Multiple agents
Several agents produce independent views before final synthesis, so one confident answer is not the whole process.
Evidence-checked AI code before merge
Evidence-Checked AI Code
Know what is safe to merge before trusting AI-generated code, patches, refactors, or architecture decisions. Undes gives you a candidate answer plus visible evidence, files checked, assumptions, critique, unresolved risks, and a conservative trust verdict.
BYOK-ready. Built for AI code review, architecture decisions, PR risk checks and long-running verification tasks.
> refactor auth middleware to support OIDC
⏺ codex.gpt-5 implementing ✔
│ "Keeping session API; adding mutex around
│ acquire() — OIDC callbacks can race."
└─ 4 files read · 142 lines
⏺ gemini.pro 2.5 reviewing ⚠
│ "handleCallback awaits exchange() without
│ holding the lock — duplicate sessions on
│ concurrent /callback requests."
└─ flagged: token-refresh race
⏺ claude.opus 4.7 reasoning ⟳ ▰▰▰▱▱
│ "Agree with gemini. The fix is mutex on
│ session.acquire(), but we also need to █
┌─ Trust gates ─────────────────────────────┐
│ ✔ payload ✔ impact ⟳ consensus · k3 │
└───────────────────────────────────────────┘
esc cancel · ⏎ expand · / filterProduct
From confident AI output to evidence-checked engineering work.
Fast answers are useful. Evidence-checked answers are safer to review. Undes is designed for cases where you need to know what was checked, what was rejected, what remains uncertain, and whether the result is ready to merge.
Evidence-backed candidate
The result separates grounded claims, assumptions, risks and open checks instead of hiding them in polished prose.
Trust boundaries
The pipeline tracks whether evidence was delivered, whether claims were grounded and whether the answer is ready to proceed, needs review or lacks enough evidence.
Provider-aware runs
Community focuses on first-party cloud providers. Pro is the paid track for expanded LLM providers and local model servers.
History and inspection
Pro is shaped for regular professional use: run history, engineering memory, richer inspection and export surfaces.
CLI and CI friendly
Run deep analysis outside chat: from the command line, scheduled jobs, or narrowly scoped pipeline checks.
How it works
Undes makes the reasoning path visible.
One model can be confident and wrong. Undes adds process discipline around AI work by forcing hypothesis review, contradiction checks and explicit confidence boundaries.
Scope the task
Start from a concrete engineering question: a risky change, bug, subsystem or review target.
Prepare context
Undes builds a bounded repository context and keeps track of which material must reach later phases.
Generate and critique
Agents propose, challenge, revise and compare explanations instead of letting the first answer win.
Expand evidence
The pipeline can fetch additional material when the answer depends on missing files, symbols or anchors.
Stress-test the result
Dedicated review phases look for unsupported claims, missing implementation paths and unsafe assumptions.
Emit artifacts
The final answer is packaged with warnings, open checks and machine-readable metadata where available.
Technical capabilities
- Multi-agent proposal, critique, consensus and final synthesis.
- Evidence anchors for file, range, symbol and basename-level references.
- Trust verdicts when the evidence is complete, partial or insufficient.
- Payload preservation checks to reduce context loss across phases.
- Pro provider expansion path for OpenRouter, NVIDIA NIM, generic OpenAI-compatible endpoints and local model servers.
Example artifact structure
Decision
Recommended fix or architectural conclusion.
Evidence used
Files, traces, logs, snippets and facts that support the decision.
Rejected hypotheses
Alternative explanations that were considered and ruled out.
Open risks
What was not checked, what remains uncertain, and what should be tested before merge.
Editions
Clear boundaries between public evaluation and paid professional use.
Community shows the core generate-and-verify workflow. Pro is where regular professional usage, engineering memory and expanded provider support belong. Organization usage is discussed directly.
Community
Public CLI for local evaluation with your own OpenAI, Anthropic or Google provider keys.
Best for first tests and understanding the workflow.Pro
Licensed package for regular individual use, richer inspection, run history and paid provider expansion.
Includes the track for OpenAI-compatible endpoints and local model servers as they ship.Team / Enterprise
Direct discussion only. No public package, install path or committed feature scope yet.
Use this path to discuss organization requirements before assuming a rollout model.Why Undes
Built for tasks where a polished answer is not enough.
✓
For code and architecture reviews
Ask Undes to analyze a repository, compare implementation options, or validate a risky change.
✓
For long-running verification
Some runs can take longer than a normal chat response. That is intentional: the goal is depth, not instant phrasing.
✓
For teams that need trust signals
The output is structured so engineers can review the reasoning before using it in a pull request or delivery pipeline.
Use cases
Use Undes where engineering judgment matters.
Solo development
Deep second opinion before implementing or merging complex changes.
Team review
Shared artifact for discussing architecture, bugs, risks and trade-offs.
CI/CD checks
Scheduled or pipeline-based AI review for sensitive parts of the codebase.
Organization evaluation
Discuss broader usage directly before assuming a team or enterprise rollout model.
Try Undes on a real engineering question.
Start with a small but meaningful task: a bug investigation, an architecture decision, or a pull request where you need a trust verdict before approval.