Internal Intelligence
Your data, made intelligent
Every MSP runs 5-8 core platforms that don't talk to each other. Elevate pulls data from each one, normalizes it into a single schema, and then runs AI models on top — surfacing what no human has time to find.
Your data is trapped in silos — and nobody has time to analyze it.
When a client calls about a server issue, your tech opens ConnectWise for the ticket, switches to Datto to check device health, opens IT Glue for the runbook, then checks QuickBooks to see if they're even under contract. That's 4 logins, 4 contexts, and zero connection between them.
The cost isn't just time. It's the insights you never see: which clients have the most repeat issues, which devices are approaching end-of-life, which contracts are underwater on margin. Your PSA has 50,000 resolved tickets with hidden patterns — but nobody has 200 hours to analyze it manually.
Today, ticket routing is either round-robin (ignores skill match) or manual (depends on dispatcher knowledge). Runbooks get written only when someone remembers. Churn is detected when the client sends a cancellation email. Internal Intelligence fixes all of this — automatically.
Five data sources. One unified model.
Professional Services Automation
ConnectWise Manage, Autotask, HaloPSATickets, SLAs, time entries, client contacts, service boards
Remote Monitoring & Management
Datto RMM, ConnectWise Automate, NinjaOneDevice inventory, OS versions, patch status, alerts, uptime
Billing & Accounting
QuickBooks, Xero, ConnectWise SellInvoices, contract values, margins per client, recurring revenue
Knowledge & Runbooks
IT Glue, Hudu, ConfluencePasswords, configs, network diagrams, SOPs, runbooks
Vendor & Ordering
Pax8, Ingram Micro, TD SynnexLicense counts, renewal dates, vendor pricing, order history
Three steps to a unified data layer.
Authenticate & Sync
OAuth2 or API key connection to each platform. Initial full sync pulls historical data (tickets, devices, invoices). Then webhooks + polling keep it current — typically within 60 seconds of a change.
Normalize & Map
Raw data gets transformed into a common schema. A "device" in RMM, an "asset" in PSA, and a "line item" in finance all become the same entity — linked by serial number, hostname, or client ID.
Link & Index
Cross-references build the graph. Every device knows its contracts, every ticket knows its device and client, every client knows their spend, device count, and open issues. The whole picture, queryable.
From fragmented to unified.
ConnectWise ticket #48291 — "Server down at Acme"
Incident linked to: Server ACME-DC01 (RMM), Contract #1204 (PSA), Monthly invoice $4,200 (Finance), Runbook: DC-failover-v3 (Docs)
5 separate systems, 5 separate views of the same client
1 unified client profile: 47 devices, 12 open tickets, $50K ARR, 3 expiring licenses, 2 unpatched CVEs
Four AI models. Each solves a specific problem.
Ticket Classifier
Raw ticket text + metadata
NLP model trained on your historical tickets. Learns your categories, not generic ITSM taxonomy. Fine-tuned on the language your clients actually use — "internet is slow" maps to Network > Performance, not a generic "Other."
Category, subcategory, urgency score, estimated resolution time
Smart Router
Classified ticket + tech profiles + current workload
Matches ticket characteristics against each technician's resolution history. Factors in: specialization (who resolves this type fastest), current queue depth, SLA deadline, client tier, and availability.
Recommended tech assignment with confidence score
Runbook Extractor
Resolved ticket notes + time entries
When a tech resolves a novel issue (no matching runbook), the model extracts the step-by-step resolution from their notes and time entries. Structures it into a draft runbook with prerequisites, steps, and verification.
Draft runbook ready for tech review
Churn Predictor
Client interaction patterns over 90-day window
Regression model tracking 12 signals: ticket volume trend, response satisfaction, SLA breach rate, invoice disputes, contact frequency drop, escalation rate, contract renewal proximity, competitive mentions, project delays, scope creep incidents, exec engagement level, and technology refresh adoption.
Churn probability score (0-100) + contributing factors
How a ticket flows through Intelligence.
"Outlook keeps crashing on Sarah's laptop" — Acme Law
Category: Software > Email Client > Crash | Urgency: Medium | Est: 45 min
Mike: 92% match (14 similar tickets, avg 38 min) | Sarah: 78% (8 similar, avg 52 min)
Auto-assigned with runbook link: KB-2847 "Outlook Profile Rebuild"
Novel approach: discovered corrupt add-in, not profile issue
Draft runbook created: "Outlook Crash — Add-in Conflict Resolution" → queued for review
Acme Law: risk unchanged (ticket resolved within SLA, satisfaction survey: 4/5)
What changes for a 50-person MSP.
Faster resolution
Skill-matched routing vs. round-robin
Earlier churn warning
vs. finding out at contract renewal
Dispatcher time saved
Automated classification + routing
More runbooks created
Extracted from every novel resolution
Acme Law — 47 devices, $50K ARR
PSA says: 23 tickets this month (up 40% from last). Average resolution: 4.2 hours. SLA breaches: 3.
RMM says: 5 devices running Windows 10 21H2 (EOL). 2 servers with >90% disk usage. Backup failures on ACME-FS01 for 3 days.
Finance says: Contract is $4,200/mo all-inclusive. Actual cost of service: $5,100/mo. Negative margin for 4 consecutive months.
Docs say: Last network diagram update: 14 months ago. 6 runbooks reference decommissioned server.
Internal data is smart.
Now add the outside world.
Level 02 maps your clients' environments against CVE databases, vendor EOL timelines, and compliance regulations — turning external data into actionable risk intelligence.