Checked before it is merged
Deterministic checks run first. Then a fresh verifier that has not seen the work. Then an independent reviewer whose job is to argue against it. Only then does it reach a merge queue.
Private AI for software teams
Agents your reviewers can trust
Propose the change, run the checks, and send an independent reviewer at it before anything reaches your branch.
What changes
One key, every model, a budget per team
Claude, GPT, Gemini, Grok, Kimi and GLM behind one gateway key. Set which models a person may reach and what their team may spend, and read the usage back per key.
Speed and cost you can look up
Median time to first token and whole-turn tokens per second per model, measured on our own production traffic and refreshed weekly, next to the vendor's own list price.
What a Tuesday looks like
- Turn a bug report into a failing test, then make the test pass, with the diff and the test run attached to the proposal.
- Take a dependency upgrade across twelve services and report which three actually broke.
- Answer "why is this endpoint slow" by reading the code, the logs and the last six merges, and showing the evidence.
- Route a trivial rename to a cheap fast model and a schema migration to a frontier one, without anyone choosing by hand.
How Borg serves it
- Borg Gateway
- One key per team with permissions, rate limits and budgets, on OpenAI-compatible and Anthropic-compatible endpoints.
- Borg Fusion
- Picks a model and an effort level per step, so routine edits do not get billed at frontier rates.
- Missions with an approval gate
- Work arrives proposed, with its evidence, and waits for a person. The record of who approved it survives the merge.
- The review chain
- Deterministic checks, a fresh verifier, an independent adversarial reviewer, then a merge queue. This runs on our own engineering work today; running it against your repositories is scoped per deployment.
What we can prove, and where it stops
The same two columns as the hub, narrowed to this one business.
Running today
- The review chain is real and it catches things: a wrong evidence filename, an edited existing test, and a red suite presented as green.
- A hard-graded coding benchmark, run by us, published per model.
- Speed per model measured on our own gateway rather than quoted from a vendor.
Not there yet
- No external customer and no named logo.
- No before-and-after cycle-time figure from a customer, because there is no customer to measure.
- Running the review chain on your repositories has not been done yet and is scoped per deployment.
Next step
Start on your own, or ask us the questions this page did not answer.