T7 builds code-review copilots that read the whole change set with repo context — architecture, conventions, prior bugs, tests — not just the diff.
Senior engineers spend hours a week on repetitive code review; junior PRs sit in queues for days.
Off-the-shelf tools comment on style, not substance. Real review needs repo context most tools don't have.
The copilot indexes the monorepo — architecture, conventions, tests, incident history — and reviews each PR against that context.
Comments target correctness, security, performance and testability with citations to related code.
Every suggestion is a suggestion; humans still approve and merge.
Monorepo indexed with symbol graph, ownership, prior PR feedback and incident post-mortems.
On PR open, copilot posts comments per file with severity and reasoning.
Author / reviewer reactions feed back — suggestions your team ignores get quieter; the ones you accept get louder.
No — deploy inside your VPC. Model calls can be scoped through Azure/AWS private endpoints.
Confidence gating, per-file severity thresholds and feedback-driven learning per team.
Yes — feed the copilot your style guide, ADRs and architectural decisions; it grounds review in them.
Industries where this workflow ships, the insights behind it, and the tradeoffs to weigh.
AI-powered learning platforms
AI for firms that sell expertise
AI copilots for law firms and in-house teams
How large engineering orgs are moving from IDE autocomplete to codebase-wide agentic development — and the guardrails that make it safe.
The threat model, data-flow controls and audit trails your CISO will demand before an AI copilot touches production data — and how to build them in.
Exposing enterprise systems to AI: REST vs MCP
How OpenAI's GPT models compare to Anthropic's Claude for enterprise workloads
Talk to a senior AI consultant from T7 about your industry, workflow, or product idea. Free, no commitment — reply within one business day.