Measure the failure mode first
We start with the behavior that needs to improve, the slices where it fails, and the metrics that decide release readiness.
Sython AI helps teams design, tune, evaluate, and operate LLM systems where quality, latency, cost, and governance all matter.
Engagements are scoped around concrete targets: fewer unsupported answers, tighter p95 latency, lower token spend, cleaner audit evidence, or safer rollout of high-risk workflows.
Architecture, model routing, serving optimization, and release planning for LLM systems.
Dataset curation, supervised tuning, adapter workflows, and rollout gates for domain-specific behavior.
Preference-data strategy, reward modeling, and policy optimization when supervised tuning is not enough.
Hybrid search, reranking, citation design, and context packing for systems that need grounded answers.
Golden sets, automated scoring, human calibration, and release gates for quality, latency, and cost.
Prompt, tool, retrieval, and model-routing systems tailored to domain workflows.
Prompt-injection defenses, PII handling, moderation, policy gates, and audit-friendly controls.
Tool-using workflows with bounded autonomy, tracing, approval gates, and failure-mode testing.
We start with the behavior that needs to improve, the slices where it fails, and the metrics that decide release readiness.
Fine-tuning, retrieval, agents, and model routing are selected when benchmarks show they beat simpler baselines.
Delivery includes logging, quality gates, latency and spend budgets, rollback criteria, and clear owner handoffs.
We document sensitive-data handling, retention boundaries, traceability, and routing rules early so security and compliance stakeholders can review the system before live traffic.
Tell us about your model, your data, and the outcome you need to hit. We will come back with a concrete scope, practical evaluation gates, and a realistic timeline.