See it work
AI that does the work — not dashboards about it.
Here's governed AI doing real, useful work — support, revenue, operations, decisions. Each is labelled honestly by maturity. Open one and try it.
A crude-but-working POC dropped into a real customer's environment: a Python script on a support agent's laptop, calling GPT-5.4, connected to their real Freshdesk ticket queue and knowledge base, writing private triage notes back into the ticket. Real tickets, real KB, real workflow. Point it at a ticket — it triages, suggests resolution steps, and writes a private note on what to confirm with the customer.
Observed in this POC: one such task, normally ~3 hours, became a ~2-minute prompt.Freshdesk is not the destination. It is the scaffold. First we make your existing support system better; then we learn enough about the workflow that the system of record becomes less of a constraint.
Start with a private note. End with a governed operating capability.
Open a slice and drive it yourself
The same governed engine across support, revenue, operations and decisions. Each card is a working demo you click into and push on — labelled honestly by maturity, from live customer pilot to preview.
Works on any Freshdesk / ServiceNow / Zendesk queue: reads your context, suggests the fix that worked, governs the AI — and earns the right to run autonomously. Watch the wheels come off, with the brakes still on.
Works on top of HubSpot / Salesforce / Pipedrive: reads every signal, proposes the play that actually closed similar deals, governs the AI — and earns the right to act. Works with — and, in time, in place of.
Reads across usage, support, billing and product: watches every account against its own baseline and surfaces the ones quietly leaving — or quietly ready to grow — ranked by revenue. Click an account, edit the drafted play, approve & send — and watch the receipt write and the customer reply. Nothing acts without your judgement.
Run a real prompt and watch the trust signal score the behaviour and gate the action.
A manufacturing event stream, governed so downstream decisions act only on trustworthy data.
Changeover, scrap and benchmarking surfaced from the governed stream as operating value.
Reconcile handwritten dockets against invoices — approve, flag, or hold for review, each with a receipt.
Rules and evidence held under the same governed loop, auditable end to end.
Enterprise workflow surfaces — CRM, finance, procurement, legal, HR, PPM, support — run on the same governed engine we sell.
Real, useful work first — governed AI doing the job, labelled by maturity, not the first thing you're asked to buy.
The problem
The hard part isn't getting an answer. It's being allowed to act on it.
Modern AI produces good answers all day. The blocker is different: a business can't safely let AI act — send the reply, change the deal, move the load — unless it knows when to trust it, when to stop it, and what evidence exists afterwards. Without those three, AI stays a demo that no one is allowed to switch on.
When to trust it
You need a verdict at the moment the AI acts — can this answer be trusted, right now, before the workflow uses it? Governance documents and generic evals don't tell you that about a live invocation.
When to stop it
When a model deviates, something has to intervene before the action lands. Observability records what happened — but only after the workflow already acted on it.
What evidence exists after
Every action an AI takes has to leave a record you can stand behind — who acted, on what evidence, under whose authority. Without it, no risk owner signs off on letting it run.
The solution
Arkheia puts governance on the execution path.
Not a PDF, not a dashboard you read later. At the moment AI acts, Arkheia can allow, block, require approval, write a private note, escalate, kill mid-flight, or roll back — and leave a decision receipt for every one. That is what turns a useful workflow into one a business can actually switch on.
Every governed invocation can be allowed, blocked, sent for human approval, held back to a private note, escalated, killed mid-flight, or rolled back — and each decision leaves a receipt of what was seen and what was done.
Where the gate sits
- API models — OpenAI, Anthropic, Gemini, xAI
- Local / self-hosted models
- MCP / tool governance
- Agent registry
- Decision receipts
- Cost attribution
What it unlocks
What the trust signal unlocks.
Once invocations run through a governed gate, the same loop gives you the records, the cost control and the learning that make autonomous AI safe to operate.
govern → prove (receipts) · control cost · improveDecision receipts
dogfoodEvery governed action leaves a tamper-evident receipt — who acted, what they did, on what evidence, under whose authority — hash-chained so the record can't be quietly rewritten.
Swarm cost management
dogfoodGoverned autonomy you can afford — see and cap what every agent spends. Cost is attributed per model, agent and workflow; models are tiered to the task, with runaway-spend caps and a kill-switch.
Self-improving workflows
demo-gradeThe learn-loop: outcomes become captured lessons that make the next run better. A resolved case writes back the fix, so the next similar case is faster — the system gets sharper as it runs.
Why we're different
The part no one else has: a runtime trust signal.
Governance can only gate what it can see — and this is the moat. Most tools inspect outputs after the fact, or give you observability. Arkheia measures model behaviour at runtime, against a per-model baseline, and turns that verdict into an execution gate — so the signal fires at the invocation boundary, before the AI acts, and can run without retaining the content.
| Common approach | Arkheia |
|---|---|
| Inspects output text | Measures behavioural surface |
| Generic evals & guardrails | Per-model behavioural baselines |
| Post-hoc review | Runtime invocation signal |
| Requires content access | Can run without retaining prompt + response content |
| Says what looked suspicious | Lets governance act before workflow damage |
See the signal fire
See detection running.
Pick a prompt and watch Arkheia profile the model's behaviour against its baseline, reach a verdict, and trigger the governance action. Scripted for the web — no setup, no key.
Illustrative, scripted for the web. Run it on your own prompts via the evaluation access on the diligence page — or run it in your own stack → with the public arkheia-mcp repo.
How you roll it out
Autonomy is earned, not granted.
AI doesn't get switched on all at once. It starts assistive and earns scope, stage by stage — each one widened only by evidence, with controls and receipts at every step. Here is the path a workflow walks, using support triage as the worked example.
How you start
Start with one workflow. Expand once it's proven.
The first step is a low-risk workflow improvement, not a technical detection evaluation. You pick something painful, run it under supervision, and only widen scope where the work has proved safe.
Why it's defensible