AI reliability engineering for agent and RAG workflows
EAVAE Labs helps teams with existing agent, retrieval-augmented generation, and model workflows reproduce failures, build evaluation surfaces, and define release gates so engineers can decide what to ship, revise, or stop.
Current start dates are confirmed after scoping.
Last reviewed: July 12, 2026
Founder-led
Scoping and accountability sit with Mohy Mabrouk unless a different delivery model is agreed.
Reusable artifacts
The engagement is framed around evals, failure notes, release gates, and decision records your team can inspect.
Safe brief first
Initial contact asks for sanitized context only, not credentials, secrets, production data, or private repository access.
NDA during scoping
Mutual confidentiality terms can be reviewed before private material is shared.
Recognition
These are the signals that usually mean the team needs an evaluation surface rather than another prompt pass.
The workflow changes, but the team cannot tell whether quality improved.
Failures cannot be reproduced consistently from traces, tickets, or eval runs.
Offline scores do not predict the incidents engineers see in production.
There is no agreed release gate for deciding what ships, changes, or stops.
Flagship offer
A scoped engagement for teams with an existing agent, retrieval-augmented generation workflow, or model pipeline that needs evaluation coverage, failure reproduction, and release criteria.
Starts at EUR 12k. Final scope, payment terms, taxes, and change-order rules are confirmed before work begins.
Work / Proof
Unsupported case studies, anonymous testimonials, exact metrics, and availability claims have been removed from the public UI.
Representative structure - not client data
The sample route shows how evidence can be packaged without pretending the example is a named client result.
View a redacted artifact sample/evals/evaluation_plan.md
Defines target behaviors, known risks, required inputs, and the decision the eval should support.
/analysis/failure_taxonomy.md
Groups reproduced failures by root cause, severity, trigger, and likely owner.
/evals/replay_suite/
Organizes representative tasks or traces so regressions can be tested consistently.
/gates/release_gate_checklist.md
Documents thresholds, blockers, warnings, and review behavior before rollout.
/decision/ship_revise_stop_memo.md
Summarizes the evidence and states whether the path should ship, be revised, or stop.
Process
The workflow is designed so sensitive material is not the first thing requested.
Step 1
You describe the system, failure pattern, available sanitized inputs, and decision needed. No secrets, production data, or private access.
Step 2
We agree the workflow boundary, evaluation target, artifact list, constraints, and whether private access or an NDA is required.
Step 3
Private repositories or sensitive material are handled only after mutual scope agreement and appropriate confidentiality terms.
Step 4
The work focuses on reproducible failures, benchmark or replay coverage, release thresholds, and engineering-readable notes.
Step 5
The final review packages the evidence and recommendation so your team can decide what to ship, revise, or stop.
Adjacent services
Each option is framed by fit and output, not a generic agency menu.
Starts at EUR 3.5k
Use when the team needs a fast read on architecture risk or evaluation gaps before committing a sprint.
Output: Risk memo, evaluation plan, and implementation roadmap.
Starts at EUR 30k
Use after the reliability question is understood and the team needs a scoped implementation path.
Output: Prototype service, integration plan, eval surface, and handoff notes.
Pricing
Deposit, tax, milestone, and change-order details still require owner-supplied policy and are listed in the content checklist.
From EUR 3.5k
A focused review for teams deciding whether a technical direction is worth pursuing.
Flagship
From EUR 12k
The flagship engagement for evals, replay tests, failure analysis, and release gates.
From EUR 30k
A scoped build path once the reliability question and target workflow are clear.
Material handling
The site avoids promising security controls that are not yet documented.
Fit
The best engagements begin with a real workflow and a decision the team needs to defend.
Founder and accountability
The public site now separates known identity details from owner-supplied facts still needed for legal and professional verification.
EAVAE Labs is presented as Mohy Mabrouk's AI reliability engineering practice. The site should add verified professional links, legal contracting identity, location, portrait provenance, and any specialist access model before making stronger authority claims.
FAQ
These answers avoid unverified guarantees and point to the facts still needed where policy is incomplete.
Next step
The first reply should clarify fit, likely scope, and the safest next step. A real form backend is listed as a content and implementation requirement.
What happens next
Your mail client opens a prefilled message. If the problem appears fit for scoping, the next step is either a written follow-up or a 30-minute call. No private repository access is requested here.
