Mustafa Ijaz · Forward-Deployed AI Engineer & Founder

The demo always works. Getting it to work in production is the job.

I run a forward-deployed AI engineering firm and I co-founded an AI product company. The work is the same in both: make production AI measurable and dependable at the level of the job it was actually trusted to perform, then own the engineering that improves it. Inside your systems, on your real cases.

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$20M+

funding secured by startups running systems I built

10+ yrs

shipping production software

4+ yrs

multi-agent systems in production

agent-reliability · eval run

> "correct" defined with the engineering team

> replaying 1,000 real production traces

> scoring against task-success rubric…

task completed end-to-end94.2%

tool call correct97.1%

claims not in source data3.4%

escalated to a human18% of volume

> 2 blocking failure modes ranked by cost

> regression baseline committed · owner assigned

✓ now the next change can ship on evidence

Illustrative. Real numbers come from your traffic, never from a slide.

Competent teams test before they launch. The gap is not testing, it is that a good-looking output and a completed job are different things — and real traffic brings messy inputs, tool failures, human handoffs and edge cases that pre-launch tests never described. Knowing how often the whole task actually succeeds is an engineering discipline, not a demo — and it is the whole of what I do.

What I do

Where I start, and where it usually goes.

Most engagements begin at the first one, because it is bounded, cheap to be wrong about, and worth having even if nothing follows it. The rest is what the evidence earns.

Where most engagements start

Production Agent Evaluation

One live AI workflow · about two weeks

Your AI feature is live, or live to a limited group. It behaves well in the cases anyone looks at. What nobody can state with evidence is how often the complete business task finishes correctly across representative real traffic — so the rollout decision, the autonomy decision and the trust decision all wait.

I establish what correct means for that workflow with your engineering team, measure task-level success on real production cases, and rank the failure modes by what they actually cost rather than how often they are noticed.

You keep three things: a defensible task-success baseline, a ranked failure map, and a regression suite your own team owns and runs after every change.

What the evidence usually earns

Reliability Engineering

The highest-impact failures, fixed

Measurement is only worth the engineering it directs. Once the failure modes are ranked, the work is concrete: tool execution, state and retries, retrieval, human handoffs, guardrails, observability, workflow logic, latency and cost, and the release process that keeps regressions out.

Nothing here is scoped in advance. It is scoped from what the evaluation actually found, which is the only honest way to price it.

For VP Engineering, Head of Applied AI and CTO owners.

The broader practice

Forward-Deployed Production Engineering

Deployment, operational workflow, and AI product work

The wider job, and where relationships tend to end up: owning an ambiguous production problem from discovery through integration, rollout, measurement and operational handoff. Complex workflows that span people, business rules, documents, APIs and existing software.

It includes the operational visibility layer most automation skips — status, exceptions, bottlenecks, confidence, cost and human actions — because a system nobody can see is a system nobody can manage.

Usually through an existing relationship, a referral, or a problem someone has already described to me in detail.

Method

Nine stages. Every engagement, same order.

Most AI work is improvised, which is why most of it cannot be repeated. This sequence is the part I refuse to skip, and it is the reason the second deployment goes faster than the first.

Discover

Find where the value and the failure actually sit, in your environment rather than in a category.

Quantify

Put a number on the problem from your own data. If it cannot be measured, it cannot be claimed.

Bound

Decide explicitly what the system may do, what it must escalate, and where it must stop.

Design

Workflow first, agent second. Deterministic wherever determinism is available.

Prototype on real cases

Your actual records and edge cases, never a curated happy path.

Evaluate

An evaluation harness before scale, so improvement is proven rather than felt.

Deploy

Into the systems your team already works in, with operational visibility by default.

Operate

Watch it under real load, handle what fails, keep the humans in command of the exceptions.

Expand

Only once the first one holds. Designed from the start to be handed over or replaced.

The stages are the same every time; what changes is how much of the ladder an engagement climbs. Most start at stage six, because scoping a build before anyone has measured the system is guesswork with an invoice attached.

Companies

One thesis, two companies.

One sells the deployment discipline as a practice. The other applies it to a single industry as a product. Same conviction underneath.

Founder & CEO · Quixas Technology

Forward-deployed AI engineering for the mid-market

For 50 to 300 employee companies in the US, UK, Australia and selected GCC markets

Quixas makes production AI measurable and dependable at the level of the job it was trusted to perform, then owns the engineering work required to improve and scale it. The entry point is deliberately narrow; the company behind it is not.

We sit in the gap between increasingly capable AI and the messy reality of mid-market operations. Branded providers of this capability are priced and structured for large enterprise programmes, which leaves companies with the same problem and no realistic access to it.

Not a staff augmentation vendor, not an offshore development shop, not a no-code automation shop. We do not sell hours or headcount.

Co-Founder · FreightMind AI

Overnight operations intelligence for freight brokers

For non-asset US freight brokerages

Most brokerages open the day scrambling across a TMS, load boards and spreadsheets to work out what matters. FreightMind reads and ranks the day's loads, lanes and margin signals overnight and delivers one briefing before the first call.

It reads and ranks. It never acts on its own. The broker keeps every decision, and gets the first two hours of the day back to spend on them.

Built with a 17-year freight industry veteran as an equity partner.

Track record

Deployed, not demoed.

PropTech · Short-Term Rentals

Short-Term Rental Operations Platform

Technical ownership of the AI operations product for a Sydney-based short-term rental technology company, automating the revenue-driving workflows behind sustained MRR growth. Still running in production.

Multi-Workspace Agent Deployment

Operational AI Workspaces

Five operational agent workspaces designed, deployed and handed over for a single client, each embedded in a different real business function rather than demonstrated in isolation. Forward-deployed delivery in its purest form. Still in use.

Climate Tech

Carbon Emissions Platform

An AI-driven carbon emissions tracking and reporting platform, turning raw operational data into audit-ready emissions intelligence where the output has to withstand outside scrutiny. Delivered and handed over.

AI Communication

Guest Messaging AI

An AI communication platform for a premium hospitality brand, handling customer-facing replies where tone and accuracy carry real commercial risk.

Sales Automation

Autonomous Sales Agent

An agent handling lead engagement in a live pipeline, built for operational reliability over novelty, with the human boundary drawn before launch rather than discovered after it.

Insurance · Architecture

Claims Intake Validation

A completeness and consistency validation architecture for property first-notice-of-loss intake: flag the incomplete file at intake, draft the follow-up, let a human send it. An architecture study rather than a client deployment, and the design pattern has been reused since.

How I work

The rules I don't break.

Deployment over demos

A system running every day in someone's real operation is worth a hundred impressive prototypes. I optimise for the thing still working six months in.

Evaluation before scale

Nothing gets rolled out wider on a good feeling. If we cannot measure task success on real traffic, we are not ready to scale it, and I will say so.

Bounded autonomy

What the system may do is decided explicitly, in writing, before launch. Escalation to a human is a designed feature, not a fallback.

No invented numbers

Every figure I use comes from your operation or a citable source. If I cannot back a claim, I do not make it. Trust is the product.

Straight answers

The questions I get asked first.

What is forward-deployed AI engineering?

It is the discipline of getting an AI system to work inside one specific company's real operation, instead of building a model or a demo in isolation. The engineer works in your environment, on your data and your systems, and owns the outcome in production: the integrations, the exception paths, the permission boundaries, the measurement, and the handover. The model is rarely the hard part. Everything around it is.

Who do you work with?

Established SaaS and tech-enabled companies, usually 50 to 300 employees, with a real product and engineering organisation. The condition that matters most: a meaningful AI feature, agent or AI-assisted workflow is already live in production or limited release and performs a definable business task. AI matters to the business but is not the entire identity of it. US-focused, with UK, Australia and selected GCC work through existing relationships. General interest in AI is not a fit, and I will say so on the first call rather than three weeks in.

We already test before we ship. How is this different?

It is a different question, not a better version of the same one. Pre-launch testing asks whether the model or the output is good. This asks whether the complete business task finished correctly, across representative real traffic, with the messy inputs, tool failures, retries, human handoffs and edge cases that production introduces and a test set rarely describes. Plenty of systems produce good-looking outputs while the work still fails downstream. If you are already measuring end-to-end task success and gating releases on it, you do not need me.

Who is this not for?

Three groups, and I would rather be direct about it. Companies whose product essentially is the agent, where reliability is core IP and the engineering organisation already exists to solve it. Teams with a mature dedicated evaluation or reliability function, who have already built what I would build. And anything prototype-only, where nothing meaningful is live yet — there is no production behaviour to measure, so there is nothing honest for me to sell you.

How does an engagement start?

With a Production Agent Evaluation: one live workflow, roughly two weeks, paid and bounded. You end up with a defensible task-success baseline, the failure modes ranked by impact rather than by how often they get noticed, and a regression suite your own engineering team owns and runs after every change. It is designed to be worth having even if nothing follows it. Engineering work is scoped afterwards from what was actually found, never assumed in advance. I do not run free pilots.

Are you an outsourcing or staff augmentation agency?

No, and it matters. I do not sell developer hours, dedicated teams, or headcount, and I am not competing on an hourly rate. What is sold is a deployed, measured outcome in production, scoped and priced as a fixed engagement. If a conversation turns into a rate comparison against a body shop, we are solving different problems and I will say so early.

Who actually does the work?

I do, with the technical leadership of my team. Forward-deployed work does not survive being handed down a delivery chain, because the value is in the judgement calls made in your environment. If the engagement grows past what we can personally hold, that becomes an explicit conversation, not a silent substitution.

Contact

Tell me the production AI workflow you are trying to trust.

Tell me what is live, what it is supposed to accomplish, and what is stopping you from letting it run wider. I will tell you what I would measure first and whether a bounded evaluation is worth doing at all. If you already have workflow-level evaluation and regression coverage you trust, I will say so and we can both get on with our day.

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