01 · the mess is the reason
Tame the systems you can't rip out.
SAP, Oracle, decades of legacy. checkLLM doesn't ask you to replace them — it puts proof around the AI already touching them.
governed digital workers · for big, legacy enterprise
Prove every action before it runs.
checkLLM puts governed AI workers — Inspectors. — in front of the systems you can't replace. Start read-only with Watch., on your own stack, and expand only when you trust it.
proof, wrapped around the ai you already run.
on your stack
no egress
audited
an inspector, watching
inboundextract invoice · SAP → model
filter2 PII fields scrubbed · SSN, DOB — blocked pre-model
rebuilddates normalized to ISO · 3 rows
modelin-tenant call · no egress
output12 line items · schema-valid
provenance
ruleSOP-14 §3 · policy v7
auditsigned · 12:04 UTC · hash 9f3a…c2
scopeon your stack · read-only
governed · nothing ran · on record
why checkllm · brownfield
The mess is the reason.
woodle.cloud is greenfield — a clean workspace, "don't inherit the mess." checkLLM is the opposite: it walks into a crowded enterprise full of legacy systems and existing AI tools and governs the mess on your own infrastructure.
02 · the layer above your tools
Not one more AI tool.
It's the layer above the tools you already have — bringing rules, safety, and a clear record. It governs the AI you run; it doesn't add to the pile.
03 · the jobs the crowd can't touch
The work Copilot isn't allowed to do.
Private, regulated, high-sensitivity, agentic work — run inside your own walls, where a general chatbot is never allowed to go.
how an inspector works
Three moves, in front of every AI call.
An Inspector is a governed digital worker that sits between your systems and the model. It does three things before anything reaches — or leaves — the AI.
filters.
Blocks or scrubs.
Anything that breaks the configured compliance rules is stopped or scrubbed before it ever reaches the model.
reconstructs.
Fixes and normalizes.
Malformed or non-conforming data is repaired and normalized so it meets the rules — no silent bad input.
detects.
Flags the mismatches.
Data problems and governance mismatches in your existing systems are surfaced, logged, and put on record.
Proof, wrapped around the AI you already run.
The gold dot is the quiet mark that an action was inspected here — filtered, reconstructed, and on record.
the trust ladder
Land read-only. Earn every step.
You don't hand autonomy to an AI on day one. An Inspector starts by watching, and only moves up the ladder as it proves itself.
watch.
Read-only. Zero risk.
The Inspector only observes and reports. It touches nothing — it just shows you what it would have caught.
coach.
Drafts a human approves.
It proposes each fix and a person signs off before anything runs. Trust builds on a record you can see.
autoaction.
Runs on its own, inside your firewall.
Once trust is earned, the Inspector acts autonomously — still governed, still logged, still inside your walls.
delivery modes · by where it runs
You choose where it lives.
Same Inspector, same controls. The only question is how close to your walls it runs.
standard.
Runs in our cloud.
Fastest to start, lowest cost, least custom. The governed baseline, hosted for you.
managed.
Dedicated, isolated, run for you.
An isolated setup we operate on your behalf for a term — dedicated, without you running it.
sovereign.
Inside your own environment.
Built and run in your cloud or on-prem. Data never leaves your walls — for the most regulated, most sensitive work.
on your stack · stated honestly
Nothing leaves. Everything's on record.
checkLLM inherits the Stringify base: sovereign, private execution with a signed trail. We describe our posture in facts, not badges.
controlsSOC 2 controls implemented
standardISO 27001 aligned
datano egress
whereruns in your environment
recordsigned audit trail
Built for enterprises that can't just try an AI tool.
CISOs & architects
Who have to prove an AI is safe before it touches a regulated system.
Legacy estates
SAP, Oracle, and systems too rigid and too critical to rip out.
Regulated workflows
High-sensitivity, agentic work where "just try it" is too risky.
Teams drowning in tools
Already running AI they can't fully see, prove, or govern.
lineage · the provable standard
Part of a standard, not a one-off.
Trusted by Stringify AI.
checkLLM is built by a team trained on the first principles of Stringify's serving layer, then rebuilt on your own stack. It's a product in its own right — not a woodle deployment — and it inherits the same provable standard: sourced, governed, on record.
trusted by stringify ai
woodle.cloud.
The greenfield sibling.
The compliant AI workspace for small regulated teams — the same standard, expressed as a packaged product instead of a governed layer on your legacy stack.
Go to woodle.cloudquestions
Answers you can quote.
What is checkLLM?
checkLLM is a product for big, legacy enterprises: custom-built governed AI workers, called Inspectors, that sit in front of the AI call on your own stack. Each Inspector proves every action before it runs. checkLLM is trusted by Stringify AI; it is never described as powered by woodle.
What is an Inspector?
An Inspector is a governed digital worker that sits in front of an AI call and does three things: it filters, blocking or scrubbing anything that breaks the configured compliance rules before it reaches the model; it reconstructs, fixing and normalizing data so it meets the rules; and it detects, flagging data problems and governance mismatches in existing systems.
What are Watch, Coach, and AutoAction?
They are the trust ladder. Watch is read-only: the Inspector only observes and reports, at zero risk. Coach drafts fixes that a human approves before they run. AutoAction runs on its own once trust is earned, inside the client's firewall. Most engagements land at Watch and expand.
What delivery modes does checkLLM offer?
Three, named by where they run. Standard runs in our cloud — fastest and lowest cost. Managed is a dedicated, isolated setup we run for the client for a term. Sovereign is built and run inside the client's own environment, on their cloud or on-premises, so data never leaves their walls.
Is checkLLM certified?
checkLLM implements SOC 2 controls, is ISO 27001 aligned, and is certification-ready. It does not claim to be externally certified or audited yet. Inspectors run in your environment with no egress and leave a signed audit trail.
How is checkLLM different from woodle.cloud?
woodle.cloud is greenfield — a clean, compliant AI workspace for small regulated teams. checkLLM is brownfield — it walks into a crowded enterprise full of legacy systems and existing AI tools and governs the mess on the customer's own stack. The mess is checkLLM's reason to exist. Both inherit the same provable standard from Stringify AI.
start read-only
See an Inspector watch your stack.
Book a demo and we'll scope one Inspector — in Watch mode, on your own infrastructure, at zero risk.