KERVALT / SAFETY
Safety-first research for AI you can trust
We build frameworks for testing, alignment, and responsible guardrails. Safety research at Kervalt is built into the product, so controls work in real use.
01 / PILLARS
Safety built into the system
We focus on the controls that keep AI reliable and accountable.
Clear rules
Your organization's values, legal limits, and sector rules are built into the system.
Stress testing
We continuously try to trick, mislead, or break the system and publish what we learn.
Reviewable answers
Every answer is logged, cited, and reviewable. Odd results are flagged for human review before any action is taken.
Controlled actions
Agents get explicit permission scopes, with dependency tracking to prevent unintended cascades.
02 / ALIGNMENT
Stopping problems before they start
Rules, boundaries, and permissions are enforced before the system answers, not after.
BEFORE
Output filters catch mistakes too late
Teams often rely on filters that catch problems after the answer is already out. Harmful outputs reach users before detection. Connections between actions are invisible. Compliance teams review logs with no source links.
AFTER
Alignment is built into the architecture
Rules, boundaries, and permissions are enforced before generation. Dependencies between actions are mapped. Every answer cites its sources and carries an audit trail.
03 / ENGINE CONTROLS
Safety controls in every part of Kervalt
Each part of Kervalt enforces its own safety checks when you ask a question.
Tafari
Source boundaries
What it does: The system only uses documents you approve.
Why it helps: Prevents off-domain answers by keeping answers inside authorized sources.
What you get: Agents answer only from approved documents, reducing leaks and misinformation.
Griot
Chain reaction detection
What it does: Tracks chains of actions and circular dependencies.
Why it helps: Surfaces unintended consequences before an action is taken.
What you get: Important workflows avoid silent automation failures and compounding errors.
Elimu
Rules as hard checks
What it does: Rules enforce data classification, retention limits, and access control.
Why it helps: Turns compliance requirements into automatic checks that cannot be skipped.
What you get: Rules become automatic guardrails, not manual checklists.
04 / SCENARIOS
Real safety scenarios
Real misuse cases drive how we prioritize controls and measure their effectiveness.
Problem: A support bot is tricked into leaking private information
A clever prompt tricks a customer-service bot into revealing sensitive client details.
Agitate: One breach can trigger fines and lawsuits
Under privacy and sector rules, a single leak can require breach disclosure, fines, and loss of client trust.
Solve: Clear rules + automatic enforcement
Policy layers block the extraction attempt before generation. The system verifies that no restricted field is returned, and the incident is logged with full context for review.
NEXT STEP
Help make enterprise AI safer
Join our safety research program to access test datasets, policy frameworks, and early benchmark releases.