AI SERVICES · AI EVALS & OBSERVABILITY
If you cannot measure it, you cannot ship it. And you certainly cannot operate it.
RankSaga builds the evaluation harnesses, regression suites, drift detectors, and production observability that turn an AI prototype into a system you can operate. The eval framework is what separates a demo from a deployment.
Most AI teams rely on vibes for quality. The model felt better. The output looks fine. The customer has not complained. None of that survives the first regression that lands silently after a model upgrade or a prompt tweak. The eval harness is the only honest signal.
WHY THIS MATTERS
Production AI quality decays. Without measurement, silently.
An AI system in production is not a static artefact. The model gets updated. The prompt gets tweaked. The retrieval index gets re-built. The query mix shifts as users discover new failure modes. Every one of those changes can degrade quality in a way that is invisible to the engineering team and obvious to the user. The eval harness is the only thing that catches it.
RankSaga's evaluation work concentrates on the parts of the system that determine whether quality is observable: a labelled eval set drawn from real queries rather than a synthetic benchmark, the metrics that map to the application outcome rather than to a leaderboard, the regression suite that runs in CI and blocks merges that drop a metric, and the production drift detection that catches a quality regression in hours rather than weeks.
The eval harness is also what makes every other engagement faster. When fine-tuning, retrieval, or RAG work has a labelled eval set as the contract, every change becomes an experiment with a measurable outcome. The work that does not move the metric is dropped early. The work that does is shipped with confidence.
WHAT WE SHIP
Six concrete pieces of work.
01 / Capability
Labelled Eval Sets
Sampled from real production queries, labelled against expected outputs, stratified by query type and edge case. The eval set is the contract between training and production.
02 / Capability
Application-Level Metrics
The metric that matches what the system is supposed to do. Citation correctness, task completion, refusal appropriateness, ticket deflection. Not BLEU. Not generic perplexity.
03 / Capability
Regression Suites in CI
Eval runs on every PR. Quality regressions block merges the same way test failures do. Every change has an attributable contribution to the metric.
04 / Capability
LLM-as-Judge Scoring
Where reference answers are hard to write, LLM-as-judge with calibrated rubrics, sampled against human-labelled gold to measure judge accuracy. Used for groundedness, refusal, attribution, and helpfulness scoring.
05 / Capability
Production Drift Detection
Quality metrics computed continuously on production traffic. Alerting when groundedness, retrieval recall, refusal rate, or task-completion metrics drift outside their bands.
06 / Capability
Operational Dashboards
Per-route quality, per-cohort breakdowns, cost and latency observability, and the runbooks the operating team uses when an alert fires.
HOW WE OPERATE
Build the eval, then build everything else.
01 / Step
Sample, Label, Stratify
We sample queries from production logs, work with subject-matter experts to label them, and stratify the eval set so edge cases are represented. The first deliverable is the eval, before any model work.
02 / Step
Wire It Into CI
The eval harness runs on every PR against the production model. Quality regressions block merges the same way test failures do. Drift between commits is attributable.
03 / Step
Monitor in Production
Eval metrics computed continuously against production traffic. Drift detection, alerting, and the dashboards your operating team uses to triage.
WHAT YOU GET
An eval framework you operate.
01 / Deliverable
A labelled eval set you own
Sampled from your real queries. Stratified, versioned, expandable. Yours at the end of the engagement, not ours.
02 / Deliverable
Regression suites in CI
Eval runs on every change. Merges that drop quality metrics are blocked. Drift between commits is visible.
03 / Deliverable
Production drift detection
Quality metrics computed continuously on real traffic. Alerts wired into your existing on-call. Drift caught in hours, not weeks.
04 / Deliverable
Operational dashboards and runbooks
What your team looks at when an alert fires. What they do next. Who they call.
PROOF
Measurement is the foundation of every RankSaga engagement.
Every RankSaga engagement begins with measurement. The BEIR work that drove 51 percent retrieval lift was the result of an eval harness wired into every iteration. The same discipline runs against customer systems inside customer environments, including the live AI deployment for the Australian Armed Forces.
RANKSAGA · BEIR BENCHMARK · ADF DEPLOYMENT · 2026
- ·Eval-first engagement methodology across every capability line.
- ·Published BEIR evaluation methodology and results.
- ·Production drift detection inside customer VPC, on-prem, and air-gapped environments.
- ·The eval harness is yours at the end of the engagement.
RELATED CAPABILITIES
Where evaluation connects.
Adjacent
Retrieval-Augmented Generation →
RAG systems scored on groundedness, attribution, and refusal appropriateness.
Adjacent
Fine-Tuning & Distillation →
The eval framework is the contract that fine-tuning is measured against.
Adjacent
Agentic Systems & Tool Use →
Multi-step agent evaluation: tool-call correctness, plan quality, end-to-end task completion.
QUESTIONS
What customers ask before we start.
We do not have a labelled eval set. Where do we start?+
By building one. Engagements typically begin with sampling a representative slice of production queries and working with subject-matter experts to label them. The first labelled set is usually 200-500 queries; that is enough to measure meaningful change. The set grows as the system grows.
Can we use LLM-as-judge instead of human labels?+
For the right metrics, yes. Groundedness, refusal appropriateness, and attribution are well-suited to LLM-as-judge with a calibrated rubric. We always sample LLM-judge scores against human-labelled gold to measure judge accuracy and recalibrate when needed. Human labels remain the ground truth.
How is this different from generic LLM observability tools?+
Generic tools give you traces, latency, cost, and prompt logs. Those are necessary and we wire them in. They are not sufficient. The hard part is the application-level quality metric and the regression suite that prevents that metric from dropping. That is what we build, and that is what most teams do not have.
What goes into a regression suite?+
The labelled eval set, the application-level metric (citation correctness, task completion, refusal appropriateness), and a thresholded comparison against the production baseline. A merge that drops any metric outside its tolerance is blocked. Same posture as test failures.
How do you detect drift in production?+
Continuous evaluation on production traffic. Where the application supports it, every response is scored on groundedness or attribution and the metric is plotted over time. Where it does not, we sample a fraction of traffic for LLM-judge or human review. Drift outside the established band fires an alert.
Can the eval framework live inside our environment?+
Yes. For customers in regulated environments, the labelled eval set, the regression suite, and the drift-detection infrastructure all live inside the customer boundary. RankSaga's defence work runs the same evaluation discipline inside air-gapped enclaves.
ENGAGE
If your AI quality is unmeasured, that is the most leveraged place to start.
Almost every AI quality problem we encounter resolves into the same root cause: nobody has measured it. The first deliverable in most engagements is the number that nobody on the team has seen before.