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AI Risk Intelligence · For Technology & IT

Digital transformations fail on complexity your PMO already tracks.

For Risk Managers · PMO Leads · BI Teams · Decision Makers

Aversight detects IT program overrun patterns 4 weeks before they hit your steering committee.

Your project data lives in Jira, Azure DevOps, ServiceNow, and SAP. Aversight reads them all, detects velocity anomalies and dependency risks weeks before your status reports surface them, and delivers insights into your existing dashboards.

From mid-market teams to global enterprises. No minimum project count.

Take the Free Maturity Check →
71%
Of IT transformations exceed budget
€3.2M
Avg IT program overrun
4 wks
Earlier detection
Read-only SOC 2 Type II ISO 27001 Your infrastructure Autonomous intelligence EU AI Act ready

Start with the dashboard. Zoom into every capability.

Nine real views — from the overall portfolio picture down to the Monte Carlo engine running underneath. Jump to what matters for your role, or scroll through the whole thing. Nothing hidden.

Jump to your role:
Aversight · Portfolio Intelligence
Synced 14 min ago
2
Critical >70
▲ +1
4
High 50–70
▲ +1
8
Watch 30–50
stable
6
Stable <30
▼ 2
58
Avg score
+3 WoW
€1.8M
Prevented YTD
4 projects
Score distribution · 20 projects
2
4
8
6
CriticalHighWatchStable
Risk heat map · Impact × Likelihood
A7
B3
D12
E4
F1
Likelihood →
Active alerts (3 new today)
Alpha-7 · overrun in 28 days
87% confidence · 2 min ago
Bravo-3 · vendor SLA at risk
71% confidence · 14 min ago
Delta-12 · velocity drop
64% confidence · 1h ago
+ 20 more this week
view all →
Portfolio Gantt · W14 — W21 · 5 active projects
W14W15W16W17W18W19W20W21
Alpha-7
!
87
Bravo-3
!
62
Delta-12
58
Echo-4
31
Foxtrot-1
22
Engine layers · live
Rules engine
12 active · 3 fired today
ML pattern
2 matches today · 1,240 trained
LLM recommendations
5 pending review
Monte Carlo · Alpha-7
P50 (median)€600K over
P5 (worst case)€2.4M over
Top driver: vendor SLA (+42%)
Live signal feed
06:14Jiravelocity −31%
06:02SAPPO delayed 17d
05:48ServiceNowSLA breach
05:30JiraCR-22 added
High-priority alert
Today · 06:14 CET
Budget overrun predicted — Alpha-7, 28 days ahead.
Portfolio: Grid Expansion North · Phase 3 · PM: J. Halvorsen

Budget burn on Alpha-7 has reached 1.4× plan at 47% timeline — well above the 1.3× threshold. Three milestones are overdue and the Lead Solution Architect is at 115% allocation. Pattern escalation_predictor matches 14 historical projects with similar overrun outcomes.

Signal contribution
budget_burn1.4× plan (thr. 1.3×)
milestone_drift3 overdue (thr. 2)
resource_conflict115% key role (thr. 100%)
open_risk_severityP×I = 16 (thr. 15)
87 Risk score · Critical band (>70) · pattern_match: escalation_predictor
Evidence trail
Alert ALT-2026-04-091
Rule ER-004 v2.1
1Rule fired
ER-004: Sustained velocity drop ≥ 25% over 3 sprints + ≥ 2 change requests open → predict overrun within 30 days.
2Raw data points
Jira: ALPHA7-2041, 2078, 2102 (velocity)
Jira: ALPHA7-CR-14, -19, -22 (change requests)
ServiceNow: INC-88412, INC-88733 (SLA breaches)
SAP: PO 4500-114-17 to -29 (delay log)
3Pattern match (ML)
Signature matches: Ostfriesland-2024 (€2.4M over), NordLink-2023 (€1.8M over), Bremerhaven-2024 (on-track, false match).
4LLM explanation
Claude Sonnet 4.6 generated the plain-language summary using only the data above. Prompt + output logged for audit.
Pattern match confidence 91%
Portfolio Gantt
5 active projects · Week 16 / 2026
synced from MS Project
+ Jira milestones
W14W15W16W17W18W19W20W21
Alpha-7 Grid Expansion
!
!
87
Bravo-3 Substation
!
62
Delta-12 Cable Lay
58
Echo-4 Smart Meter
31
Foxtrot-1 Windpark
22
Critical At risk On track ! active alert
Delivery · Middleware output
Where your intelligence lands
Standard formats
No migration required
📎
Power BI · Tableau · Looker
Push to dataflow / semantic layer. Refreshed daily. Your existing reports keep working.
Dataflow
Weekly email brief
HTML or plain text. Top risks, recommended actions, 2-minute read. Every claim cross-linked.
Email
📄
PDF · PPTX · DOCX exports
Branded, steering-committee-ready. Auto-translated EN/DE/FR/ES. White-label mode for consulting.
Files
🔌
REST API · Webhooks
JSON output with OpenAPI schema. Plug into any downstream system. Alerts pushed on threshold breach.
API
💬
Teams · Slack
Critical alerts only. Channel-routed by portfolio. No noise for low-priority signals.
Chat
Aversight is middleware: it sits between your sources and your outputs. No new dashboard to own, no data to migrate.
Active data connectors
Last scan 14 min ago
Jira Cloud
12,481 tickets indexed
SAP ERP
3,204 POs · 14 plants
ServiceNow
SLA + incident feed
SharePoint
Project docs, minutes
MS Project
218 active schedules
Outlook 365
Re-authing · 2 min
Excel / OneDrive
94 portfolio sheets
Azure DevOps
IT projects · 38 repos
Asana
Mid-market PM · 412 projects
Monday.com
Boards · pulses indexed
Notion
Docs, minutes, RAID logs
Google Workspace
Drive + Calendar + Gmail
Week 16 / 2026
To: Steering Committee
Portfolio at a glance
38 active engagements · 3 escalated · 1 new pattern detected
Client A — ERP rolloutScore 87
Client C — M&A integrationScore 74
Client F — Data migrationScore 69
• Client A: schedule go/no-go review before Friday; budget signal matches historical overrun pattern (91%).
• Client C: request updated staffing plan; velocity drop on workstream 2.
• Client F: verify cutover window; dependency on legacy CRM looks under-scoped.
Aversight Intelligence Engine · Reviewed: M. Okonkwo, Partner
What happens in your first six months
Week 1–2 · Live now
Read-only connections live
Jira + SAP + SharePoint connected. First portfolio scan complete. Baseline scores published.
Week 4
First rules-based alerts
Configurable thresholds active. Your first audit-ready alert hits your inbox — with full evidence trail.
Month 3
Pattern recognition kicks in
ML has enough history to detect cross-project anomalies. First predictive alert usually arrives here.
Month 6
Predictive intelligence
Escalation prediction at >85% precision on your portfolio. Weekly briefings fully automated.
Month 12+
Compounding intelligence
Your knowledge base is now your unfair advantage. Every completed project teaches the engine.
Scenario simulation · Monte Carlo
Alpha-7 · what-if risk score distribution
1,000 iterations
Updated 14 min ago
Budget increase+15%
0%50%
Milestone delay+10 days
0d30d
Resource loss1 person
03
Score 30 (stable) Score 60 (high) Score 90 (critical)
Best case (P95)
Score 48
Watch band · 5% probability
Median (P50)
Score 72
Critical band · 50% probability
Worst case (P5)
Score 89
Deep critical · 5% probability
P(critical) 72% under current inputs P(high): 93%
Rules engine · Deterministic layer
Signal weights & thresholds
LIVE
5 signals · weighted sum → 0–100 risk score · recalculates across portfolio on save
budget_burn28%
alerts when burn rate > 1.3× plan at <50% timeline
milestone_drift22%
alerts when ≥ 2 milestones overdue
resource_conflict20%
alerts when allocation > 100% on any key role
open_risk_severity18%
alerts when P×I ≥ 15 (high/critical risks open)
risk_staleness12%
alerts when risk register > 14 days without update
Pattern engine · Cross-portfolio learning
5 pattern types · detected today: 2
Retrained weekly
on your portfolio
escalation_predictor 91%
Projects trending to critical — 5+ consecutive days with score >50, later hit critical threshold.
Affects: Alpha-7, Bravo-3 · detected 06:14 today
budget_burn_correlation 87%
Burn rate >1.3× plan at <50% timeline, combined with elevated open risk severity.
Affects: Alpha-7 · historical matches: 14 similar projects
milestone_cascade 72%
2+ overdue milestones on same project — historically predicts 6-week schedule slip.
Watching: Delta-12 · 1 overdue, 1 at risk
resource_cluster 68%
Key role over-allocated >100% across multiple projects in the portfolio.
Watching: 3 solution architects · 4 projects
trend_correlation 54%
5+ consecutive rising days on score — early signal that later correlates with critical outcomes.
Monitoring: no active matches this week
Every match logged · false positives flagged by users feed back into the model · confidence improves with your portfolio history
AI recommendation · LLM layer
Alpha-7 · suggested action
Claude Sonnet 4.6
Grounded in
budget_burn rule (fired 06:14 · burn ratio 1.4×)
Pattern: escalation_predictor (91% confidence)
Evidence: 4 BudgetSnapshots · 3 Milestones · 1 high-severity Risk
Action
Schedule go / no-go review with Alpha-7 steering team; freeze change requests for 4 weeks.
Who
J. Halvorsen (PM)
+ Steering Committee
Timeline
Before Friday
(3 business days)
Expected impact
Prevent ~€1.8M overrun
based on 14 historical matches
Priority
HIGH
01

Your portfolio — at one glance.

One view. Every signal, every alert, every trend. The shape of your portfolio in under five seconds — before your first meeting. Click into any cell to see the raw evidence.

  • 4 live KPIs: open risks, escalations, portfolio score, prevented value YTD
  • Interactive impact × likelihood heat map
  • Top-priority alert feed (3 visible, rest on click)
  • Live signal stream from every connected system
02

Alerts you can act on before the meeting.

Every alert reads like a memo, not a log line. A concrete driver. A confidence score. A recommended action. Your inbox becomes a decision queue — not a problem feed.

  • Plain-language summary, 3–5 sentences
  • Numeric drivers tied to the underlying ticket, PO, or SLA row
  • One-click open into Jira, SAP, ServiceNow
  • Confidence scoring based on 14+ historical matches
03

Every alert is auditable to the source.

The EU AI Act is here. Aversight is built around transparent scoring. Every alert shows the rule ID, the raw data points that fired it, the historical pattern it matched, and the exact LLM output — with prompt and logs preserved.

  • Rule identifier + ruleset version for every alert
  • Direct citations to raw ticket, PO, SLA, or doc
  • Pattern match confidence + historical references
  • Full LLM prompt & output log for audit
04

Every project. Every milestone. Risk overlaid.

Your Gantt — but with intelligence baked in. Red bars are flagged projects. Flags mark active alerts. The “today” line moves itself. Drag-through any bar and Aversight pulls the raw ticket, PO, or minute behind the slippage.

  • Auto-synced from MS Project, Jira, Asana, or Smartsheet
  • Risk overlay recomputed every night
  • Dependency map: who blocks whom, with alert propagation
  • Critical path re-calculated on every new signal
05

Delivered into the tools your team already opens.

Aversight is middleware — it sits between your data sources and your outputs. No new dashboard to own. No data migration. Intelligence lands as standard formats in whichever channel your team actually uses every day.

  • Dataflow push to Power BI, Tableau, Looker (no custom visual, no ETL)
  • Weekly HTML email brief — every claim cross-linked to raw evidence
  • Branded PDF / PPTX / DOCX exports · auto-translated EN/DE/FR/ES
  • REST API + webhooks · OpenAPI schema · plug into any downstream system
  • Teams / Slack routing for critical alerts only
06

Signals from every system. Diverse by design.

The more signal diversity, the sharper the intelligence. Aversight reads from 8+ enterprise systems out of the box — tickets, ERP records, files, emails, calendars — and 40+ more via REST.

  • Pre-built: Jira, SAP, ServiceNow, SharePoint, MS Project, Outlook, Excel, Azure DevOps
  • Live status view: last scan, failed sources, row counts
  • Read-only service accounts — zero write access
  • SOC 2 Type II, ISO 27001, DSGVO-compliant by default
07

Board-ready briefs. In one click.

Every Monday, a one-pager lands in your inbox. Top risks. Client-ready language. Forward it to the steering committee without reformatting. White-label mode puts your logo on it for external engagements.

  • Branded PDF + editable PPTX & DOCX outputs
  • Automatic translation: EN / DE / FR / ES
  • White-label mode for client-facing deliverables
  • Every claim cross-linked to evidence for Q&A
08

Value in weeks — not quarters.

Typical engagement path: read-only connections in week two, first audit-ready alert by week four, pattern detection from month three, predictive intelligence by month six. Your practice bills faster because the intelligence is live earlier.

  • Week 1–2: baseline scoring across full portfolio
  • Week 4: first rules-based alert with full evidence
  • Month 3: pattern recognition + cross-project anomalies
  • Month 6+: predictive alerts at >85% precision
09

Under the hood: 10,000 futures per project, every night.

Every night, Aversight runs 10,000 Monte Carlo simulations per active project. Worst case, median, best case — with the drivers that move the outcome most, ranked. No black box: every assumption is surfaced, editable, and logged for audit.

  • P5 / P50 / P95 outcome distributions per project
  • Editable assumptions — see impact in seconds
  • Driver decomposition: which variable moves the outcome most
  • Export to Excel with full simulation log for audit
10

The rules engine: every alert is auditable code.

The deterministic layer. Every rule is written in plain language, version-controlled, reviewed by Legal and the AI Committee, and test-covered. You see exactly what would fire, why it would fire, and its historical precision — before you promote it to production.

  • WHEN / THEN syntax — readable by risk managers, not just engineers
  • Test-driven: every rule shipped with true-positive / false-positive history
  • Version-controlled: rollback any rule to any previous version
  • Approval workflow: Legal & AI Committee sign-off before PROD
11

Pattern recognition: your projects, ranked against history.

The statistical layer. Every active project is embedded into a 248-feature vector and ranked against 1,240 completed projects from your organization. When the closest historical matches overran — you get warned. When the match pattern was a false positive last time — the engine remembers and downweights it.

  • 248-dimensional project embedding, trained on your data
  • Top-5 historical matches surfaced with confidence + actual outcome
  • Feedback loop: false positives flagged by users adjust the model
  • Retrained weekly on your completed projects — compounds over time
12

AI recommendations — grounded, not generated.

The generative layer sits on top of rules and patterns. Claude Sonnet 4.6 turns the deterministic output into plain-language actions — but only using data the rules engine and ML already verified. No hallucination layer. Every prompt and output is logged for 7-year audit retrieval.

  • Grounded exclusively in rule output + pattern data + raw evidence
  • Plain-language action (“schedule go/no-go review before Friday”), not a summary
  • Full prompt + output logged for EU AI Act audit (7-year retention)
  • One-click Accept / Edit / Dismiss — every decision captured as training signal
Core challenges in technology

Why IT programs run over budget and schedule.

01

Tool sprawl creates blind spots

Jira, Azure DevOps, ServiceNow, SAP, Confluence — each holds a piece. No single system shows the full risk picture across your digital portfolio.

02

Agile velocity masks systemic risk

Sprint completions look healthy, but cross-team dependencies, tech debt accumulation, and integration risks compound invisibly.

03

Alert fatigue numbs the team

200+ Jira notifications per day. The signal that predicts a €3M overrun gets buried in noise.

What Aversight monitors

What Aversight monitors for technology teams.

📈

Sprint velocity anomalies

Detects when completion rates diverge from historical baselines across teams.

🔗

Cross-team dependency risks

Maps blocking dependencies and flags cascade effects before they delay delivery.

⚠️

Integration failure patterns

Monitors deployment frequency, rollback rates, and incident correlation.

👥

Resource allocation drift

Identifies when teams are stretched across too many parallel initiatives.

Powered by three autonomous intelligence layers: deterministic rules for auditability, statistical ML for pattern detection, and LLM for actionable recommendations. All three run continuously — no manual trigger required.

Early warning system

What Aversight alerts look like for technology.

⚠️
ERP Cloud Migration
IT Transformation€8.2M
84
⚠️
Platform Modernization — Core Banking
Digital€15.4M
69
💡
Data Lake Infrastructure
IT Infrastructure€3.1M
47
External data sources

Industry data that powers smarter risk models.

📊

Gartner IT spending benchmarks

Compare your program costs against industry averages by category.

💻

Stack Overflow Developer Survey data

Talent availability and skill gap signals for technology decisions.

🔒

CVE vulnerability databases

Security risk signals for dependencies in your technology stack.

☁️

Cloud provider status feeds

AWS/Azure/GCP incident data to model infrastructure dependency risk.

Scenario analysis

What does Aversight actually show you?

“What if your core API team loses 2 senior engineers mid-sprint?”

Best
2 weeks delay, +€0.8M additional cost
Mid
6 weeks delay, +€3.5M additional cost
Worst
14 weeks delay, +€8.2M + cascading downstream delays
Trusted by leading organizations

Used by teams at top technology companies.

Enterprise software vendors Semiconductor manufacturers Platform technology companies Cloud & infrastructure

“We caught a cross-team dependency risk in our platform migration 4 weeks before it would have cascaded into a 3-month delay. Aversight turned a potential €5M overrun into a 2-week course correction.”

— VP Engineering, Global Technology Company

What this means for your career.

🚀

From “Department of Jira Tickets” to strategic advisor

Stop drowning in tool notifications. Start delivering intelligence that shapes transformation strategy.

📊

Cross-portfolio visibility, not dashboard fatigue

Present unified risk data across Jira, Azure DevOps, and ServiceNow — in 20 minutes instead of 4 hours.

💼

The tool that gets your transformation budget approved

Show your leadership the ROI before you ask for investment. The numbers speak for themselves.

Calculate your ROI →

What Aversight means for your role

If you're an Analyst

You pull data from Jira and ServiceNow into spreadsheets every sprint, watching velocity metrics deteriorate with no tools to predict where delays will hit next. With Aversight, cross-system patterns are detected automatically — and you become the analyst who called the platform migration delay 6 weeks early.

If you're a Risk Manager

Your risk assessments reach the steering committee after the sprint is already done. By the time they read it, the delay is already a crisis. With Aversight, you present AI-backed predictions 4 weeks ahead — and the committee acts immediately because the data is undeniable.

If you're a Director / VP

You lose credibility when IT programs overrun after your team flagged no issues. Your people do great work, but the value is invisible until something goes wrong. With Aversight, you quantify risks prevented and justify transformation budgets with data.

If you're a CTO / CIO

Every delayed migration costs political capital. Aversight predicts which programs will escalate — so you allocate resources to prevention, not firefighting.

Career impact, not just project impact

BI analyst connected Jira velocity data with ServiceNow incident trends and flagged a cross-team dependency risk 5 weeks before the program review. Migration stayed on schedule. Promoted to Senior BI Engineer within 8 months.

BI Analyst, enterprise software vendor

Risk director presented AI-backed predictions to the transformation steering committee instead of static status reports. Committee now schedules monthly risk intelligence sessions — and approved 3 additional headcount for the PMO.

Risk Director, platform technology company

VP of Engineering used accumulated pattern data from 6 completed migrations to redesign the resource allocation process for cloud transformations. Program delivery predictability improved by 40%.

VP Engineering, global hardware & infrastructure vendor

Why your leadership will approve this

Your CFO wants:

Prevention, not firefighting. Aversight turns risk from a cost center into measurable savings — with data your CFO can put in the board deck.

Your CTO wants:

Competitive intelligence, not status report overhead. Pattern recognition across the portfolio identifies which transformations will succeed — and which need intervention now.

Without Aversight?

You stay stuck with fragmented tool data, sprint reports that mask systemic risk, and steering committees where you present status updates that executives politely ignore. The analyst stays invisible. The risk manager stays excluded. The director keeps losing political capital to IT overruns that could have been caught 4 weeks earlier.

Gets Smarter Over Time

After 6 months, Aversight recognizes delivery patterns across all active programs — predicting overruns before they happen, based on early signals that took your PMO leads years to learn.

The average project overrun costs €2.3M. Calculate what earlier detection saves you →

Connects to Your Existing Stack

JiraAzure DevOpsServiceNowSAPConfluence

Read-only. No data migration. Your teams keep working as before.

Not the decision maker? Share this page with your team.

Your projects know more than you can see today.

30 seconds — and we'll get back to you within 24 hours. No sales pitch, just an honest conversation about your challenges.

This dashboard is our demo lens. In production, scores and alerts live in your Power BI, your inbox, your workflows.

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