Catch an error late and you've paid for every token after it.
Taiso Verify checks every action before it runs. One config change puts an independent check in front of your agent: every response and tool call is verified before it executes — allowed, held for a human, or blocked. Guardrails filter words and evals watch after the fact; only a gate stops the action before it runs.
One gate, five checks, one verdict
Every action, checked before it counts
Intent, Evidence, Policy, Risk, Audit — one verdict per call, before the action runs and before the next step spends anything.
ALLOWESCALATEBLOCK
Independent by design
The AI never grades its own homework
A separate system does the checking. If Taiso is ever unreachable, your calls pass through or pause — your call. It never takes production down.
Built for the production path
No training on your traffic
Every verdict is kept on an audit ledger you can replay. Built for agents that move money, execute trades, or write to customer records.
# your existing client — one line changes - base_url = "https://api.openai.com/v1" + base_url = "https://api.taiso.ai/v1"
Speaks the OpenAI API and routes to OpenAI, Anthropic, and more behind one endpoint. Fits behind the routing you already use — no new SDK, no migration.
Try it in shadow mode · 14 days · self-serve
Day 0
Swap the base URL
Blocks nothing. No meeting to start.
Day 3
Shadow-mode report
What it would have caught — and what those runs cost you.
Day 7
Preview a gate
See what one high-risk tool would escalate.
Day 14
Review the ledger
Convert if it's worth it — cancel by deleting a key.
$0.017
Taiso, at step one — per action, measured
$0.046–$0.23
checking it yourself with a frontier model
~21%
needed a second look, on our test set
Measured on our own extraction workloads — not list price. Shadow mode shows your rate by day 3.
You can't scale AI you can't control. Taiso is built from the ground up to control it.
MIT found 95% of enterprise AI pilots fail — not because the models are weak, but because the foundation is. Agent frameworks were built to run agents, not control them, and control can't be bolted on afterward. The Taiso Agent Control Platform is the only platform built from the ground up as the control system: every step of your operation a control, every item systematically checked, not a sample — for the review hours you spend today.
Six core components · one platform
Each component is a control in its own right. Together they share one policy layer and one ledger — which is what makes this a platform, not a toolkit of add-ons.
Core component
SOPs
Your procedure enforced, not suggested — every claim passes its checks before it counts.
Core component
Agents
Runs the procedure end to end without a person driving — models, tools, and checks in sequence, every step a control.
Core component
Projects & Files
Every relevant document, findable — a searchable corpus of everything the work touches.
Core component
Structured Blocks
Every extracted field audited against its source — not trusted because the model said so.
Core component
Human review queue
Can't confirm it? A person decides — the few the machine can't confirm reach your reviewers, evidence attached.
Core component
Evidence ledger & chat storage
All items, interactions, and corrections recorded — the record your auditor and your board accept.
Together they run your procedure end to end — intake → extract → verify → route → escalate → prove — with every step on the same ledger.
Todayyou audit a sample
reviewed by a personshipped unchecked — your liability
Everything you didn't look at, you still answer for.
With Taisoevery item, checked
checked against its source, shippedthe unconfirmed few → your reviewers
on the ledger — the record your auditor accepts
Same review hours. Every item systematically checked. The machine checks everything; your people see only what it couldn't confirm.
Is this you?
Your work is a procedure
Intake, extract, check, decide, escalate, prove — today it lives in people, wikis, and brittle scripts.
Is this you?
You answer for the result
Clients, auditors, or regulators ask how you know it was right.
Is this you?
You're putting AI on regulated work
And can't ship a black box you can't prove to the people who sign off.
Common in managed review, insurance claims, freight audit, mortgage & title QC, and F&A back-office.
How you start · one closed workflow
Run one workflow on work you've already closed — so nothing in production changes. By day 14 you have your first ledger, and the list of what your sample audit missed — yours to keep.
An Agent Control Platform is the governance layer that makes AI agents reliable enough for production: it enforces policy, verifies every action against evidence, escalates uncertainty to humans, and records a defensible ledger.