From a long list of findings to a short list of fixes — shipped.
We don't drop a 200-page report and walk away. Every dimension that scores low gets a remediation playbook, an owner, a deadline, and a partner to deliver it. We re-score after the fix.
Score → prescribe → ship → re-score.
A score is only useful if it changes. Risk reduction is the second half of every AgentSure engagement: closing the loop between what we measured and what your team actually changed in production.
Severity × likelihood × regulatory impact. Top 5 findings get an owner.
Dimension-specific playbook with code-level reference implementations.
Your team or a delivery partner implements; we hold the spec and the QA gate.
Same harness, same scoring rubric. Improvement is measurable, not narrated.
What "fix it" actually looks like.
A sample of the dimension-level playbooks we ship. Each one comes with a reference implementation, a regression test, and the evidence template your auditor will ask for.
Ground-truth eval set, retrieval rerank, citation enforcement, decoder constraints.
Input firewalls, tool allowlists, dual-LLM judge, structured output schemas.
Tokenization at ingest, output redaction, memory boundary review, DPA refresh.
Subgroup eval, reweighting, threshold calibration, fairness gate in CI.
Capability scoping, dry-run mode, human-in-loop on irreversible tools, rate caps.
Step budgets, cost ceilings, plan-of-thought guards, deadlock detectors.
We don't pretend to do everything.
We work with the best in each layer of the AI stack and bring them into your engagement when it makes sense. You get one accountable owner — AgentSure — and a curated bench behind us.
Score improvements you can put in front of an underwriter.
Our reference engagement took a Singapore LLM platform from CCC to AA in eight weeks. Yours is next.