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Sython AI Services

LLM Evaluation and Benchmarking Services

We build practical evaluation systems for LLM and RAG workloads so teams can release faster with confidence. Our benchmark approach combines offline scoring, task-level checks, and live telemetry—and we surface common failure modes early: benchmark overfitting, train/test leakage, LLM-as-judge bias, and distribution shift between staging and live traffic.

Benchmark suite design

Define benchmark sets aligned to your core user workflows, failure classes, and acceptance thresholds.

DeepEval and RAGAs retrieval and answer scoring

Evaluate context relevance, groundedness, and answer faithfulness (DeepEval, RAGAs, and similar libraries) to identify where quality breaks down.

Human-in-the-loop calibration

Layer expert-reviewed golden sets and periodic calibration on top of automated and LLM-as-judge signals so high-stakes workflows keep a credible ground truth.

Layered metrics: offline, online, human review

Pair model-centric scores with operational signals—task success, escalation or deflection, latency SLOs, token cost—and reserve human review lanes for high-risk segments.

Regression and release gates

Set automated pass/fail gates in CI/CD to prevent quality regressions during prompt, model, and retriever changes.

Cost and latency observability

Track p95 latency, token spend, and quality deltas together so optimization decisions are evidence-based.

Frameworks and stack options

We adapt architecture to your constraints, model family, and compliance profile.

DeepEval RAGAs LangSmith OpenAI Evals Arize Phoenix Helicone Promptfoo

Evaluation framework outputs

  • Golden set and synthetic test set generation strategy with explicit leakage controls.
  • Metric definitions for retrieval, generation, and business KPI alignment.
  • Benchmark dashboard with trend and segment breakdowns.
  • Release checklist integrating quality, latency, and spend budgets.
  • LLM-as-judge calibration notes and human review cadence for high-risk workflows.

Example: release gate for support assistant

A customer support assistant shipped frequent prompt changes. We introduced a benchmark gate combining answer correctness, groundedness, and p95 latency budgets. Deployment confidence increased and rollback incidents dropped.

Frequently asked questions

Can we trust LLM-as-judge evaluation?

LLM judges are useful when calibrated with human-reviewed sets and deterministic checks. We combine both to avoid blind spots.

How often should benchmarks run?

At minimum, run before each release and on a regular cadence (daily/weekly) for drift detection in live conditions.

Ready to improve your LLM stack?

We help teams move from demos to measurable business outcomes with robust quality, latency, and cost controls.

Book an LLM architecture call

Related services

LLM Development and Optimization Services LLM Fine-Tuning Services LLM Reinforcement Learning and Alignment Services RAG and CAG Engineering Services Custom LLM Specialization and Optimization Services Financial Services LLM Security and Compliance Support Government LLM Security and Compliance Support LLM Guardrails and Governance Services AI Agent and Workflow Automation Services