4–6 weeks earlier. Every project, every portfolio. Aversight catches the pattern while it's still cheap to act — not after your next steering committee.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Monthly reporting cycles miss the weekly acceleration. By the time the breach shows in the report, mitigation options have narrowed and costs have doubled.
Steering committee finds out when the vendor SLA is already breached twice. The cheap intervention was possible 4 weeks earlier — nobody saw the signal.
Jira velocity drop. SAP PO delay. SharePoint meeting minutes. Five systems, five people, no single operator with a full picture. Patterns that span systems go unseen.
budget_burn signal watches burn rate against plan. Fires when >1.3× at <50% timeline — typical early-overrun signature.
milestone_drift signal flags when ≥2 milestones slip on the same project. Historical correlation with 6-week schedule slip.
resource_conflict signal detects when key roles hit >100% allocation across projects — early staffing fragility.
escalation_predictor compares your current project against 1,240+ completed ones. Matches fire before symptoms become obvious.
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