01 / TRIAGE
Organize the work
reads the ticket text, the KB, closed tickets
WHERE YOU’VE SEEN IT
ConnectWise zofiQ
Kaseya
Halo
Thread
Give every technician an investigation team.
AI investigators run live diagnostics and post a cited diagnosis before a technician opens the ticket.
From the advisory board
“This goes beyond anything I have seen.” They’re building exactly where MSPs are feeling the pressure: how to adopt AI in a way that’s useful, safe, and operationally real.
See AI investigators in action.
WATCH IT RUN2 TICKETS
INGESTCLASSIFYBINDPLANPROBEHYPOTHESIZECONFIRMVERIFYCOMPOSEPOST
T+00:00
0 live checks · 0 systems · 0 flagged
ORCHESTRATOR
Idle
LEDGER · WHAT FLOWMIND RAN0 lines
OUTCOME
PROBABLE CAUSE
RULED OUT
NEXT STEP
EVIDENCE—
Today, MSP AI comes in two types.
Triage tools read the ticket and organize it. Workflow engines run a fix you already wrote. Neither one investigates a ticket whose cause is unknown. That is the work your senior technicians still do by hand.
02 / WORKFLOWS
Execute known work
runs a recipe that already exists
WHERE YOU’VE SEEN IT
Rewst
Pia
Vibe coding
WHAT YOU HAVEN’T BEEN SHOWN
There is a third type.
03 / FLOWMIND
Investigate unknown work
WHERE YOU’LL SEE IT
FlowMind
Automate known work. Investigate unknown work.
Tickets with a known fix belong in a workflow. Tickets whose cause is unknown have to be investigated before anyone can fix anything.
- Ticket · Service request Password reset
- Ticket · Service request On- and off-boarding
- Ticket · Service request Add, delete and unlock accounts
KNOWN WORK
The fix is known. It can be automated.
Best fit: workflow automation
- Ticket · Incident Wi-Fi is slow
- Ticket · Incident Mail keeps going to spam
- Ticket · Incident PC is sluggish
UNKNOWN WORK
The fix is not yet known. Someone has to investigate first.
Best fit: FlowMind investigation
Today’s answer to the unknown ticket leaves the work with you.
AI assist in your PSA summarizes the ticket, suggests articles and lists steps to try. Every one of those steps is still a login, a lookup and a wait, and a technician still does it.
AI ASSIST · IN YOUR PSA
#4628xx · Mail keeps going to spam
SUMMARY
Outbound mail from the client’s domain is landing in recipients’ junk folders since last week; nothing changed on their end.
PRIORITY
Medium · Queue: Service Desk
ARTICLES
Why email goes to spam
SPF, DKIM and DMARC basics
SIMILAR TICKETS
#4581xx · closed 07-02
#4490xx · closed 05-19
SUGGESTED STEPS
1. Check the SPF record 2. Verify DKIM is enabled 3. Ask the user for message headers
We’ve automated diagnostic investigation.
A fleet of specialized AI investigators, one per system, dispatched by an orchestrator. The ticket goes in; a cited diagnosis and a note come back on the same ticket.
AI INVESTIGATORS
FlowMind Orchestrator
PLAN · ROUTE · DISPATCH · COLLECT · VERIFY · COMPOSE
dispatches the fleet
dispatches the fleet
NETWORK
Cisco Meraki
41 probes · 5 live tools
Fortinet
41 probes · 4 live tools
HPE Aruba
7 probes · 4 live tools
Datto Networking
25 probes
UniFi Network
13 probes
CLOUD APPS
Microsoft Graph
73 API endpoints
Exchange Online
18 probes · 14 EXO + 4 DNS
Google Workspace
28 probes
ENDPOINT & SYSTEMS
NinjaOne
26 probes
ConnectWise RMM
9 probes
Liongard
7 verified metrics
DOCS & DB
IT Glue
SOPs · assets · contacts
Similar Tickets
resolved tickets · cited
PSA · SERVICE DESK
Where technicians live: tickets in, answers back
ConnectWise
Autotask
Halo
ticket ingested · history mined · diagnosis + note posted
13
AI investigators
one per system, across network, Microsoft 365, Google Workspace, endpoint, documentation and ticket history
301
diagnostic probes
298 read-only
13
live network tools
pings, traces, cable tests, throughput
3
PSA platforms
ConnectWise, Autotask, Halo, as trigger and destination
You never leave the ticket
Technicians never log in to FlowMind. A ticket lands in the PSA; the diagnosis comes back as a note on the same ticket, with a link to the full evidence report.
Read-only by default
Evidence collection never changes a customer environment. Anything that changes anything waits for a technician’s approval, every time.
Three synchronized layers.
Investigators gather the evidence. An orchestration layer decides which ones to run and keeps them honest. An intelligence layer turns the findings into a diagnosis.
LAYER 1 · AI INVESTIGATORS
AI Investigators
One investigator per system. Each runs that system’s diagnostics against live state.
LAYER 2 · ORCHESTRATION
The Orchestration Layer
Classifies the ticket, plans the checks, dispatches the investigators.
LAYER 3 · INTELLIGENCE
The Intelligence Layer
Synthesizes every finding, correlates, diagnoses and recommends, in the ticket.
One AI investigator per system. Each automates that system’s diagnostics.
Every investigator speaks its vendor’s API and knows which checks matter for which symptom. The two totals below are generated from the running catalog.
NETWORK
Cisco Meraki
ping · throughput · cable test · WLAN
Fortinet
firewall · VPN · WAN · policies
HPE Aruba
AP · client · uplink health
Datto Networking
switches · APs · DHCP
UniFi Network
console · clients · ISP metrics
CLOUD APPS
Microsoft Graph
identity · licenses · sign-ins · CA
Exchange Online
message trace · DKIM/DMARC · rules
Google Workspace
users · mail · Drive · devices
ENDPOINT & SYSTEMS
NinjaOne
device state · patches · disk · CPU
ConnectWise RMM
device state · alerts · inventory
Liongard
posture · certs · MFA · lockouts
DOCS & DB
IT Glue
SOPs · assets · contacts
Similar Past Tickets
your resolved tickets, cited
301
diagnostic workflows
13
live network tools
An orchestration layer that synchronizes the investigators.
It reads the ticket, chooses the checks and runs the investigators in sequence, inside a harness built for vendor APIs that time out, throttle and fail.
TICKET
Mail going to spam
ORCHESTRATION LAYER
CLASSIFY
Reads the ticket
PLAN
Chooses the checks
DISPATCH
Runs the investigators
THE HARNESS
ERROR HANDLING
RATE LIMITS
RETRIES
GUARDRAILS
Cisco Meraki
Fortinet
HPE Aruba
Datto Networking
UniFi Network
Microsoft Graph
Exchange Online
Google Workspace
NinjaOne
ConnectWise RMM
Liongard
IT Glue
Similar Tickets
Synthesize. Correlate. Diagnose.
Every finding lands in one picture. When two sources disagree, that disagreement is usually the diagnosis.
WHAT THE INVESTIGATORS FOUND
EXCHANGE ONLINE
DKIM signing: enabled · selector1, selector2
PUBLIC DNS
selector CNAMEs: not found
MICROSOFT GRAPH
no service incident · sign-ins clean
LIONGARD
no configuration change in the window · posture metrics clean
SIMILAR TICKETS
2 resolved matches · one published CNAMEs
INTELLIGENCE LAYER
SYNTHESIZE
every fact in one picture
CORRELATE
two sources disagree: enabled vs. unpublished
DIAGNOSE
one probable cause · five ruled out
THE OUTPUT
The investigators did the work before anyone opened the ticket.
Outbound mail landing in junk, the first of the two runs above. This is what was on the ticket when the technician first looked at it.
0
Portals opened by the technician
The investigation was in the ticket before anyone opened it.
4
Systems queried
Exchange Online + DNS · Microsoft Graph · Liongard · Similar tickets
18
Evidence events collected
Rules & forwarding · Email auth · Message tracing · …
5
Causes ruled out
forwarding · inbox and transport rules · outbound connectors · …
1
Technician-ready next action
Publish selector1 and selector2 CNAMEs at the DNS host
This is the diagnostic intelligence that senior engineers produce after deep investigation. FlowMind produces it at ticket creation. This is transformational.
Specialized agents, custom-built for customers.
The same engine runs at the other moments of the service loop: the call, the reset, the alert board.
LIVE
Password Reset
Agent
Approval-gated. Nine checks first.
LIVE
Intake Navigator
Agent
The right question, live signals, one-click ticket.
IN DEVELOPMENT
Voice
Agent
The same intake, by voice.
LIVE
Alerts Synthesis
Agent
Repeats grouped. One digest a week.
Our forward-deployed engineers build new workflow automations for you.
Measured, not claimed.
The value dashboard credits time per ticket from your own baselines and rates, and shows its arithmetic.
MEASURE 01
Investigation time removed
Technician minutes spent gathering evidence before and after FlowMind.
→ Hours Saved
MEASURE 02
Endpoints per technician
How many endpoints each technician can carry when the investigation arrives with the ticket.
→ Endpoints / Technician
Your baseline. Your rates. Your tickets.
FlowMind · Value dashboard
Sample data · illustrative
Today
MTD
QTD
30D
90D
Tickets Touched
412
318 sessions credited
Hours Saved
138hours
from your baseline assumptions
Endpoints / Technician
312
+26 vs 286 baseline
Hours saved by week · last 30 days
| Customer | Tickets | Hours |
|---|---|---|
| Northwind Legal | 168 | 54 |
| Harbor Dental | 139 | 47 |
| Summit Logistics | 105 | 37 |
| All customers | 412 | 138 |
Credited minutes = baseline − measured duration, per ticket. Baselines are configured per tenant.
Nothing changes without a technician. Nothing leaves the ticket.
FlowMind connects to the systems you already run through their APIs and works inside the PSA your technicians already use.
Read-only evidence
Every probe reads. Every evidence line carries a timestamp and the system it came from.
Approval before any change
A password reset runs only after a technician approves it, after its preflight checks pass.
The ticket is the interface
No FlowMind login for technicians. The note lands on the ticket in ConnectWise, Autotask or Halo, with a link to the full report.
Tenant isolation
Per-tenant credentials and rate-limit buckets. One client’s investigation never touches another’s.
Every tool you’ve tried. Here’s where FlowMind fits.
We already have AI triage in our PSA. Isn’t that the same thing?
Triage assistants such as ConnectWise zofiQ, Kaseya Cooper, Halo AI and Thread read the ticket and organize it: a summary, a priority, similar tickets, suggested steps. FlowMind runs the checks those steps describe, against the client’s live systems, and posts what it found. One question separates the two: when a tool cites something, is it citing the ticket text, the knowledge base, or a check it ran a minute ago?
We use Rewst or Pia. Can’t we build this as a workflow?
A workflow runs a fix you already wrote for a ticket you already understand. An unknown ticket has no recipe: the next check depends on what the first check returned. FlowMind plans the checks per ticket, runs them in sequence and revises the plan on the findings. Workflow engines are the right tool for tickets with a known fix, and FlowMind’s notes tell you which kind of ticket you have.
What about AI technicians that resolve tickets on their own?
Their own sites list what they execute: password resets, account unlocks, onboarding and offboarding, mailbox and group access, endpoint scripts. That is known work with a known fix. FlowMind’s job starts where that list ends, on the ticket nobody pre-modeled.
Couldn’t we build this ourselves with an LLM and some scripts?
You would be building 13 investigators speaking 13 vendor APIs, 301 diagnostic probes, a harness for timeouts, throttling, retries and tenant isolation, and an intelligence layer that decides what to post and when to stay silent. Then you would keep all of it current as each vendor’s API changes. FlowMind is that build, already graded on thousands of live tickets.
What does FlowMind do to our clients’ environments?
It reads. Evidence collection is read-only. Anything that changes anything, such as a password reset, waits for a technician’s approval. Every evidence line in a note carries a timestamp and the system it came from, so a technician can check the check.
Would we let it write into a client-visible ticket? How do we know it’s right?
A note is posted only when it would change a technician’s next action. In one 30-day window at a live customer the value gate withheld 350 notes, with zero load-bearing false positives. Each note separates what was found, what worked before on your own closed tickets, and what could not be verified.
What about our RMM and the vendor dashboards we already have?
Your RMM and the Meraki or Microsoft 365 consoles each show their own silo, and technicians keep them. FlowMind uses them as evidence sources, correlates across them and posts one diagnosis to the ticket, so nobody has to open four consoles before the first useful sentence.
What does it take to get started?
A scoped, read-only pilot on real tickets. Connect your systems read-only, observe a ticket cohort, review the findings together. Technicians never log in to FlowMind; the diagnosis arrives as a note on the ticket in ConnectWise, Autotask or Halo.
THE NEXT STEP
Prove it on your own tickets.
Start with a scoped, read-only pilot on real tickets. Measure the evidence quality and operational value before expanding coverage.
- 01Connect your systems, read-only
- 02Observe a ticket cohort
- 03Review findings together
No new tools. No new training. No commitment.