Evaluation-driven
development for AI
systems in
production

Roro works with your team to design and operationalize the evaluation layer so every prompt change, model update, and agent release is a measured decision, not a guess.

Trusted by product and technology teams

L'Oréal
SkinCeuticals
Precision Pro
Luxer One
Harbor
+ many more

What we do

Most teams don't know when their AI starts failing

Logs, traces, and dashboards tell you something broke. They don't tell you why, when it started, or how to stop it happening again. That's the gap evaluation driven development fills.

AI Quality Review — Production Snapshot

Where AI quality breaks

Logs don't explain what went wrong

Failures show up in dashboards, but root cause still takes hours to understand.

Prompt changes create blind spots

A small prompt update can improve one case and silently break another.

No baseline to compare changes against

Teams ship changes without knowing if quality improved or regressed.

Production failures are hard to reproduce

Bad responses are difficult to trace back to the exact prompt, context, tools, and model behavior.

The Service

How Roro helps your team

A hands-on service engagement to design, implement, and operationalize evaluation for your AI workflows.

Evaluation System Setup

We define what quality means for your AI workflow, create test cases, select metrics, set baselines, and set up repeatable evaluation checks.

Data Transformation

We turn production examples, edge cases, support issues, and expert feedback into structured datasets your team can use for evaluation.

Agent Reliability Support

We help evaluate tool use, multi-step reasoning, retrieval behavior, memory, and handoffs so agent failures become easier to trace and improve.

Engagement Path

01

Diagnose

We review your AI workflow, current failures, logs, prompts, retrieval, traces, and evaluation gaps.

02

Define

We define quality criteria, risk areas, datasets, metrics, and baselines.

03

Implement

We set up evaluation checks, regression workflows, tracing/replay processes, and reporting.

04

Operationalize

We hand over documentation, reporting cadence, and recommendations your team can continue using.

Measure first, then improve with confidence

Every engagement gives your team a practical evaluation workflow, clear reporting, and decision-ready visibility into what changed, why it changed, and what to fix next.

Weekly Quality Snapshot and RAG Quality Review dashboards

Not another AI dashboard

Roro is not a self-serve product your team has to figure out alone. We work with your team to design and implement the evaluation workflow that fits your AI system, your risks, and your production environment.

What Roro helps set up:Metrics · Agent evaluation · Regression checks · Datasets · Replay workflows · Reporting

FAQ

01Is Roro a software product we need to buy?

No. Roro is a service partner. We work with your team to design, set up, and operationalize evaluation workflows using the tools and systems that fit your environment.

02Do we need to already use DeepEval?

No. Roro can work with DeepEval, your existing evaluation stack, or help you choose the right setup. The service is focused on creating a practical evaluation workflow, not forcing a specific tool.

03We already have some evaluations. Can Roro work with them?

Yes. We can review your current evaluations, identify gaps, improve coverage, add baselines, and make the process easier to run continuously.

04What does the engagement include?

A typical engagement includes an evaluation audit, failure pattern review, dataset preparation, metric selection, baseline setup, regression checks, reporting structure, and recommendations for ongoing improvement.

What's Included

A clear scope, nothing left undefined

Typical Service Deliverables

Every engagement gives your team a practical evaluation system they can understand, run, and improve, not just a one-time audit or generic report.

01

Evaluation Audit

Review current AI workflow, failure points, prompts, retrieval, traces, and existing evaluation coverage.

02

Golden Dataset Creation

Convert real examples, edge cases, and known failures into structured test cases.

03

Metric Selection

Choose metrics that match your actual risks, not vanity scores.

04

Baseline Setup

Create a reference point so future prompt, model, retrieval, and agent changes can be compared.

05

Regression Checks

Set up checks that catch quality drops before changes go live.

06

Failure Replay Workflow

Create a process to reproduce production issues and identify root causes.

07

Reporting Loop

Define weekly or monthly reports that show what changed, what improved, and what still needs attention.

08

Team Handoff

Document the workflow so your team can continue using it after the engagement.

Start with a diagnostic call

Tell us a little about your AI workflow. Roro will review where you are today and suggest the right evaluation setup for your team.

No self-serve product signup. Roro works with your team as a service partner.

The Right Foundation Changes Everything.

Start with a diagnostic call. We'll review where your AI system is today, identify what's missing from your evaluation layer, and tell you exactly what it would take to fix it before you commit to anything.