Coding Assistant
Completion, review, and refactoring on a model that signed your NDA by physically living in your rack.
Your codebase is IP, and for many of our clients it is export-controlled IP. A self-hosted coding assistant gives developers the completion and review experience they want, from a model running on your GPUs, with your code never leaving your network, not even as telemetry.
Why engineering leaders are moving assistants inside
Cloud coding assistants send code context to external services on every keystroke, and their telemetry stories change with every terms update. For defense programs, medical device firmware, and anyone whose codebase embodies regulated IP, that is not a paperwork problem; it is disqualifying. The alternative used to be 'no assistant.' It isn't anymore.
Code-specialized open-weight models now deliver production-grade completion and strong review capability at sizes that serve an engineering org from modest GPU hardware. Self-hosting stopped being a compromise roughly when those models crossed the utility threshold; now it is simply the compliant configuration.
What the deployment includes
The developer experience must be indistinguishable from the cloud tools or adoption fails; that is our engineering bar.
- Inline completion at interactive latency, served from your GPUs
- IDE integration for VS Code and JetBrains via standard extension protocols
- Chat over your repositories: ask about the codebase, generate against your conventions
- Review agents: PR summarization, diff review, and standards checking wired to your git hosting
- Optional fine-tuning on your code so completions follow your idioms, not the internet's
Tuned to your codebase, governed by your policy
A LoRA pass over your repositories teaches the model your naming, your internal frameworks, and your architectural idioms, which is where the 'it suggests exactly what I would have typed' moments come from. Governance rides alongside: repository-level access scopes, prompt and completion logging to your SIEM, and the ability to exclude sensitive paths from context entirely.
Air-gap configuration
For cleared development environments the whole stack runs inside the enclave: model, index, and IDE extensions installed from your offline registry. Updates follow the signed sneakernet process we run for all air-gapped deployments. Developers inside the boundary get the same assistant as everyone else, which for most cleared teams is the first time that has been true of any modern tool.
Straight answers
How good are self-hosted models compared to cloud Copilots?
For completion and codebase-grounded chat, current code-specialized open models are firmly in the productive range, and fine-tuning on your code closes most of the remaining gap where it matters (your internal frameworks, which cloud models have never seen). For frontier-hard algorithmic reasoning, hosted flagships keep an edge. Most daily development is not that.
What GPU footprint does this need?
A code-tuned model in the 7-15B class, quantized, serves interactive completion for a substantial team from one or two 24-48GB GPUs. Larger review/chat models can share the same node or a second one. We size from your developer count and usage pattern.
Does it work with our existing IDE setup?
Yes; we integrate through standard completion and chat protocols that VS Code and JetBrains extensions already speak, so the rollout is an extension install pointing at your gateway, not a new toolchain.
Can we exclude parts of the codebase?
Yes, at the index and context level: excluded paths never enter retrieval or completion context, and the exclusion is enforced server-side where your admins control it, not by an honor-system client setting.
Your developers want this. Your security team will approve this.
We'll demo a self-hosted assistant on a representative codebase and scope the deployment for your team size.
Scope a coding assistant deploymentFree AI workshops available. A founder reads every message.