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We build the evaluation and operating layer around AI applications so teams can inspect behavior, compare changes, trace failures, control tool use, and make release decisions using defined evidence.
We scope around the system you actually need to operate, maintain, and operate — not a fixed vendor product or a one-size-fits-all implementation.
Task-specific evaluation suites for prompts, retrieval, agents, tool use, and model changes, using automated scoring where it is meaningful and human review where judgment is required.
Tracing and operational telemetry that connects user requests to model calls, retrieval, tools, latency, cost, and application outcomes.
Application-layer controls for input handling, tool permissions, output validation, policy checks, rate limits, and escalation paths.
Versioning and release workflows for prompts, models, retrieval configuration, datasets, and evaluation evidence.
Threat-informed controls for prompt injection exposure, data leakage paths, over-permissioned tools, untrusted content, and sensitive logging.
Each engagement is broken into defined phases with reviewable outputs. Scope can adapt, but accountability stays visible.
Identify unacceptable behaviors, task-critical criteria, data and tool boundaries, and the evidence required before a release decision.
Create representative examples, adversarial cases where appropriate, scoring logic, and human-review guidance for the target workflow.
Capture model, retrieval, tool, latency, token, error, and application traces with privacy-aware logging choices.
Compare candidate changes against defined evaluations and operational thresholds before promotion to production.
Review production traces, user feedback, cost and latency signals, then feed verified failures back into the evaluation set and release process.
Technology choices follow your environment, operating constraints, team capability, and long-term ownership requirements.
The same technical capability can require very different controls, integrations, and operating models across industries.
Release evidence, tracing, guardrails, and operational review for model-powered product features.
Tool-use controls, action boundaries, trace review, and evaluation for agents connected to business systems.
Retrieval and answer-quality evaluation, permissions testing, and ongoing content-freshness review.
Engineering controls and review evidence designed around the applicable organizational and regulatory requirements defined for the project.
The exact architecture and delivery plan depend on your environment. These answers describe how OSYSTIC approaches the work.
No. Evaluation provides structured evidence about defined behaviors and known test cases. Production controls, monitoring, human review, and domain-specific validation remain important where errors carry material risk.
No. Guardrails are one layer. We also consider identity, authorization, tool permissions, data boundaries, untrusted content, application validation, logging, infrastructure, and operational response.
Usually, yes. The implementation depends on the current model clients, orchestration layer, hosting environment, privacy requirements, and how much end-to-end trace context the application exposes.
For production AI, versioning commonly covers application code, prompts or policies, model configuration, retrieval settings, evaluation datasets, tool schemas, and the evidence used for release decisions.
Share the AI workflow, current failure modes, release process, and operating constraints. We will help define an evaluation and reliability layer around it.