LLM evaluation and observability from senior AI pod teams

Ryz builds LLM evaluation and observability with dedicated AI pod teams of senior engineers who work in your cloud and repos, alongside your team. The pod turns "it looked fine in the demo" into numbers: labeled eval sets from real traffic, automated scoring in CI, production tracing, and alerts when quality, latency or cost drifts. Every engineer comes from the top 1% of the tens of thousands we interview, and they work on US business hours.

What we build

Most teams ship an LLM feature, watch a few conversations, and then find out about regressions from users. Evaluation is the part that makes every later change safe: a new model, a prompt edit, a retrieval tweak. Typical deliverables:

• Golden eval sets. Labeled question-and-answer sets built from real production traffic and your experts' judgments, versioned in your repo, with slices for the cases that matter (edge inputs, high-value customers, regulated topics).
• Scoring pipelines. Deterministic checks (schema validity, required citations, banned content), reference metrics, and LLM-as-judge rubrics calibrated against human labels so you know how far to trust the judge.
• Regression gates in CI. Every prompt, model, tool or retrieval change runs the eval suite before merge, and the pull request shows the score diff per slice instead of a single average.
• Production tracing. Spans for each model call, retrieval step and tool call, with inputs, outputs, token counts, latency and cost, using OpenTelemetry-compatible tooling so traces live next to the rest of your observability.
• Online quality monitoring. Sampled production outputs scored continuously, user feedback captured from your UI, and drift alerts when answer quality, refusal rate or tool-error rate moves.
• Cost and latency dashboards. Spend per feature, per customer and per model, with p50 and p95 latency, so model and caching decisions are made on data.
• Model comparison runs. Side-by-side evaluation of candidate models or providers on your own eval set before you switch, including cost per correct answer.
• Red-team and safety suites. Prompt-injection, data-leak and policy test cases that run with every release, for features exposed to customers or untrusted documents.

How an engagement works

What happens after you contact us: We look at the LLM features you run or plan to run, how they fail today, and what logs you already keep. If the data is sensitive, we'll sign your NDA first. Then you get the pod members' names with a scoped plan and price.

1. Talk. We agree on what "correct" means for each feature, who can judge it on your side, and which failures are expensive.
2. Match. We propose a pod scoped to your stack, usually a tech lead, an ML engineer with evaluation experience and a backend or platform engineer, with names and a price.
3. Join. The pod works in your repos, CI and standups, with weekly demos of the scores and what changed.
4. Grow. You extend the harness to more features, or the pod hands it to your engineers with runbooks, labeling guides and the dashboards.

Week 1 is instrumentation and a first baseline: tracing on the main LLM path and a first eval set drawn from real traffic. Month 1 typically brings automated scoring in CI with per-slice results and a judge rubric checked against human labels. Month 3 is about running it as a habit: online monitoring, drift alerts, cost dashboards and a release rule that no prompt or model change ships without passing the suite. Actual pace depends on scope, data access and your onboarding.

The stack our teams work in

How we keep evaluations honest

An eval suite can give false comfort as easily as real signal. The failure modes we design against:

• Eval sets that drift from reality. Sets built from invented questions score well and predict nothing. We sample from real traffic, refresh the set on a schedule and keep a frozen holdout so scores stay comparable over time.
• Uncalibrated LLM judges. A judge model has its own biases (length, position, self-preference). We measure judge agreement with human labels before relying on it, and keep humans on the slices where the judge disagrees.
• Averages that hide regressions. A one-point overall gain can hide a ten-point drop on your most important customers. Scores are reported per slice, and gates fail on slice regressions.
• Leakage between test and tuning. Examples used to tune prompts or fine-tune models are kept out of the test set, or the scores mean nothing.
• Tracing that leaks data. Traces can capture personal or confidential data. We redact at capture, set retention limits and keep traces in your own accounts and regions.
• Non-deterministic flakiness. Model outputs vary between runs. We run repeats on critical cases and set thresholds with that variance in mind, so gates fail for real reasons.

Team shapes and cost

Typical Ryz cost is $7,000 to $15,000 per engineer per month, mostly mid-level to senior. Shapes we often see for evaluation work:

• Eval foundation pod: a tech lead plus 2 senior engineers. 3 × $10,000–$15,000 = about $30,000–$45,000 per month (the lead may price above that range, quoted per team).
• Eval plus feature pod: evaluation folded into a pod that is also building the LLM features, around 5 senior engineers. 5 × $10,000–$15,000 = about $50,000–$75,000 per month.
• Single engineer on your team: one senior LLM evaluation engineer working with your existing ML or platform team.

You get a scoped plan, a price and the names of the people who would do the work before anything starts.

Dedicated team or staff augmentation?

Choose a dedicated AI pod when you need the whole evaluation and observability layer built and running, especially across several LLM features. Choose staff augmentation when your team already owns the features and needs a senior engineer who has built eval harnesses before. Many teams start with one engineer and grow into a pod as the number of LLM features grows. Our LLM development and RAG development pods build evaluation in from the first week; this service is for teams that need it added to systems already in production. Production systems our pods have shipped include a fraud detection model validated by the client's own fraud team; see more case studies.

When Ryz isn't the right fit

If you want a self-serve evaluation platform with no engineers involved, buy one of the tools above directly. If you need a formal third-party audit or certification of a model for a regulator, that belongs with a specialist audit firm; our pods build and run the engineering side of evaluation on your team.

Related

• Agentic QA and AI agent testing
• LLM development services
• RAG development services
• MLOps services
• Hire LLM evaluation engineers
• Partners to take an AI pilot to production

FAQ

What is LLM evaluation?

LLM evaluation is the practice of measuring whether a language-model feature gives correct, safe and useful outputs, using a labeled set of real inputs, automated scoring and human review. Done well, it lets you change prompts, models or retrieval and know within minutes whether quality went up or down.

What is the difference between LLM evaluation and LLM observability?

Evaluation tests a feature against known cases before and after changes. Observability watches the live system: traces of every model and tool call, cost, latency and sampled quality scores in production. You need both, because production traffic always finds cases the eval set missed, and those cases should flow back into the eval set.

Can you add evaluation to a system another team built?

Yes. Most evaluation work starts on systems that are already live. The pod instruments the existing path, builds an eval set from its real traffic and adds gates to your CI without rewriting the feature.

How much does it cost?

Typical Ryz cost is $7,000 to $15,000 per engineer per month, so a three-person evaluation pod runs about $30,000 to $45,000 per month. You get a scoped plan, a price and names before you start.

How fast can work start?

After the scoping call we propose a team with names and a price. Most of the timeline depends on scope, access to your logs and data, and your onboarding.

Questions we didn't answer? Email info@ryzlabs.com.