AI That Works.
Software That Lasts.
Xlorit is a founder-led AI development agency, started in 2023 on one premise: AI and software should hold up long after launch, not just on demo day.
“An AI demo takes a weekend. AI that holds up with real users, real data and a real budget takes engineering. That's the part we do.”
MOHAMMED IBRAHIM · FOUNDER
Built for the Day After Launch
Over 7+ years of building software, I kept seeing the same pattern. Projects looked fine at the handover demo, but a few months later the team couldn't add a single feature without something else breaking. The problem was rarely the developers. It was an incentive model that rewards shipping fast over building well.
AI made that pattern sharper. A convincing AI demo now takes a weekend, so plenty of teams ship one, and then watch it give wrong answers, break the frontend or run up costs once real users arrive.
I started Xlorit to work differently. We take fewer projects, spend more time on architecture upfront, and treat AI like any other production system: validated, tested and monitored. And you work directly with me, the person accountable for what we ship.
Reliability Is the Product
Most AI projects stall after the demo. We engineer AI the way we engineer any production system, with the checks that keep it working once real users arrive.
Validated Outputs
Every model response is checked against a defined structure before it reaches your users or your frontend. Malformed answers are caught, not shipped.
Evaluations, Not Vibes
Features are tested against an evaluation set of real cases, so we can measure whether a prompt or model change made things better or worse.
Guardrails
Clear limits on what the AI can say and do, with a person in control of the actions that matter.
Monitoring & Cost Control
Quality, latency and spend are tracked after launch, so the system stays useful and the bill stays predictable.
Your Data Stays Yours
Your data is never used to train models. We choose the model for each task on accuracy, speed, cost and data requirements.
Your Cloud, If You Need It
When security or compliance requires it, we deploy the AI system inside your own cloud account.
Built With AI. Owned by People.
AI makes us faster. It doesn't make the decisions. Every system we ship has a named person accountable for it, and you know exactly who that is.
AI does the heavy lifting
We use AI-assisted engineering for drafting code, writing tests and keeping documentation current, so more of the time goes into the decisions that matter.
People own the architecture
Data boundaries, system design and trade-offs are decided by a person who understands your business, not generated and hoped for.
Every change is reviewed
Nothing reaches your codebase without a human review. You see the pull requests, the tests and the builds in your own repository.
You talk to the builder
No account managers translating technical detail. The person designing your system is the one on your calls, with weekly demos of working features.
Opinionated Engineering Beliefs
We guide our daily decisions by strict technical standards rather than vague corporate catchphrases.
Architecture decisions made early determine outcomes made late.
We set clear system boundaries, data models and integration contracts on day one. When the foundations are right, features can change later with minimal friction.
Clarity in code is not optional. It's the product.
Code is read far more than it is written. We deliver type-safe, cleanly structured, documented codebases that your own team can inherit and extend.
We don't take shortcuts that become your problem in 6 months.
Shipping without tests or validation creates compounding debt. We build test suites and automated deployments so your application stays stable under real load.
AI is a production system, not a feature flag.
A model's output is untrusted input until it's validated. AI features get the same tests, monitoring and failure handling as the rest of your stack.
Grounded in Metrics
7+
Years Building Software
10K+
Active Daily Users
50%
Fewer Support Tickets With AI
95%
AI Flagging Accuracy
Proof From Real Engagements
We'd rather show the work than quote ourselves. Here are outcomes from real engagements, AI work first.
PropTech Revenue Intelligence Platform
0 → fully shipped SaaS: 6 modules, 60+ features, 500,000+ records synced
Built a multi-integration PropTech SaaS from zero, with a 24x7 multilingual prospect AI agent and a sync layer that unifies AppFolio, Rent Manager and Rentvine data and catches its own failures.
AI-Powered Event Marketplace
Structured AI output enforced: no malformed responses reaching the frontend
An all-in-one event marketplace connecting vendors, planners and customers, where AI search and a vendor copilot had to work reliably in production, not just in demos.
Teams Call Quality AI Analyzer
50% fewer support tickets, with 95% AI flagging accuracy
Every Microsoft Teams call quality complaint meant a support ticket and a manual investigation call. We built an AI monitoring layer that flags real issues before they become tickets.
Indian Grocery Ecommerce Platform
$5,000+/month in lost revenue recovered, 98.27% crash-free
A US online grocery platform for the Indian diaspora had a webview app with no crash visibility and a silent billing flaw. We rebuilt the foundation and shipped native apps on top of it.
Enterprise Appraisal Management System
Delivered production-ready in 2 months for 500+ workers, then awarded a second project
A multinational agribusiness needed its tea estate and factory worker appraisals off paper and spreadsheets, live before a fixed appraisal cycle just two months away.
B2B Credit Card Payment Platform
0 → fully shipped app, backend and admin panel, with gateway and OTP fallback
An early-stage FinTech startup wanted businesses to pay rent, GST and vendors by credit card. We built the full platform, and proposed the architecture that made it production-safe.
Unified Infrastructure Monitoring Dashboard
One live view across all regions, and 40% lower storage costs
An enterprise's IT teams watched FreshService, Zscaler and other tools separately, so outages slipped through. We built one real-time dashboard for the head office, and caught a hidden cost problem along the way.
Faculty Recruitment Management System
75% less screening time, validated at 10,000+ concurrent users
One of India's most prestigious arts and science colleges screened 800+ faculty applications by hand. We digitized the entire hiring lifecycle and load-tested it to 10,000+ concurrent users.
Questions About Xlorit
Who we are, how we use AI and how to start working together. If your question isn't here, reach out. Every message gets a personal reply.
Contact us for moreXlorit was founded in 2023 by Mohammed Ibrahim, who has 7+ years of experience building software. It's a founder-led agency: the person responsible for your architecture is the person you talk to, with no account managers in between.
Both. We're an AI development agency: AI agents, RAG, copilots and automations lead our work. Web Development, Mobile Application Development and UI/UX Design remain core services, because most AI features need a solid product around them.
We focus on AI that works in production, not demos. Every AI feature ships with structured output validation, evaluations against real cases, guardrails, monitoring and cost control, so it holds up once real users arrive.
Yes. We use AI-assisted engineering to move faster on drafting, testing and documentation. A person still makes every architecture decision and reviews every change before it reaches your codebase.
No. Your data is never used to train models. Where your security or compliance needs require it, we can deploy the AI system inside your own cloud account.
Book a free discovery call, or start with a fixed two-week AI Discovery Sprint: a use-case audit, a working prototype and a clear go or no-go before you commit to a full build.
If this sounds like the team you want building your AI, let's talk.
Book a free 30-minute call directly with the founder. We'll look at your system and tell you honestly whether AI is the right answer, and what it would take to make it work in production.