Private AI for Development Teams
Your team's AI, running local
We set up a private AI platform for your developers — local models and a RAG server trained on your own codebase, wired into every engineer's IDE. Faster delivery, tailored answers, and your code never leaves your network.
On-prem or air-gapped. No per-token bills. No code leaving your walls.
Runs Local
Open models on your own hardware — private by default, even fully air-gapped
Knows Your Code
A RAG server indexed on your repos, docs, and standards — answers in your context
In Every IDE
Cursor, VS Code, and JetBrains — every developer connected to the same brain
Who We Help
Teams that can't — or won't — send code to the cloud
Most of our clients come to us for one of two reasons. Both end the same way — every developer working faster, on AI that's fully under your control.
Privacy & Compliance Comes First
You work in finance, healthcare, legal, defense, or any field where source code and data simply cannot go to a third-party API. We bring modern AI to your developers without a single byte leaving your network.
- On-prem or fully air-gapped deployment — no external calls
- Data governance, access controls, and audit trails built in
- IP protection without slowing your engineers down
Best for: regulated industries and IP-sensitive teams
One Brain for Every Developer
Your engineers each use different AI tools with no shared context, unpredictable per-seat bills, and answers that don't know your codebase. We give the whole team one private AI that actually understands how you build.
- A shared RAG server that knows your repos, docs, and conventions
- Connected from Cursor, VS Code, and JetBrains out of the box
- Flat, predictable cost — no per-token or per-seat surprises
Best for: engineering orgs standardizing AI across every team
What We Do
Everything your private AI platform needs
From the inference server to the IDE plugin on every developer's machine — we build, connect, and keep improving the whole stack.
Local Inference Server
We deploy a fast, private inference server on your hardware — vLLM, Ollama, or TGI — with the right open model, GPU sizing, and throughput for your whole team.
Private Codebase RAG
A retrieval server indexed on your repos, docs, ADRs, and standards — so the model answers with your context, not the public internet's. Tuned for your stack.
IDE Integration
Every developer connected from Cursor, VS Code (Continue), and JetBrains — code completion, chat, and codebase Q&A, all pointed at your private server.
Model Selection & Tailoring
We pick and tune the right open models for your languages and domain — Llama, Qwen, DeepSeek, Mistral — with fine-tuning and prompt strategies that fit how you build.
Security, Privacy & Compliance
Air-gapped deployments, access controls, audit logging, and data governance that satisfy security reviews in finance, healthcare, legal, and defense.
Continuous Improvement
A managed loop that keeps your platform sharp — re-indexing new code, upgrading models, adding data sources, and tuning against evals so velocity keeps climbing.
How We Work
Stand it up fast — then keep making it faster
We get a private platform live quickly, then run a continuous loop that keeps accelerating your team. No guesswork, no black boxes.
Assess
A focused technical conversation about your stack, security constraints, hardware, and team workflows. We map where AI will save the most time — and what "private" has to mean for you.
Stand Up
We deploy a local inference server and RAG server on your hardware — on-prem or air-gapped. No code leaves your network, from day one.
Tailor
We index your codebase, docs, and standards, then tune models and retrieval to your domain so the answers feel like they came from your most senior engineer.
Connect & Measure
Every developer is wired in through Cursor, VS Code, and JetBrains. We baseline real metrics — acceptance rate, cycle time, review speed — so impact is visible, not vibes.
Improve — continuously
ongoingAs an optional managed engagement, we keep the platform sharp — re-indexing new code, upgrading to better models as they ship, adding data sources, and tuning retrieval against evals. Your team's AI gets smarter every week, not stale.
Questions
Frequently asked
Does any of our code or data leave our network?
No. That is the entire point. The inference server and RAG server run on your hardware — on-prem or fully air-gapped. There are no third-party API calls and no telemetry. Your source code and data never leave your walls.
What hardware do we actually need?
Less than most teams expect. A single modern GPU server can comfortably serve a team with a strong open model, and quantized models run on surprisingly modest hardware. During the discovery call we size everything to your team and budget — and we can start on hardware you already have.
Which models and IDEs do you support?
We run leading open models such as Llama, Qwen, DeepSeek, and Mistral, and pick the best fit for your languages and domain. Developers connect from Cursor, VS Code (via Continue), and JetBrains IDEs — completion, chat, and codebase Q&A, all pointed at your private server.
How is this different from Copilot or cloud AI tools?
Cloud tools send your code to someone else and bill per seat or per token. Ours runs locally, knows your codebase through a private RAG index, and costs are flat and predictable. You get tailored, context-aware answers without the privacy and compliance headaches.
Can it run fully air-gapped for compliance?
Yes. We regularly deploy into air-gapped and regulated environments (finance, healthcare, legal, defense) with access controls, audit logging, and data governance designed to pass security review.
How do you keep the platform improving over time?
Through our optional Platform Acceleration engagement: we re-index new code, upgrade to better models as they ship, add data sources, and tune retrieval against evals — so your team’s AI keeps getting sharper instead of going stale.
How do we get started?
Book a discovery call. It is a focused technical conversation with no sales pitch and no commitment. We will discuss your stack, your constraints, and whether a private AI platform is the right move for your team.
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Ready to build on solid ground?
Let's talk about your AI platform. No pitch decks — just a technical conversation about what your team needs.