Managed cloud
The operations loop the internal agents work inside.
Details
AI Workflows
AI That Accelerates.
Humans That Decide.
Practical AI systems for qualification, triage, and operational support. Always with human oversight, confidence scoring, and full audit trails.
Supervised AI
| 4.2x faster qualification | 95%+ confidence | Full audit trail How it works
Every answer has a source. Every action has a threshold.
A request comes in. The workflow retrieves from your curated knowledge, scores its own recommendation, and routes it by confidence: act with logging, present with evidence, or hand it to a person. Whatever happens is recorded. That is the whole system, and it runs the same way for a customer on this site, an incident in our operations, or a ticket in your queue.
One request through the gate
Sampleticket-2291 · support
"Our nightly backup job reported two failures this week. Is our data at risk, and what should we do?"
01Retrieved
- 01Backup floor: 3-2-1+1 and restore verificationtraced
- 02Runbook BK-02: failed backup jobtraced
- 03Severity matrix P1 to P4traced
02Scored
Classify P3 · backups, assign to operations, reply with the restore-verification status and the next check.
91%
03Routed
0%80%95% · auto
Presented with evidence at 91%
Between 80% and 95%: the reviewer sees the recommendation, the three sources and the score, and decides. Nothing executes on its own.
04Recorded
ticket-2291 · rec: P3/backups → operations · sources: 3 · conf: 0.91 · band: evidence · human: pending · outcome: pending
Retrieve · score · route · record
Four moves, always in this order. The score is the only judgment the system makes on its own, and it is calibrated against real outcomes at every review.
Step through it
What is built in
Three programs. One governance.
Customer-facing, internal, and business workflows, each scoped with clear guardrails, escalation paths and human override. The same confidence gate and audit trail under all three.
Customer guidance flows
On-site conversational guidance that explains offerings, captures requirements and routes qualified demand.
Built for · Enquiries that arrive when nobody is there to answer them.
Internal ops agents
Automated incident triage, runbook context packaging and repetitive platform tasks, under the guardrails.
Built for · Triage that pulls an engineer off real work.
Business workflow agents
Support ticket classification, sales qualification assist and process automation with a full audit trail.
Built for · Process-heavy work that waits for whoever is free.
Governance
Nothing destructive. Nothing unlogged.
Guardrails live in the workflow definition itself, not in a policy document. The workflow cannot execute destructive actions, modify production infrastructure or make commitments without a person. And every assisted decision leaves the same record.
decision-record_ticket-2291.md
Sample format# AI-assisted decision · business workflow agent
Input: support ticket, "nightly backup job reported two failures"
Sources: backup floor · runbook BK-02 · severity matrix
Recommendation: classify P3/backups, assign to operations, reply with restore status
Confidence: 0.91 · band: present with evidence
Reviewer: approved with one edit to the reply
Executed: classification + assignment · reply sent by the reviewer
Outcome: restore verified, ticket closed
Calibration: counted at the next accuracy review
Always automatic · logged
Routine work at 95% confidence and above
- Data gathering and pattern matching
- Ticket classification and routing
- Runbook context packaging
- Drafting a recommendation for review
- Requirement capture on the site
Always a person
Whatever the confidence says
- Architecture changes
- Escalation decisions
- Customer communication and commitments
- Any change to production infrastructure
- Anything destructive
Live on this site
Program 01 is running right here.
The guidance operator in the corner of this page is a CloudPresto AI workflow: grounded on our own knowledge base, confidence-gated, every conversation logged. The qualification workflow beside it is another. Both route what they learn to a person.
Qualification workflow
Tell us about your environment
1 / 5
How critical are your workloads?
This determines response targets and service depth.
How we work
One use case first. Then the next.
No fleet of agents on day one. We start with one well-defined use case, prove it works under full human review, and expand from there.
01
Use case definition
Identify the highest-value use case, map the manual process, define success criteria and agree the governance boundaries.
02
Knowledge base setup
Curate what the workflow may retrieve from: documentation, runbooks, service descriptions, history. Define retrieval boundaries and access.
03
Build and calibrate
Build the workflow with confidence scoring, guardrails and audit logging. Test against historical cases and calibrate the thresholds.
04
Supervised launch
Go live with 100% human review. Every recommendation is verified by a person before action, until the numbers earn the gate.
Get started
See a governed workflow on your own case.
Bring one process that costs your team time. We show the retrieve, score, route and record loop on it, and the scoping defines the first workflow.
What the scoping produces
01
The use case, scoped
The manual process mapped, success criteria written, governance boundaries agreed.
02
The knowledge inventory
What the workflow may retrieve from, and what it may not.
03
The confidence policy
Which actions are automatic at 95%, which are presented with evidence, which always go to a person.
The first workflow starts from here.
Go further
Guides, related work, questions.
Collection
AI essentials
The approach: supervised automation, confidence scoring, audit trails.
Collection
AI deep dive
The three programs, governance, RAG architecture and onboarding.
Guide
AI That Accelerates. Humans That Decide.
Why the goal is judgment with better inputs, not fewer people.
Guide
RAG Architecture: Grounded, Not Hallucinated
How every answer traces back to source material.
