Decide whether a forecast is safe to act on.
The trust layer that scores evidence, applies policy gates, and leaves an audit trail.
Tollama Trust attaches behind Tollama Core for forecast-driven workflows. It turns benchmark artifacts, uncertainty signals, and routing evidence into trust scores, review steps, policy gating, and compliance-ready records before downstream actions fire.
A forecast alone is not operationally safe. These are the gaps that appear when model outputs start driving positions, interventions, or customer-facing actions.
A forecast exists, but nobody can see the benchmark, uncertainty, or routing context behind it.
Scores may exist, but there is no policy telling the system when to allow, escalate, or block action.
Low-confidence outputs can still trigger production behavior, and the blast radius is only obvious after failure.
Teams cannot reconstruct who approved what, why they approved it, or which evidence was attached at the time.
Trust thresholds drift over time, but governance rarely updates with fresh production evidence.
Attach this stack behind Tollama Core when forecast outputs start driving real actions. Each layer adds a trust dimension, from policy enforcement and uncertainty to reviewability and audit.
Policy-as-code enforcement, identity-aware review paths, and approval gates. This is the layer that decides whether a forecast can trigger action.
Embedding-based drift monitoring that flags when forecast inputs move away from baseline conditions before production behavior degrades.
Distribution-free statistical guarantees for uncertainty-aware forecast decisions. Know how wide the error envelope is before approving execution.
Feature contribution analysis with dual-seed stability so reviewers can see which inputs drove a forecast and whether the explanation is stable.
Value-based guardrails beyond formal rules. Apply transparency, fairness, and human-oversight requirements before high-stakes forecast actions go live.
Trace, benchmark, review, improve. The production loop keeps trust thresholds and escalation paths aligned with fresh evidence over time.
Tollama Trust maps forecast-decision workflows to regulatory requirements as a design constraint, not an afterthought.
Transparency (Art.13), human oversight (Art.14), and accuracy/robustness (Art.15) requirements mapped to trust layers with automated evidence collection.
Reliability assessment (§31), transparency obligations (§32), and impact assessment (§23) compliance with continuous monitoring and audit trails.
Start with benchmark bundles and routing manifests from Tollama Core. Then expose the same trust decisions to review systems, APIs, or agent frameworks when needed.
Consumes benchmark artifacts from Tollama Core as the trust-scoring input surface.
Turns routing recommendations into allow, escalate, or block decisions with policy reasons attached.
Expose trust verification to tools, review consoles, and operator workflows.
Optional integrations for agent workflows after the Core path is already producing stable evidence.
Use Tollama Core to generate evidence. Use Tollama Trust to decide whether that evidence is strong enough for production action.