AI Evaluation
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.
- Regression suites
- Golden datasets
- Human review
AI & AUTOMATION / SYSTEM NODEAI Reliability & LLMOps
Production AI needs evidence, controls, and operational visibility.
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.
Ownership, deployment boundary, acceptance criteria, and handover are defined per engagement.
MODEL LIFECYCLE
WHAT WE BUILD
AI Reliability & LLMOps, engineered as an operating system.
Scope follows the real workflow, environment, failure modes, and ownership model rather than a fixed vendor package.
AI Observability
Tracing and operational telemetry that connects user requests to model calls, retrieval, tools, latency, cost, and application outcomes.
- Distributed traces
- Model/tool telemetry
- Failure analysis
Guardrails & Tool Controls
Application-layer controls for input handling, tool permissions, output validation, policy checks, rate limits, and escalation paths.
- Tool allowlists
- Validation
- Escalation paths
LLMOps & Release Management
Versioning and release workflows for prompts, models, retrieval configuration, datasets, and evaluation evidence.
- Version records
- Release gates
- Rollback planning
AI Security Engineering
Threat-informed controls for prompt injection exposure, data leakage paths, over-permissioned tools, untrusted content, and sensitive logging.
- Threat review
- Least privilege
- Logging controls
DELIVERY SEQUENCE
From unknowns to production evidence.
01
Define Failure Modes
Identify unacceptable behaviors, task-critical criteria, data and tool boundaries, and the evidence required before a release decision.
AI Use Case Evaluation Plan02
Build Evaluation Sets
Create representative examples, adversarial cases where appropriate, scoring logic, and human-review guidance for the target workflow.
Evaluation Plan Eval Suite03
Instrument
Capture model, retrieval, tool, latency, token, error, and application traces with privacy-aware logging choices.
AI Application Operational Traces04
Gate Releases
Compare candidate changes against defined evaluations and operational thresholds before promotion to production.
Candidate Change Release Evidence05
Monitor & Review
Review production traces, user feedback, cost and latency signals, then feed verified failures back into the evaluation set and release process.
Production Improvement LoopTECHNOLOGY SURFACE
Tools selected for the system, not the trend.
Technology follows environment, operating constraints, team ownership, and long-term maintainability.
01Evaluation
Promptfoo · Ragas · Custom eval harnesses · Human review
02Observability
OpenTelemetry · Langfuse · Arize Phoenix · Structured logging
03Runtime
Python · FastAPI · TypeScript · Queue / workflow systems
04Storage
PostgreSQL · Object storage · Analytics stores
05Delivery
CI/CD · Container platforms · Cloud or private deployment
WHERE IT FITS
The same capability changes by domain.
AI Products
Release evidence, tracing, guardrails, and operational review for model-powered product features.
Agentic Workflows
Tool-use controls, action boundaries, trace review, and evaluation for agents connected to business systems.
Enterprise Knowledge
Retrieval and answer-quality evaluation, permissions testing, and ongoing content-freshness review.
Regulated Workflows
Engineering controls and review evidence designed around the applicable organizational and regulatory requirements defined for the project.
TECHNICAL FAQ
Questions worth resolving before delivery.
01Does evaluation guarantee that an AI system is correct?+
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.
02Do guardrails make an LLM secure by themselves?+
No. Guardrails are one layer. We also consider identity, authorization, tool permissions, data boundaries, untrusted content, application validation, logging, infrastructure, and operational response.
03Can you add observability to an existing AI application?+
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.
04What should be versioned?+
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.
OSYSTIC / PROJECT CHANNELOPEN
START A PROJECT
Make AI changes reviewable before they become production incidents.
Share the AI workflow, current failure modes, release process, and operating constraints. We will help define an evaluation and reliability layer around it.