Conquer AI Labs · AI engineering studio
We do one thing.
Build AI that ships.
Autonomous agents, enterprise chatbots, AI voice agents and generative AI systems that reach production — not the slide deck. Grounded in your data, guarded in code, auditable by design. Start with a fixed-scope pilot on your own data in about 30 days.
Free 30-minute opportunity call · a founder replies within 24 hours
01What we build
Four ways AI starts earning for you
Organised by the outcome you buy, not the technology underneath. Every card links to the evidence.
AI-Native Mobile Apps (iOS & Android)
iOS and Android apps with AI built into the experience — assistants, voice, camera intelligence — not bolted on as an afterthought.
AI-Powered Product Development
Full-stack product engineering around the model — so the AI ships inside a product your users actually touch.
Cloud & DevOps for AI
The infrastructure under production AI: resilient pipelines, CI/CD, observability and cloud cost control on Azure and AWS.
02Where to start
Start small. Prove it. Then scale.
Wherever you are, the first step is bounded — in scope, in price, and in risk.
“We know AI could move the needle, but not where to start.”
Free opportunity call
Thirty minutes with a founder. We map your workflows to what AI genuinely does well today, tell you what we'd build first — and tell you plainly if the honest answer is 'nothing yet'.
Book the call“We have a use case and need to know if it works.”
Fixed-scope pilot
A working agent on your own data in about 30 days, evaluated against criteria we agree up front — ending in a go/no-go recommendation and a scaling roadmap. Fixed scope, fixed price, no commitment beyond it.
Scope a pilot“We've validated it. Now it needs to be real.”
Full build & operate
Production engineering by the founders themselves: guardrails, evaluation gates, observability and cost governance included — then we run it with you, or hand it over clean. You own all of it.
Start the build03The evidence
Systems that survive contact with production
Real platforms delivered by our founding team — attribution stated plainly on every one, limitations included. That candour is the point.
Flagship mobile build iOS & Android in development
Sahtain صحتين — point your camera, get honest calories
An Arabic-first AI calorie tracker built by our founders. The vision model is a commodity — the curated truth it answers from is the product.
Snap a plate, get honest macros
A vision model reads the photo; a hand-curated Saudi dish database turns its guess into a defensible calorie range — kabsa, mandi, shawarma.
Arabic-first, RTL-native
Designed right-to-left from the first screen, with Arabic dish search — not an English app with translations bolted on.
Metered AI, honest billing
Free scans are quota-limited, the price shows before signup, and the quota's race condition is proven safe on a real database.
04How we work
Named artifacts. Real gates. No leap of faith.
Every stage ends in something you can inspect, and the build only advances through a decision you control.
Discovery & use-case definition
We separate what you asked for from what you actually need, and put numbers on it before anyone writes code.
Design & fixed scope
The architecture, the guardrails and the evaluation criteria — agreed before the build, so the pilot has a finish line.
Build & evaluate
Hardest parts first, demos every week, and an eval suite growing alongside the system — quality measured from day one, not asserted at the end.
Validate & launch
A formal acceptance gate against the KPIs from step one, then a phased rollout — a go/no-go decision, made on evidence, that you control.
Operate & grow
Monitoring, spend governance and iteration after launch — because an AI system is a living thing, and the second month's answers matter as much as the demo's.
You own everything
Code, prompts, evals, infrastructure — in your repos and your cloud. No lock-in engineered in.
Founders do the work
No hand-off to juniors after the sales call. The people you meet are the people who architect, build and answer.
We say no early
If AI is the wrong tool for your problem — or we are the wrong team — you hear it in the first call, free.
Honesty over advocacy
Every case study on this site lists what the system does not do. Expect the same candour about yours.
05How we build with AI
AI at every stage. Engineers in control.
AI accelerates every stage of our delivery — and a senior engineer owns every decision it touches. That combination is why the systems we ship are fast to build and safe to run.
AI across the lifecycle
Agents support planning, coding, testing and review inside our own delivery pipeline — we run the SDLC Agent Platform we sell.
Engineers stay accountable
Senior engineers direct the agents, make the critical decisions and validate every output. A human gate sits wherever a wrong action costs real money.
Guardrails in code, not prompts
Destructive actions are blocked before the tool call runs, refusals are deterministic, and budgets are hard-capped. Hope is not a control.
Quality that is measured
Gold-set evaluations gate releases, so a prompt change is tested like a code change and quality drift is caught before your users see it.
06Who you work with
You talk to the people who build it
Large agencies sell you a partner and staff you with juniors. Conquer AI Labs is two founders who architect, write the code, and answer the phone.
Co-Founder · AI Engineering
Founding team
Senior AI engineer with 7+ years in software engineering, five of them delivering remotely to North American clients. Builds generative AI systems that hold up in production: multi-agent orchestration, agentic RAG, LLM fine-tuning, and the LLMOps discipline around them on Azure and AWS.
Most recent work: a compliance-grade multi-agent platform for a regulated industry — four agents under supervisor orchestration, human approval gates on high-risk actions, and a cryptographically signed evidence trail an auditor can verify independently. Before that, a multi-tenant RAG platform answering from enterprise documents, tables and figures with a citation on every answer.
Starts with discovery rather than code: sit with the client, separate what they asked for from what they need, turn that into a design, then build and ship it — on several engagements inside the client's own Azure tenant, through their security review.
- 9 specialised AI agents in production across two platforms
- LLM spend cut 35% on one platform, no measured quality regression
- Agent task success up 30% after an orchestration rebuild
- Delivered inside client tenants, against client security reviews
Co-Founder
Founding team
This founder's profile and selected projects are being added to the site.
Senior-only, end to end. The team that scopes your project is the team that ships it — no hand-off, no telephone game, no junior bench learning on your budget.
07For the technical evaluator
The stack, stated plainly
Everything here is in real production use by this team. Ask the chatbot about any of it.
Proof, live on this page
Don't take the site's word for it. Interrogate the chatbot.
The assistant in the corner is our own engineering: grounded in this site's content, a citation on every answer, and a refusal — with a route to a human — when it doesn't know. Ask it something we don't cover and watch it decline instead of improvise. That behaviour is what we sell.
Ask it something hard08Questions buyers actually ask
Straight answers, up front
How fast can we have something working?
A scoped pilot typically goes from kickoff to a working system on your data in about 30 days. The first demo lands earlier — we build the hardest part first and show it weekly.
Who actually does the work?
The founders. There is no bait-and-switch to a junior bench after the sales call — the people who scope your project are the people who architect it, write the code and answer your messages.
Do we own what you build?
Entirely. Code, prompts, evaluation suites and infrastructure live in your repositories and your cloud accounts. We design for a clean handover from day one, then most clients keep us for the operate-and-grow phase anyway.
How do you stop AI systems from making things up?
In code, not in hope: retrieval scores gate whether the model may answer, weak evidence reaches it labelled as weak, refusal is a deterministic outcome with a human handoff, and every answer carries its source. The chatbot on this site is built that way — ask it something we don't cover and watch it decline.
Can you work inside our cloud and security review?
Yes. Several engagements have been delivered entirely inside the client's own Azure tenant, against their data, through their security review. AWS equally. Your data does not train anyone's models.
What happens after launch?
The part most agencies skip: monitoring for quality drift, gold-set regression tests before any prompt change, cost caps so spend cannot surprise you, and iteration driven by what real users actually ask. AI systems are living systems; we price and plan for that from the start.
Tell us the pain point. We'll tell you honestly what AI can do about it.
A founder replies within 24 hours. If the answer is 'AI is wrong for this', you'll hear that too — free either way.