Strataforge3 · Sentinel · Production AI Observability
Production AI models degrade quietly after deployment. Strataforge Sentinel gives you continuous observability into the model itself — not just your infrastructure — optimises against your actual business cost, and produces the governance documentation your regulators require.
The full package
Strataforge Sentinel is not a drift detector. It is the AI observability and governance layer between your model deployment and your regulatory accountability — combining continuous model monitoring, explainability, and documentation into a single audit-ready package.
Always-on visibility into whether your production model is still behaving the way it did when it was approved — recall, precision, and feature-level signal health tracked across every production period, not just at deployment.
ShieldXAI transforms black-box AI models into interpretable decisions — the auditability that regulated environments require, not an afterthought bolted onto a dashboard.
Strataforge governance infrastructure translates objectives into measurable MLOps metrics. Produces audit-ready documentation for EU AI Act Articles 9–17, DORA, and Shariah governance standards.
What's different
Most production ML monitoring tools were built to watch infrastructure — uptime, latency, throughput. Sentinel watches the model: whether its decisions are still correct, in systems where the cost of a wrong output is never equal to the cost of the right one.
Uptime monitors tell you the service is responding. They say nothing about whether the model's decisions are still correct. Sentinel watches the thing that actually matters.
A model can be "accurate" and still bleed money if costs aren't weighted correctly. Sentinel optimizes against your actual business cost function, not a generic F1 score.
Built for lean teams and emerging-market fintech. Runs as a lightweight service — not a six-month infrastructure project before you see any value.
Every other platform stops at a chart. Sentinel produces the executive summary and regulatory mapping your compliance team and board actually need.
No competitor in this space offers a governance layer aligned with Islamic finance principles. For GCC and South Asian institutions, this isn't a nice-to-have.
Most observability platforms assume Western enterprise budgets. Sentinel was designed for the constraints real teams operate under globally — then proven to scale.
Why Strataforge Sentinel
A production model that looks operational can have its recall quietly collapse. Sentinel gives you observability, explainability, and governance documentation — before losses appear, not after.
The problem
Data shifts. Patterns evolve. User behaviour changes. The model's learned relationships break down — but without observability, nobody notices. This is a global production ML pattern, not a regional one.
Model trained and validated. Strong ROC-AUC on held-out test data. Team moves on to the next priority.
Underlying feature distributions begin moving. Without observability, this signal is invisible — the model still looks fine from the outside.
The shift continues. Recall and precision begin moving — sometimes in directions that look fine in aggregate but aren't.
Recall drops below the defined safety threshold. Sentinel fires the alert. This is the moment that matters — before losses compound further, not after.
Audit scope
A non-invasive, read-only diagnostic across six dimensions of production model health. No codebase access. No system changes. No downtime.
Recall, precision, AUC, and FPR trends — the questions a CRO actually asks, not a chart only an engineer reads.
PSI and KS tests across your top production features — the actual inputs driving decisions, not a proxy on output scores.
Changes in the relationship between features and outcomes. We detect when your model's learned logic no longer matches reality.
Feature attribution and counterfactual analysis. Which signals drive decisions — and whether those signals are still valid.
Audit of what your current stack cannot see. Blind spots mapped against your model type, risk profile, and regulatory obligations.
Analysis of your decision boundary against actual business cost. Optimization can recover recall without model retraining.
How it works
01
Read-only access to prediction logs and feature distributions
02
Recall, AUC, FPR trends — plus PSI + KS signal checks
03
Decay timeline across your production periods
04
Feature attribution and explainability analysis
05
Business cost impact and regulatory gap assessment
06
Delivered as PDF in 5–7 days
Deliverables
PDF showing how your model's performance has actually moved in production — and which underlying signals explain it.
Recall, AUC, and FPR trends across your production periods with annotated inflection points and early warning markers.
Feature attribution summary and counterfactual examples. Your model's decisions, explained in language your compliance team can verify.
Monitoring thresholds calibrated to your model architecture, risk profile, and actual business cost assumptions.
EU AI Act Articles 9–17 mapping, DORA audit trail structure, and Shariah compliance scoring where applicable.
One page, non-technical. Ready to share with your Head of Risk, CTO, compliance team, or board.
API access
Run scoring, observability checks, explainability queries, and threshold diagnostics programmatically — from your CI/CD pipeline, data platform, or risk dashboard.
# Observability check — POST /v1/sentinel/observe import requests response = requests.post( "https://api.strataforge3.com/v1/sentinel/observe", headers={"Authorization": "Bearer <your_api_key>"}, json={ "model_id": "fraud_xgb_prod_v2", "period": "2025-Q2", "features": ["amount", "velocity_24h", "merchant_risk"], "alert_threshold_psi": 0.25, "explain": true } ) # Returns: recall trend · alert status · signal drift per feature · feature attribution data = response.json() print(data["recall_current"], data["alert_status"], data["top_signal_change"])
/observe · /recall · /explain · /threshold · /report · /alerts
JSON · PDF export · Webhook alerts · CSV
Bearer token · Key rotation · IP allowlisting
Live · Provisioned per engagement
Investment
No retainer required to start. No lock-in. The audit tells you what is breaking. Next steps are your decision.
A 15-minute scoping call is enough to find out whether a Sentinel audit is relevant for your stack. No commitment. No pitch deck. One diagnostic question.