Our Technology Stack for Web, Mobile, AI and Enterprise Software

Every technology here is one we have shipped with. A stack is a set of decisions, so what follows is the reasoning behind each one, and the projects they have run on.

How We Choose a Technology Stack

There is no universally correct stack. There is a correct stack for your budget, your timeline, the load you expect and the team who owns it after we hand over. We work through the same questions on every project.

What has to be true in twelve months?

An MVP proving a market and a platform carrying fifty thousand daily transactions are different problems. Building for the second when you need the first is the most common way to spend money badly.

How many platforms, really?

Cross-platform halves the build and most of the maintenance. It costs you same-day access to new platform APIs. If your product does not depend on those, native is a premium you do not need to pay.

Where does the data live, and who regulates it?

Health and payment data change the architecture, not just the security review. That decision belongs at the start, because retrofitting it is where the expensive incidents come from.

Who maintains this afterwards?

If your team takes it over, we build with what they can hire for. An elegant stack nobody can staff is a liability we handed you.

What already exists?

Most projects are not greenfield. The stack has to meet the ERP, CRM and identity provider already in the building, and that constraint usually decides more than preference does.

What is the honest budget?

We will tell you when a cheaper stack is the right one. We would rather scope a project honestly than win it and renegotiate in month four.

Frontend Development

React and Next.js cover most builds. Angular suits large teams that want strong conventions; Vue fits incremental adoption into a codebase that already exists. Everything ships in TypeScript, which stops being optional the moment a product outlives its first release. Next.js earns its place when server rendering genuinely matters for search or first paint — when it does not, plain React is simpler to run. See web development.

ReactReact
Next.jsNext.js
AngularAngular
Vue.jsVue.js
TypeScriptTypeScript
Tailwind CSSTailwind CSS

Backend Development

Node.js for API-heavy and real-time services, Python where data and AI work sit close to the application, Laravel for fast delivery on business systems, .NET and Java in enterprise estates already invested there, Go where raw throughput justifies it. In practice the framework matters more than the language — FastAPI, Django and Express are where these decisions actually land. See Node.js, Python and Laravel development.

Node.jsNode.js
PythonPython
LaravelLaravel
JavaJava
.NET.NET
GoGo
FastAPIFastAPI
DjangoDjango
ExpressExpress

Mobile App Development

Native iOS in Swift and SwiftUI, native Android in Kotlin and Jetpack Compose, cross-platform in Flutter and React Native. Which route is right is a budget and roadmap decision more than a technical one, and we argue it either way before development starts rather than after. See iOS, Android, Flutter and React Native development.

FlutterFlutter
React NativeReact Native
SwiftSwift
SwiftUI
KotlinKotlin
Jetpack ComposeJetpack Compose
AndroidAndroid
iOSiOS

Generative AI and LLMs

OpenAI GPT, Anthropic Claude and Google Gemini for hosted work; Meta Llama and Mistral where cost, latency or data residency argue for open weights. We build so the model sits behind an interface: a price change or a better model next quarter should be a configuration decision, not a rebuild. See AI development services.

OpenAI
Anthropic ClaudeAnthropic Claude
Google GeminiGoogle Gemini
Meta LlamaMeta Llama
MistralMistral
Hugging FaceHugging Face

AI Agents, RAG and Vector Retrieval

LangChain, LlamaIndex and LangGraph for orchestration; Pinecone and Weaviate for vector search; PostgreSQL with vector extensions where the data already lives there and a second database would only add operational overhead. Retrieval quality is what decides whether an AI feature gets trusted or quietly abandoned, so evaluation is part of the build rather than a follow-up. See AI agent development.

LangChainLangChain
LlamaIndex
LangGraph
Pinecone
Weaviate
n8nn8n

Conversational and Voice AI

Vapi, Deepgram, ElevenLabs and Whisper for speech; Twilio and the WhatsApp Business API for the channels customers already use. Voice raises the bar on latency and on handling interruption — a half-second pause that reads as thoughtful in chat reads as broken on a phone call. See conversational AI development.

ElevenLabsElevenLabs
DeepgramDeepgram
Vapi
Whisper
TwilioTwilio
WhatsApp APIWhatsApp API

Machine Learning and Data

TensorFlow, PyTorch and Hugging Face for models; Apache Kafka and Spark for the pipelines that feed them; Grafana for what happens after deployment. AWS SageMaker and Bedrock, and Google Vertex AI, where managed training and serving beat running it ourselves. Elasticsearch, Snowflake and Databricks where the data platform is the actual problem. See AI and ML engineers.

TensorFlowTensorFlow
PyTorchPyTorch
Apache KafkaApache Kafka
Apache SparkApache Spark
ElasticsearchElasticsearch
SnowflakeSnowflake
DatabricksDatabricks
GrafanaGrafana
PandasPandas
JupyterJupyter

Cloud and Infrastructure

AWS, Microsoft Azure and Google Cloud, including their managed AI services. Which one is right is usually settled by your existing commitments, your compliance obligations and where your team already has depth — rarely by a feature comparison. Vercel and Cloudflare for edge delivery and protection. See cloud managed services.

AWSAWS
AzureAzure
Google CloudGoogle Cloud
VercelVercel
CloudflareCloudflare

DevOps and CI/CD

Docker and Kubernetes for packaging and orchestration, Terraform for infrastructure as code, GitHub Actions and Jenkins for pipelines. The goal is releases that are boring: reproducible environments, staged rollout, and a rollback nobody has to think hard about. See DevOps engineers.

DockerDocker
KubernetesKubernetes
TerraformTerraform
GitHub ActionsGitHub Actions
JenkinsJenkins

Databases and Storage

PostgreSQL as the default and MongoDB where the access pattern genuinely calls for a document store. MySQL in existing estates, Redis for caching and queues, Firebase and Supabase where managed services let a small team move quickly. See database architecture.

PostgreSQLPostgreSQL
MongoDBMongoDB
MySQLMySQL
RedisRedis
FirebaseFirebase
SupabaseSupabase

APIs and Real-Time

REST for most services, GraphQL where clients need to shape their own queries, WebSockets for live data, webhooks for event-driven integration. Most integration failures we get called in to fix are not protocol problems — they are missing retries, absent idempotency and no plan for the third party being down. See API development and integration.

REST APIs
GraphQLGraphQL
WebSockets
Webhooks

Payments and Financial Integrations

We build against the published APIs of the gateway you contract with, including tokenisation, webhooks, retries and reconciliation. We are not a reseller or partner of any payment provider — the commercial relationship stays between you and them. See fintech app development.

StripeStripe
RazorpayRazorpay
PayPalPayPal
AdyenAdyen
Plaid

Maps and Location

Google Maps and Mapbox for search, routing and live tracking, with the offline behaviour and the cost model settled early — mapping bills scale with usage in a way that surprises teams after launch. See GPS and location software.

Google MapsGoogle Maps
MapboxMapbox

ERP and Enterprise Platforms

Odoo end to end: implementation, custom modules, integration, migration, version upgrades and support, across Community and Enterprise. ServiceNow for IT and enterprise service management. We also build on Salesforce, and handle migrations and integrations across SAP, Zoho and QuickBooks estates. See Odoo development and Odoo AI integration.

OdooOdoo
ServiceNow
SalesforceSalesforce

CMS and E-commerce

WordPress and Strapi where content teams need to work without a developer in the loop; Shopify and Magento for commerce where the platform already does more than a custom build would. See e-commerce development.

WordPressWordPress
ShopifyShopify
StrapiStrapi
MagentoMagento

Testing, Monitoring and Release

Jest and Detox for automated testing, Fastlane for mobile release automation, Sentry for crash and error monitoring, Grafana for operational dashboards. A product with users needs watching, and the team that built it should be the one watching. See performance optimisation.

JestJest
Detox
FastlaneFastlane
SentrySentry
GrafanaGrafana

Technology Stacks by Application Type

What we would actually recommend for each kind of product, and the point at which it stops being the right answer.

Mobile app

Flutter or React Native, Node.js API, PostgreSQL, AWS, Firebase for push.

Go native when the product depends on new platform capability or the last few frames of performance.

See the service ›

Web application

React with Next.js, Node.js or Python, PostgreSQL, Redis, hosted on AWS or Vercel.

Drop Next.js when nothing on the page needs server rendering — it is a cost with no return.

See the service ›

SaaS platform

Next.js, Node.js or Python, PostgreSQL with tenant isolation designed in from day one, Stripe, Kubernetes once scale justifies it.

Retrofitting multi-tenancy is the most expensive rewrite on this page. Decide it before the first line.

See the service ›

AI product

A hosted or open-weight model behind your own interface, LangChain or LlamaIndex, Pinecone or pgvector, Python and FastAPI.

Wire evaluation in before launch. Without it you cannot tell a model regression from a bad day.

See the service ›

ERP

Odoo, configured first and customised only where the business genuinely differs from the default.

Every custom module is a permanent upgrade obligation. Configuration is nearly always the cheaper answer.

See the service ›

MVP

Cross-platform mobile or a Next.js web app, a managed database, one payment integration, analytics from the first release.

Buy everything you can and build only what tests the idea.

See the service ›

Technologies We Have Run in Production

Each stack below is the one published on that project's own case study, not a general capability claim.

Working With Your Existing Stack

Most of our work is not greenfield. We take over existing codebases, extend them, and migrate them when the cost of staying is higher than the cost of moving. That starts with an honest audit: what to keep, what to rewrite, and what is not worth saving. If your stack is sound we will say so and work inside it rather than sell you a rebuild. See custom software development.

Frequently Asked Questions

How do you choose the right technology stack?

From your requirements rather than our preferences: expected scale, how many platforms you need, compliance obligations, timeline, budget, and who maintains the product afterwards. We put the recommendation and the reasoning in writing before development starts.

What technologies do you use for AI development?

OpenAI, Anthropic Claude and Google Gemini for hosted models, with Llama or Mistral where open weights suit the cost or data-residency constraint. LangChain, LlamaIndex and LangGraph for orchestration, Pinecone or Weaviate for retrieval, and TensorFlow or PyTorch for custom models.

Which technology stack is best for mobile app development?

There is no single best. Flutter and React Native deliver both platforms from one codebase and usually cost less to maintain. Native Swift or Kotlin wins where the product leans on platform-specific capability or peak performance.

Which cloud platforms do you support?

AWS, Microsoft Azure and Google Cloud, including their managed AI services such as Bedrock, SageMaker and Vertex AI. We also use Vercel and Cloudflare for edge delivery.

Can you work with our existing technology stack?

Yes, and often that is the right answer. We audit the codebase and infrastructure first, then tell you honestly whether it can carry what you want to build or whether the cost of staying now exceeds the cost of moving.

Can you recommend a technology stack for an MVP?

Yes. For most MVPs it is cross-platform mobile or a Next.js web application, a managed database, one payment integration and analytics from day one. Buy what you can and build only what tests the idea.

Do you build with Node.js and Python?

Both, extensively. Node.js for API-heavy and real-time services, Python where data and AI work sit close to the application. Laravel, .NET, Java and Go are also in regular use.

Can you build enterprise applications on Odoo?

Yes. Implementation, custom modules, integration, data migration, version upgrades and support across Odoo Community and Enterprise, including AI built natively inside Odoo.

How do you handle security?

Security is designed in at architecture stage: OAuth 2.0 and JWT for authentication, role-based access enforced server-side, TLS in transit, platform keystores on device, and OWASP as the working checklist. Appther is ISO 9001:2015 certified.

Can you take over a project someone else started?

Yes. That begins with an audit of the codebase, the infrastructure and the release process, and an honest view of what is worth keeping.

Need a specific stack?

We adapt to your technology preferences. Let's discuss the right stack for your project.

Discuss Your Project