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Our tech stack

Proven platforms, AI-native workflows, and clean architecture for products that can keep moving.

We combine modern AI tooling with years of practical product development experience. Each project starts with the right architecture, development workflow, and integration strategy so we can iterate quickly without losing control.

Typical stack layers

Laravel Livewire Vue Tailwind Quasar OpenAI Postgres SQLite APIs

AI-native

Agentic development, MCP-ready workflows, and useful AI features from day one.

Pragmatic

Proven frameworks first, custom architecture only where complexity demands it.

Scalable

Simple infrastructure early, clean structure for long-term product evolution.

Backend foundation

PHP with Laravel

Laravel is our preferred backend framework for many web-based products because it runs broadly, has a mature ecosystem, and gives us a reliable base for complex applications.

Strong fit for APIs, admin areas, business workflows, and dependable product backends.
Vertical scaling keeps infrastructure simple through many early and mid-stage product phases.
Queues, jobs, and background tasks support automation, AI workflows, and data processing.
First-party movement around agents, MCP, and modern tooling makes Laravel increasingly relevant for AI-powered applications.

Interactive interfaces

Livewire and Vue

We use Livewire and Vue depending on how much client-side interaction, component reuse, and app-like behavior the product needs.

Livewire keeps dynamic interfaces close to Laravel and speeds up product delivery.
Vue fits heavier client-side interaction, reusable frontend systems, and richer app flows.
Both help us build responsive user experiences without unnecessary technical overhead.

Fast product UI

Tailwind, daisyUI, and Nexus Admin

For MVPs, dashboards, and internal tools, this UI stack helps us move from concept to usable interface quickly.

Clean layouts, reusable patterns, and functional admin areas without slow custom groundwork.
Fast iteration while the product direction is still being validated.
A practical base for testing, adjusting, and improving real workflows.

Mobile reach

Quasar with Capacitor

For iOS, Android, and web apps, Quasar with Capacitor lets us reuse core logic while still accessing native device capabilities.

One codebase for multiple platforms when separate native teams would slow down the start.
Access to push notifications, camera, local storage, and device-specific features.
Useful for MVPs and multi-platform products that need momentum before native specialization.

Measurement

Plausible, Fathom, and Matomo

We prefer data-lean analytics that answer specific product questions without collecting unnecessary user data.

Clear insight into conversion points, user flows, and feature usage.
Privacy-conscious setups that stay lightweight and easier to maintain.
Analytics scoped to decisions instead of dashboards for their own sake.

AI layer

OpenAI, OpenRouter, Azure, and Local LLMs

AI providers are selected by use case, data sensitivity, cost structure, performance needs, and deployment constraints.

Cloud models for high capability, local models when privacy and control matter more.
AI used where it creates real value: automation, decision support, content, interpretation, and workflow acceleration.
Provider flexibility keeps products adaptable as model quality, pricing, and infrastructure change.

Context for AI

Embeddings and Vector Databases

Embeddings and vector search connect AI models with project-specific knowledge so generated output becomes grounded and useful.

Postgres or SQLite vector capabilities depending on product size and infrastructure.
Semantic search, retrieval-augmented generation, knowledge bases, internal assistants, and context-aware automation.
AI that works with actual project data instead of generic answers.

Auditability

Event Sourcing

For systems that need traceability and long-term integrity, event sourcing records the sequence of changes instead of only current state.

Useful for business processes, financial logic, workflow systems, and audit-heavy products.
Makes it possible to understand exactly what happened and when.
Past events can be replayed, analyzed, or used to rebuild projections when requirements change.

Optimization

CP-SAT Solvers

For planning, scheduling, allocation, and optimization, solvers bring mathematically reliable constraint handling into AI-driven workflows.

AI helps with uncertainty, interpretation, and flexible reasoning.
Solvers handle exact optimization when many variables, rules, and limitations must be respected.
Strong fit for hard operational problems where reliable answers matter.

Data collection

Crawlers, Scrapers, and Parsers

Real-world data often lives in websites, PDFs, documents, tables, exports, or legacy systems. We make it structured and usable.

Collection, cleaning, and normalization for applications, dashboards, databases, and AI workflows.
Parsers for fragmented or inconsistent sources that cannot be used directly.
A path from messy information to operational data.

Connected products

APIs and Workflow Integrations

Modern products need to fit into existing tools, platforms, and business processes instead of forcing teams to work around software.

Existing APIs where possible, custom APIs where accuracy and ownership matter.
Integrations with CRMs, internal tools, automation platforms, payments, analytics, communication systems, and AI services.
Software that supports how teams already work while improving speed and control.