AI orchestration for engineering teams

Reliable AI answers for real engineering work.

Undes coordinates multiple AI agents through a strict review process: hypothesis, evidence, critique, consensus and final synthesis. The result is not just a chat response, but a structured engineering artifact your team can inspect.

Start with Undes Read documentation

BYOK-ready. Built for code analysis, architecture reviews, CI 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 changed · 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  ·  / filter

Product

From AI chat to verifiable engineering process.

Fast answers are useful. Verified answers are deployable. Undes is designed for cases where the team needs a traceable result: what was checked, what was rejected, and what still remains uncertain.

Multiple agents

Several agents produce independent views before final synthesis, so one confident answer is not the whole process.

Evidence-first output

The result separates facts, assumptions, risks and open checks instead of hiding them in polished prose.

Trust gates

The pipeline tracks whether evidence was delivered, whether claims were grounded and whether the answer is patch-safe.

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.
Patch-safe / diagnostic status when the evidence is incomplete.
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 that needs evidence before approval.

Get started View examples