AI Reliability Engineering

We build reliable
AI systems.

We've shipped. We can fix yours or build yours.
Your AI features in production are silently degrading — costs creep, prompts drift, nobody measures quality. We engineer the evals, observability, and release safety that turn AI prototypes into systems you can actually ship.

Book a Free Strategy Call See How We Work
Accepting Q3 2026 engagements
17+ yrs
Deep production engineering
$50K+
Cloud spend reduced
40%
Faster CI builds
95%
Release confidence (up from 70%)
30%
Cycle time reduction
The Problem

What breaks when AI ships without discipline

If your team has shipped LLM features into production, you've probably hit at least three of these. None of them announce themselves.

Quality decays silently

New prompts ship to prod with no regression baseline. Output drift only surfaces when customers complain in your support inbox.

Token costs leak

GPT spend climbed 30% last quarter. Nobody can attribute the spike to a feature, model, or user segment — so nobody can fix it.

No real eval suite

Manual spot-checks aren't evaluation. Your team is making release decisions on vibes — and shipping anyway because the roadmap says so.

Hallucinations slip through

When the model misbehaves you have no traces, no replay, no audit trail. Just an angry user and a stack of guesses about what went wrong.

What We Do

What we build

Six practice areas. One team. Every engagement starts with a conversation — scope first, quote second.

AI Reliability Engineering

Ship AI without regressions, cost explosions, or quality decay.

  • Eval suites and prompt regression testing
  • LLM observability, trace collection, and replay
  • RAG quality monitoring (precision, recall, faithfulness)
  • Cost governance and per-feature token attribution
  • Release safety: staged rollouts and model auto-rollback
Engagement Options
Snapshot · Audit · Sprint · Retainer

Every engagement is scoped to your problem. We define the brief before any numbers are discussed.

Request a Quote

AI Reliability engagement ladder

Most engagements start with a free Strategy Call. Then you choose how deep we go.

Strategy Call
Free30-min call

We hear your problem, you hear what's possible. No pitch, no commitment — just a straight conversation.

Audit
2–3 weeksFixed scope

Deep technical assessment of evals, observability, cost, and release process. Written report and remediation plan.

Sprint
4–8 weeksBuild & deliver

We build the eval pipeline, observability layer, RAG quality monitoring, and cost governance. Production-ready.

Retainer
MonthlyOngoing

Continuous monitoring, eval refresh, cost optimization, release safety reviews. We're on call.

Built & Shipping

We don't just consult — we ship.

Here's what reliable AI looks like when it goes from architecture to production.

Case Study · MOTRaxis

Vehicle intelligence platform with AI-powered telemetry

MOTRaxis turns raw vehicle telemetry into actionable health intelligence — at the edge, in the cloud, and in dashboards drivers actually use. We engineered the full reliability stack end-to-end.

  • Edge inference layer with sub-second decision latency
  • Eval pipeline catching prediction regressions before deploy
  • Cost-governed RAG retrieval over fleet history
  • Traceable model decisions per vehicle with full replay
  • Zero-downtime model rollouts with automatic rollback
Request architecture walkthrough
motraxis · pipeline.live
Vehicle
Sensors
→
Edge
Inference
→
Cloud
Pipeline
Eval
Suite
↔
Health
Insights
5 stages · 12,847 evals/day · 99.94% uptime
Training & Coaching

We also teach what we build

Three live formats, all built on real production experience. Or join a structured cohort at MindzBrain Academy.

01 / Executive

Communication Coaching

AI-powered speaking confidence sessions for technical leaders. 1:1 format or corporate workshop. Structured around real delivery scenarios.

Enquire →
02 / Engineering

DevOps & Platform Masterclass

CI/CD, Kubernetes, and platform engineering for working teams. Async course or live cohort — both include real-world lab exercises.

Enquire →
03 / Leadership

AI Reliability Workshop

Half-day workshop for CTOs, VPs of Engineering, and AI team leads. Based on the Reliability Audit framework. Half-day in-person or online.

Enquire →
Our Practice

Built on years of shipping — not consulting.

MindzBrain is a reliability-first practice. Every engagement is led by engineers who have shipped AI at scale and have the production incidents, post-mortems, and results to prove it.

AI Systems Reliability

Evals, observability, cost governance, and regression safety for production LLM systems.

Platform Engineering

Infrastructure, CI/CD, cloud operations, and developer experience that scale with your team.

Quality Architecture

Automated test frameworks, quality gates, and performance testing that catch regressions before users do.

Embedded & Edge Systems

Real-time systems, edge AI inference, and hardware-in-loop validation for industrial environments.

Product Delivery

End-to-end product engineering — from architecture decisions to production deployments that hold under load.

Technical Leadership

Coaching, workshops, and fractional CTO engagements that transfer knowledge, not dependency.

01

We build before we advise.

Production experience informs every engagement. Our recommendations are testable hypotheses backed by shipped systems, not slide decks.

02

Evals are not optional.

Measuring AI output quality isn't a nice-to-have. It's the difference between shipping with confidence and shipping with fingers crossed.

03

Reliability is ongoing.

A system that works today can degrade tomorrow. We engineer for the long run — not the demo, not the launch, not the first sprint.

Free Strategy Call

Tell us what you're building.

30 minutes. You bring the problem — we'll tell you what's possible, what's risky, and what we'd do first. No pitch. No commitment.

Book a Free Strategy Call