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.
A Phase III clinical trial with 12 sites across Europe was progressing on paper. But the lead CRA was allocated to 3 parallel trials, enrollment pace at 2 sites had dropped 34% below plan, and a document review backlog was growing silently.
Traditional reporting showed “on track” — the underlying data showed an impending 3-month delay. The cost of that delay: €5M in extended site operations, patient retention, and regulatory re-scheduling.
No single system connected the dots between CRA allocation, enrollment pace, and document backlogs. Until Aversight did.
Resource allocation data showed the lead CRA assigned to 3 concurrent trials with overlapping site visit windows in weeks 14–18.
Two sites showed sustained enrollment decline over 4 consecutive weeks. Pattern consistent with pre-delay trajectory in historical trials.
Regulatory document queue increasing week over week. At current pace, review completion would miss the submission window by 3 weeks.
“The signals were all there — in our data. We just couldn’t see them. Aversight connected the dots we were missing.”— VP Clinical Operations, Global Pharma Company
How It Worked
The CRA team kept familiar systems. Aversight read trial data, detected anomalies, delivered weekly intelligence.
Intelligence Accumulated
By month 3, Aversight recognized site enrollment patterns that predicted delays 6 weeks earlier than the CRO's own monitoring.
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This dashboard is our demo lens. In production, scores and alerts live in your Power BI, your inbox, your workflows.
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