Mustafa Ijaz · CEO, Quixas Technology

I build the systems that catch the expensive mistake before it costs you.

Ten years shipping production software. Now I run two companies building AI operational systems that teams trust with revenue and compliance work: claims intake validation and freight operations intelligence.

$20M+
funding secured by startups running systems I built
10+ yrs
production software
2
companies, one idea
fnol-intake · validation
> new FNOL received · claim #2471
> running completeness checks…
  policyholder identity match
  loss description present
  damage photos missing
  date of loss outside policy period
> 2 issues caught at intake
> follow-up drafted · awaiting your team's review
✓ nothing reaches your adjusters unready

Most operational losses are visible before they happen. A claim file missing photos. A load booked on a lane going soft. The work is catching it while there is still time to act, and that is the only kind of AI I build.

Companies

Two industries. The same expensive moment.

Both companies sit at the point where a preventable problem is about to become a real cost.

CEO · Quixas Technology

FNOL completeness and consistency validation

For property TPAs, IA networks, and claims-authority MGAs

A first notice of loss arrives looking complete, then the field adjuster opens it: no damage photos, a date of loss outside the policy period, a claimant who doesn't match the named insured. Now your team is chasing information you should have had on day one.

Quixas flags incomplete and inconsistent property FNOLs at intake and drafts the follow-up your team reviews and sends. Adjusters open files that are ready, not files they have to chase.

Your people make every call. The platform never contacts a policyholder on its own.

Co-Founder · FreightMind AI

Morning intelligence for freight brokers

For non-asset US freight brokerages

Most brokerages start the day scrambling across a TMS, load boards, and spreadsheets to figure out what matters. FreightMind pulls the day's loads, lanes, and margin signals into one briefing, delivered before the morning starts.

Soft lanes and thin margins get flagged before a load is booked. Margin protected before the load ships, not reconciled after.

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

Track record

Deployed, not demoed.

$20M+ in cumulative funding secured by startups running platforms my teams and I built. Investors fund traction, and traction runs on systems that work.

PropTech · Short-Term Rentals

Short-Term Rental Operations Platform

We developed the AI operations product for a Sydney-based short-term rental technology company, automating the revenue-driving workflows behind sustained MRR growth.

Climate Tech

Carbon Emissions Platform

We developed an AI-driven carbon emissions tracking and reporting platform, turning raw operational data into audit-ready emissions intelligence.

AI Communication

Guest Messaging AI

We built an AI communication platform for a premium hospitality brand, handling customer-facing replies where tone and accuracy carry real business risk.

Sales Automation

AI-Powered Sales Agent

We deployed an autonomous sales agent handling lead engagement in a live pipeline, built for operational reliability over novelty.

How I work

The rules I don't break.

01

Deployment over demos

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

02

No invented numbers

Every figure I use comes from your operation or a citable source. If I can't back a claim, I don't make it. Trust is the product.

03

Humans stay in the loop

AI drafts, flags, and prepares. Your people review, decide, and send. Systems that act alone on your customers are a liability, not a feature.

Contact

If your team is still catching the costly thing by hand, let's talk.

My inbox is open and I read every message myself. If incomplete claims or margin leaks are costing your operation, we should be talking. And if we're not the right fit, I'll say so in the first call.