“Which version is better?”
Compare changes against the same examples and criteria, not whichever output looks best today.
AI evaluation and engineering firm
Know where
your AI breaks.
For AI product teams shipping applications, RAG systems, and agents. In 10 business days, establish a quality baseline, investigate failures, test one improvement, and keep a repeatable evaluation.
A 20-minute scoping conversation, arranged by email if there’s a fit.
- 10 business days
- First engagement. One use case, a repeatable evaluation, a clear next step.
- Scoped engagements
- AI evaluation, AI SaaS security audits, and engineering.
- Your stack
- Existing tools where they fit. Artifacts your team keeps.
EVALUATION NOTE / 001
Rubrex internal systemA fluent answer isn’t
A fluent answer isn’t
a reliable answer.
Testing an AI-generated evaluation rubric.
06Test profiles
03Critic lenses
Observed failureFinding
01Generic scoring anchorsRevise
02Overlapping criteriaRevise
03Unsupported specificityRevise
7
prompt fixesfrom the evaluation findings
Deterministic checks + human release gate.
Semantic LLM judge: planned, not live.
EVERY ENGAGEMENTBuilt for teams shipping AI applications, models, and agentic workflows.
- Written scope and quoteAgreed before any work begins.
- Agreed data termsAccess, tools, and retention settled up front.
- Inspectable resultsAgreed criteria, a readout, and a documented handoff.
- A senior teamClose to the work and accountable for it.
01The problem
A better demo isn’t
proof of a better system.
If your team can’t explain what changed, a new prompt or model is another guess.
“Why did our agent fail?”
Separate bad answers from retrieval misses, tool errors, and incomplete tasks. Know what to fix first.
“Can we release this?”
Turn real failures into regression cases. Make the quality bar explicit before the next release.
02Start here
Ten days.
A quality bar you can use.
The Rubrex AI Reliability Sprint. One priority use case, a repeatable evaluation, and a clear next step.
One use case.10 business days · A focused evaluation
For AI product teams and engineering leaders with a working system, real examples, and a quality problem worth solving.
Request an evaluation callScope and pricing agreed before kickoff. Starts once access and inputs are ready.
What we need from you
One technical owner, access to the system and representative data, two working sessions, and timely feedback.
THE WORK, AND WHAT YOU KEEP
- 01
Define the quality bar
Success criteria, an anchored rubric, and a representative evaluation set.
- 02
Establish the baseline
A repeatable procedure or lightweight harness, plus the first evaluation run.
- 03
Find and test a fix
A failure taxonomy, one targeted improvement experiment, and a rerun.
- 04
Make the next decision
Your evaluation artifacts, an executive readout, and a prioritized 30-day plan.
A FOCUSED SCOPE
Not a full product build, unlimited labeling, ongoing monitoring, or a comprehensive security audit. Additional use cases and implementation are scoped separately.
03Selected work
The method, in practice.
Work we can explain and stand behind.
Internal systems and products built by our team, clearly labeled.
Inside an evaluation / Illustrative sample
From an unsupported answer to a testable next change.
Inspect a support-assistant example: the policy, the answer, the finding, and the proposed retest. See the report format before we talk.
01 / Rubrex internal case study
Evaluating the
evaluation generator.
A generated rubric can sound credible and still be impossible to score. We built a quality contract, tested representative cases, and turned the failures into prompt changes.
Read the case study6profiles×3critic lenses→7prompt fixes
✓ Structural validation on every run
✓ Human review before release
○ Semantic judge remains planned
02 / AI product built by the Rubrex team
ResumizeAI
An AI job-application workflow spanning structured generation, editing, document delivery, agent tools, and evaluation gates.
Built end to end, from user workflow to release process.
Request a product walkthrough04Beyond evaluation
Need the system
built, too?
Our senior engineering team builds agentic workflows and production software. A scoped pilot or an embedded pod takes ownership of the work, from implementation to handoff.
Explore engineering engagements Scoped production pilots · Ongoing engineering podsAI SaaS security audits
Test the boundaries.
Trace the data.
Bring the same evaluation discipline to your product’s security, data handling, and AI workflows.
Check access and actions
Assess prompt injection, agent permissions, application security, and whether one customer can reach another’s data.
Follow customer data
Review provider settings, training-use claims, retention, deletion, and third-party sharing against available evidence.
Evaluate the product
Test retrieval, data accuracy, and output quality. Get prioritized findings, proposed fixes, and agreed retests.
05Who you work with
Evaluation depth.
Engineering follow-through.
A senior team with experience in AI training and evaluation, product development, and production infrastructure. Explore the work behind that expertise.
Evaluation and human review
Define useful examples, anchored scoring criteria, and review gates. Investigate failures before choosing the next change.
Work you can inspect
Inspect the internal case study Our internal rubric evaluation: 6 profiles, 3 critic lenses, 7 prompt fixes.
AI product engineering
Build the workflow around the model: structured generation, editing, document delivery, agent tools, and evaluation gates.
Work you can inspect
Explore our engineering work ResumizeAI, an end-to-end AI product built by the Rubrex team.
Production systems and handoff
Bring product and infrastructure experience to scoped implementation. Work in your existing stack where practical and document what your team keeps.
Work you can inspect
See the Sprint deliverables Defined deliverables, working sessions, and a documented handoff in every Sprint.
06A few useful answers
Before we talk.
Can you assess the security and data handling of our AI SaaS?
Yes. We scope AI SaaS security audits covering application access, model and agent boundaries, customer-data handling, and relevant product evaluations. Security work is separate from the Reliability Sprint; the scope specifies tests, access, and deliverables.
Do we need to train our own model?
No. The sprint can evaluate applications built on model APIs, retrieval systems, fine-tuned models, or tool-using agents. We start with a working use case and the behavior you need to measure.
We already use an evaluation platform. Can you work with it?
Yes. A platform can run experiments and collect traces; your team still needs useful examples, scoring criteria, calibrated judgment, and someone to investigate failures. We can do that work in your existing stack where it fits.
What if we already have a rubric and need evaluations run?
We can scope managed execution against an existing rubric, including output grading or preference comparisons where appropriate. Volume, review requirements, cadence, and pricing are agreed separately.
Will the sprint make our system production-ready?
The sprint establishes a baseline, tests one improvement, and leaves a repeatable evaluation and plan. It is not a guarantee of production readiness or a full security assessment. Implementation can follow through a separately scoped pilot.
How do you handle our data?
Before work starts, we agree what data is needed, who can access it, which tools may process it, and the retention and deletion terms. Do not send sensitive datasets, credentials, or customer records through this contact form.
Can we start with engineering instead?
Yes, when the workstream and acceptance criteria are clear. A Production AI Pilot covers one defined workflow; an embedded engineering pod is for ongoing delivery. You do not have to buy an evaluation sprint first.
Research & field notes
A method worth examining.
How to evaluate an LLM application before release
Define success, choose examples, and investigate what fails.
A correct answer can still cite the wrong evidence
Separate answer quality from citation support and faithfulness.
Test the agent, not just its final answer
Inspect outcomes, tools, and consistency across repeated runs.
07Start the conversation
Start with
a scoping call.
Tell us what you’re building and the decision you need to make. If there’s a fit, we’ll email you to arrange a 20-minute scoping call. No system access is needed for this first conversation.
What we’ll discuss
evals@rubrex.ai - Your use case and the problem you’re seeing.
- The evidence you have and what to test first.
- Whether a Sprint, audit, or engineering engagement fits.
WHAT HAPPENS NEXT
- We review your request and whether we can help.
- If there’s a fit, we agree a call time with you by email.
- A written scope and quote before any work begins.