Cantus Industries designs agentic architectures where the generator and the verifier are never the same agent, standards are measurable instead of aspirational, and an incomplete review can never return an approval.
Custom bots, autonomous agents, and end-to-end agentic workflows — built to order.
Every claim on this page is verifiable: our frameworks are public, tested, and documented — clone them and run the suites yourself.
Contracts, policies, releases, content — reviewed in parallel by specialist agents, with a verdict your team can gate real decisions on.
Reporting and data-to-production automation that validates at every boundary and never publishes on an incomplete picture.
The judgment calls your team makes fifty times a week, executed by agents — every outcome traceable to evidence and a named standard.
Assistants that answer only from sources you control and decline everything else. You can talk to one at the bottom of this page →
Most consultancies show you slide decks. We show you repositories — three hundred fifteen tests between them, every limitation documented in the README. And these are the examples, not the extent: Cantus Industries builds custom bots, autonomous agents, and end-to-end agentic workflows to order.
Parallel document review by six specialist agents with a fail-loud consensus ledger. If a reviewer crashes, times out, or never reports, the run is blocked and is never approved on partial evidence. 156 tests. Runs offline, on Claude, or on Gemini behind one protocol.
A scheduled pipeline from parallel ingestion agents to a gated, reversible production deploy. Refuses to publish on partial data; every write is atomic, backed up, and verified after landing. 159 tests.
A case study: one operator directing ten-plus agents across four layers and two LLM platforms, with persistent state and a human gate on every decision. The architecture the two frameworks above were extracted from.
Every engagement is fixed-scope and milestone-gated. We do not bill hourly.
We take one workflow you believe could be automated — a review process, a reporting pipeline, a repetitive decision — and put it through the same analysis our own systems use. You receive a written findings report: where agents genuinely fit, where they don't, what the architecture would look like, and what it would cost to build. Findings come with severity ratings and evidence, the same format our review council produces. If the answer is "don't build this," the report says so.
The exact figure sits inside the range by the scope of the workflow, is fixed in writing before we start, and never moves mid-engagement.
A multi-agent review panel adapted to your documents and your standards — contracts, policies, releases, content — with viewpoints you define, a fail-loud ledger, and verdicts your team can gate real decisions on. Built on our public council framework.
Scheduled, hands-off data-to-production automation with validation at every boundary, atomic deploys, backups, and rollback. Built on our public pipeline framework. Setup plus an optional maintenance retainer.
Five principles, all visible in the public code.
Hard problems get solved with versioned files on disk — ledgers, timelines, knowledge bases — not longer prompts. No agent depends on its memory of a prior session.
Producer and reviewer are separate agents, ideally on separate platforms with separate failure modes.
Numeric caps and countable rules, not adjectives. "Use sparingly" is not a standard; "no more than two per document, counted mechanically" is.
A system that cannot complete its checks blocks; it never returns a confident default. Incomplete evidence is a verdict, and that verdict is no.
Throughput comes from the agents; judgment does not. A named person owns every decision that ships.
I came to agentic AI through an organizational mandate to raise AI fluency at scale, where the measure of success was tempo — how much faster a team can move without lowering its standard for verification. That environment made two things obvious early: speed without a check is a liability, and a check that slows everything down defeats the purpose. Everything Cantus Industries builds is aimed at having both.
The portfolio is built on commercially available frontier models, on my own time, on personal projects. The code is public and tested. Read it before you call me.
Answers come only from our public materials. Anything it can't source, it declines — that's the product working.