Real value, measurable impact, enterprise-grade reliability
Removes noise before it reaches the model and processes only what matters — up to 50% lower token spend with improved output quality.
Cross-checks facts, dates, and sources, filtering out conflicts and stale content so every answer is grounded in verified, current information.
Each validated answer teaches the system how to do better next time - adaptive rule refinement, not retraining — for steadily improving accuracy.
Analyses vague inputs, tests several interpretations, and strengthens them with past context — repairing gaps and keeping only evidence-backed results for clear, confident answers.
Every element in an answer is logged with its source and reasoning path, giving clear visibility and audit-ready evidence for clients, regulators, and internal teams.
Quality gates catch conflicts, bias, and policy breaches before any model call — stopping errors early, saving compute, and reducing operational risk.
Validated truths stored in enSmaller's knowledge graph are reused automatically, ensuring every user and model call returns the same verified, policy-aligned information.
Outputs arrive ready for dashboards, APIs, and documentation pipelines — no manual clean-up, faster rollout, and effortless integration with existing systems.
Applies policy rules, detects contradictions, and protects authoritative statements before release — simplifying compliance with GDPR, ISO 42001, and upcoming AI-Act standards.
Performance and cost data feed a live analytics layer that fine-tunes future runs — optimising budget, model choice, and retrieval scope for predictable cost and measurable ROI.
We're currently onboarding selected enterprise partners. If you're exploring AI optimisation, governance, or cost control, contact us to discuss early access.
Get Early Access