AI-native product engineering · Dubai, UAE
Pinnolaris designs, builds and scales AI-first SaaS platforms for founders and enterprises — from the first architecture decision through to a production system running real users.
What we do
We start with ERP and the core business logic — roles, workflows, onboarding, moderation — because that's what every client-facing surface ends up built on. Each stage ships a real, working part of the platform for you to test and sign off, with discovery unfolding alongside the build instead of locked into a spec upfront.
We use AI tooling ourselves to map user flows, draft requirements and prototype screens fast — so discovery moves at the pace of the build, not months ahead of it.
We start with the backend and ERP — roles, workflows, onboarding and moderation — because that's the logic everything else gets built on.
Product, brand and interface design, scoped once real workflows exist to design around — UX/UI, design systems, and the brand and marketing assets that go with them.
Dev-draft through beta on web and mobile: authentication, core flows and backend integration, shipped one working release at a time.
LLM features, retrieval pipelines and autonomous agents woven into flows that are already proven — not bolted on afterward, and not limited to one feature.
Every step ships as working software. You test it, give feedback, and confirm you've received it before the next step starts.
Technical documentation for your dev team and plain-language guides for end users, so the platform doesn't run on institutional memory.
Final stabilization, production deployment and monitoring through go-live.
Free technical support for up to six months after launch, so the platform doesn't go quiet the moment it ships.
Also part of every engagement
A dedicated project lead running sprints, scope and communication, so the team stays accountable end to end.
Provisioning, environment setup and ongoing infrastructure management for the systems your platform runs on.
Hardening architecture, performance and reliability once real users start depending on you.
Bug fixes, dependency upgrades and small improvements on an ongoing basis, beyond the free post-launch window.
Monolith-to-microservices decomposition and framework version upgrades, done incrementally without stopping feature work or betting on a rewrite that never ships.
Industries we know
We go deep in a handful of industries rather than wide across all of them, so the team already understands the domain before the first sprint starts.
Scheduling, staffing, dispatch, workforce marketplaces and contractor management.
Fleet telemetry, routing, dispatch, logistics operations and vehicle management — from real-time monitoring down to last-mile delivery.
Underwriting assistants, document intelligence and compliance tooling where every AI output has to be traceable back to its source.
Patient management, clinical workflow tools and compliant health platforms, built with the data-handling discipline the sector requires.
Engineering capabilities
Complex internal platforms, operational ERP systems and custom enterprise applications.
BPMN, ARIS, process analysis, workflow design and business-process automation.
Multi-sided platforms connecting businesses, partners, contractors and workers, including matching, billing and settlement logic.
APIs, legacy systems, ERP/CRM integrations, payment systems and third-party services.
AI assistants, RAG, document intelligence, agents and AI embedded directly into operational workflows.
Dashboards, analytics, monitoring, reporting and decision-support systems.
Why Pinnolaris
Our engineers work daily with OpenAI, Claude, Gemini, LangChain and LangGraph, and vector stores like Pinecone, Weaviate and Qdrant — inside production systems, not tutorials.
Every engagement gets a dedicated project lead, structured sprint planning and direct access to the engineers actually writing the code.
We're set up for partners who keep building after launch — when you're ready to scale, we already know your codebase and your roadmap.
Our method
We built our own engineering system — the Pinnolaris Engineering System (PES) — because "describe what you want and hope the AI gets it right" doesn't hold up on real, long-lived products. A human architect stays accountable for every consequential decision; the AI is a fast, tireless pair that explores, proposes and executes inside that framework — and every change has to be proven to work, not assumed to.
Map what already exists — the codebase, the constraints, the edge cases — before proposing anything.
Propose an approach backed by what we actually found, not a guess.
Surface every ambiguous call to you and get explicit sign-off before a line of code is written.
Agree upfront on exactly what "working" means and how it gets proven.
Implement only the aligned scope — nothing silently expanded.
Exercise the real flow and show the evidence. Code review alone is never treated as proof.
Selected work
Full write-ups are coming — for now, a look at platforms we've built.
Gig-work marketplace matching people with same-day retail shifts across Russia — job discovery, fast onboarding, same-day pay.
Video-consultation concierge platform for a UAE healthcare provider — patient app, doctor and support workflows, ERP backoffice.
ERP and client portal for a construction-fleet service company — repair workflows, spare-parts logistics, GPS telemetry.
Frequently asked questions
Pinnolaris builds software where AI is part of the core architecture from the start — intelligent search, automated workflows, LLM-powered assistants and predictive features — on top of a proper SaaS foundation: multi-tenancy, billing, auth and cloud deployment.
Because every client-facing screen — client portal, admin panel, mobile app — ends up reading and writing the same roles, workflows and business rules. Building that core first means each later stage ships against logic that already works, instead of a UI that has to be reworked once the real rules show up.
Every change goes through our Pinnolaris Engineering System: explore the existing codebase, propose a plan grounded in what's actually there, get your sign-off on anything ambiguous before a line of code is written, agree upfront on what "done" means, build only the aligned scope, then prove it by running the real flow — not just a code review. A human architect stays accountable throughout; the AI never ships unsupervised.
It depends on scope, integration depth and how much of the AI layer is custom versus off-the-shelf. Share a rough brief and we'll come back with a real estimate rather than a generic range.
It depends on scope, not a fixed number — a focused module can ship in weeks, a full ERP-first platform typically runs a few months. Because we deliver stage by stage, you're testing real working software throughout the build, not waiting for one release at the end.
Yes — our AI integration work is designed to slot into an existing codebase rather than requiring a rebuild, so you keep what already works.
You get free technical support for up to six months after go-live, plus ongoing options after that — maintenance, integration and scaling, modernization work. The platform doesn't go quiet the moment it ships.
Tell us what you're building — we'll reply the same business day.