaver.io |
DBeaver's own stated user count. No local AI option exists in their product today. Verifiable in under 30 seconds. |
| Industry Signal | Primary |
Databricks Professional Direct LinkedIn conversation · Feb 2025 |
Firsthand confirmation that regulated enterprises routinely restrict or prohibit cloud-based LLM use for internal data workloads. Domain-dependent but standard practice in healthcare, finance, and government. |
Every stat above links to a named, dated, publicly accessible report. The 63% figure that appeared in an earlier draft was removed — it came from a consumer survey, not an enterprise benchmark. All market size estimates were also removed as they could not be traced to a specific verifiable report. Every number here can be looked up in under 60 seconds.
The SQL IDE for every developer who has been told: "You can't use AI here."
Built on Rust · Tauri · React · llama.cpp · Arctic model