Technologies
Engineering stack for products that have to work.
Backend systems, SaaS platforms, mobile products and cloud infrastructure. We choose the stack after the problem is clear — and every page here tells you when it is the wrong choice too.
- React.js
- Next.js
- React Native
- Node.js
- Python
- FastAPI
- Cloud & DevOps
- MERN Stack
- MEAN Stack
- MongoDB
- PostgreSQL
- Redis
- Vector Databases
- AI & LLM Engineering
- LangChain
- LlamaIndex
- Haystack
- CrewAI
- LiteLLM
- vLLM
- Ollama
The stack is the answer. It is never the question.
- React
- Next.js
- Node.js
- TypeScript
- React Native
- Flutter
- MongoDB
- PostgreSQL
- Redis
- Express
- NestJS
- GraphQL
- AWS
- Azure
- GCP
- Docker
- CI/CD
- RAG pipelines
- Vector search
- LLM evals
Architecture first
Technology decisions connected to architecture.
A stack is only as good as the architecture underneath it. Every technology below is applied through the same delivery discipline.
- 01
Scalable APIs
Secure APIs with performance and observability built in.
- 02
Event systems
Queues, retries, async jobs and resilient flows.
- 03
AI infrastructure
RAG, vector search, orchestration and guardrails.
- 04
Distributed data
Models, caching, analytics and data pipelines.
- 05
Cloud delivery
CI/CD, containers and infrastructure as code.
Technology fit guide
Which technology fits which business need?
Read it by what you are building, not by what is popular. The last column is the one most agencies leave off.
| What you're building | Where we'd start | Why | What we'd talk you out of |
|---|---|---|---|
| Client portal or admin dashboard | MERN Stack | Roles, forms and data views in one connected JavaScript stack | A CMS — workflows are the product, not content |
| Marketing site where search drives revenue | Next.js | Server-rendered HTML and Core Web Vitals as a requirement | A client-side SPA Google has to execute to read |
| App on both iOS and Android | React Native | One codebase, 90%+ shared, one team shipping both stores | Funding two native teams for feature parity |
| Ledger, payments or eligibility rules | PostgreSQL | Constraints and transactions enforce correctness in the database | A document store bent into relational shape |
| Catalogue or product data still changing shape | MongoDB | Documents model how the product actually reads its data | A migration every sprint to keep a schema in step |
| Answers over your own documents | Vector search + LLM | Retrieval grounded in your content, with citations and evals | A chatbot with no retrieval, guessing confidently |
| Slow pages under real traffic | Redis | Caching and queues absorb load before it reaches the database | Scaling the database to serve the same query repeatedly |
| An assistant that takes actions, not just answers | LangChain | Tool calling and multi-step control flow you can read and test | A framework wrapped around what is really one prompt |
| AI where data cannot leave your network | vLLM | Open-weight models served on your own infrastructure | Buying GPUs before the volume maths says to |
| AI spend rising with nobody able to attribute it | LiteLLM | Routing, per-team budgets and usage visibility in one place | A proxy in front of a single model doing a single job |
| Python AI capability behind a product API | FastAPI | Typed contracts and generated docs at the service boundary | A second runtime added where Node would have done |
| Deterministic rules, audited and provable | Plain code + PostgreSQL | Correct every time, for a fraction of the running cost | An LLM — the fashionable answer, and the wrong one here |
Technology categories
Grouped around real delivery needs, not trend chasing.
Every technology here has to earn its place against a practical goal — a workflow, a search result, an app in two stores, a record that has to reconcile.
Frontend and mobile technologies
03Interfaces, app screens, component systems and search-visible page delivery.
Jump to groupBackend and infrastructure technologies
04APIs, business logic, authentication, and the infrastructure it all runs on.
Jump to groupFull-stack development
02Complete delivery from interface through API to data and admin.
Jump to groupDatabase technologies
04Where the records live — documents, relational data, caching and embeddings.
Jump to groupApplied AI and LLM engineering
08Orchestration, retrieval, routing and model serving — plus the reviewed pipeline we build everything through.
Jump to group
All technology pages
Explore each stack or tool in more detail.
21 pages, each connecting the technology to real service work and real engagements. Every one of them carries:
- Practical selection guidance
- Where it makes sense
- Where we've shipped it
- Pairings & services
Technology group
Frontend and mobile technologies
Frontend and mobile choices decide how a product feels, whether search engines can read it, and how much a new screen costs to add two years in.
React.js
Component-driven interfaces for dashboards, portals and products with real interaction depth — not brochure sites.
React · TypeScript · Component architecture
Explore React.jsNext.js
Server-rendered React for products where search visibility, first paint and Core Web Vitals are business requirements, not nice-to-haves.
Next.js · React · Node.js · TypeScript
Explore Next.jsReact Native
iOS and Android from one codebase — cross-platform mobile without funding two native teams, sharing logic with your web app.
React Native · Expo · TypeScript
Explore React NativeTechnology group
Backend and infrastructure technologies
Backend choices matter the moment a product needs APIs, authentication, integrations, background work — and somewhere reliable to run them. Node carries the product; Python joins it where the AI and data ecosystem makes it the better tool.
Node.js
Event-driven APIs and services — the backend layer under most of what we deliver, from nine-week MVPs to high-throughput platforms.
Node.js · Express / NestJS · TypeScript
Explore Node.jsPython
The language the AI and data ecosystem is written in — used where that ecosystem is the reason, alongside Node for the product itself.
AI & ML libraries · Data pipelines · Services · Automation
Explore PythonFastAPI
The Python API layer for AI and data services — typed, validated and documented at the boundary, with async throughput underneath.
Async Python · Pydantic validation · OpenAPI · Dependency injection
Explore FastAPICloud & DevOps
AWS, Azure and GCP with CI/CD, containers and observability built in from the first sprint — not bolted on at the end.
AWS · Azure · GCP · Docker · CI/CD · IaC
Explore Cloud & DevOpsTechnology group
Full-stack development
For products needing interface and backend to move together, one connected stack keeps a feature that spans all three a week's work rather than a negotiation.
MERN Stack
One connected JavaScript stack — MongoDB, Express, React, Node — for products a team can still maintain two years after launch.
MongoDB · Express · React · Node.js
Explore MERNMEAN Stack
Angular in place of React — the same connected JavaScript stack, favoured where enterprise teams want structure and typed patterns built in.
MongoDB · Express · Angular · Node.js
Explore MEANTechnology group
Database technologies
Data choices are the hardest to reverse later. We pick per access pattern, not per preference, and routinely run more than one store in a single system.
MongoDB
Document data modelling for products whose shape evolves — with the indexing and aggregation discipline that keeps it fast at scale.
MongoDB · Atlas · Aggregation pipelines
Explore MongoDBPostgreSQL
The relational default for domains where correctness is the product — ledgers, eligibility, anything auditable.
PostgreSQL · SQL · pgvector
Explore PostgreSQLRedis
The layer that keeps a system fast under load — caching, queues, sessions and rate limits, held in memory.
Redis · Pub/Sub · Streams
Explore RedisVector Databases
The retrieval layer under RAG — embeddings stored and searched by meaning rather than by keyword.
Atlas Vector Search · pgvector · Pinecone · Qdrant
Explore Vector DatabasesTechnology group
Applied AI and LLM engineering
AI shows up twice here: in the products we build with it, and in how we build everything else. Both are engineered, reviewed and measured rather than trusted. The frameworks below are the parts we assemble from — chosen per problem, and named here with the cases each one is wrong for.
AI & LLM Engineering
RAG, agents and tool use built properly — with the eval harnesses and guardrails that keep them from embarrassing you.
LLMs · RAG · Agents · Evaluation harnesses
Explore AI & LLM EngineeringLangChain
The orchestration layer for LLM applications — chains, tool-calling agents and memory, wired to your data and your systems.
Chains · Agents · Tools · Memory · Retrievers
Explore LangChainLlamaIndex
Retrieval built properly — ingestion, indexing and query engines tuned so an assistant answers from your documents instead of guessing.
Ingestion · Indexing · Query engines · Rerankers
Explore LlamaIndexHaystack
Composable search and RAG pipelines for enterprise estates — explicit, inspectable stages rather than one opaque call.
Pipelines · Retrievers · Rankers · Readers
Explore HaystackCrewAI
Multi-agent orchestration — specialised agents with defined roles handing work between them, for processes a single prompt cannot hold.
Agents · Roles · Tasks · Delegation
Explore CrewAILiteLLM
One interface across every model provider — with routing, fallbacks, per-team budgets and the spend visibility that makes AI costs governable.
Unified API · Proxy · Budgets · Usage tracking
Explore LiteLLMvLLM
High-throughput inference for open-weight models — serving your own LLMs at production concurrency, on your own hardware.
PagedAttention · Batching · OpenAI-compatible API
Explore vLLMOllama
Open-weight models running locally — for private workloads, air-gapped environments and development that costs nothing per token.
Local runtime · Model library · OpenAI-compatible API
Explore OllamaThe constant
What every stack here gets.
The technology is the variable. This is not — whichever of the nine above turns out to be right for you.
the same engineering discipline
- Typed end to endTypeScript by default
- Senior sign-offA named human on every diff
- Tests to criteriaWritten to week-one acceptance
- DocumentationSo the next team can read it
automated delivery, from week one
- CI/CDDeploy on every merge
- Infrastructure as codeEnvironments reproducible
- StagingClickable from week three
- ObservabilityTracing, alerting, dashboards
what actually breaks in production
- Security reviewAdversarial review, secret scanning
- Performance budgetsLatency and cost, costed up front
- AccessibilityEmpty states and screen readers
- Error trackingCaught before it is your incident
yours, with nothing to buy back
- Your repositoryYour account from week one
- Your cloudNo platform underneath
- Your IPAssigned in the contract
- No lock-inNothing to license back
Related services
Technology only matters if it supports the right service outcome.
Nobody buys a stack. These pages are where the technology choices above connect to what you are actually trying to get done.
FAQs
Questions about the technologies we use.
How stacks get chosen, what we work with, and who owns what at the end of it.
How do you choose a technology stack?
From the problem, in this order: what the product must do, who maintains it, whether search matters, how the data is really shaped, and what your team can hire for. The stack is the last decision, not the first — and we will recommend one we do not sell if it fits you better.
What technologies does TrivialWorks work with?
React, Next.js and React Native on the front; Node.js on the back, with Python and FastAPI where AI and data work make them the better tool; MERN and MEAN as connected stacks; MongoDB, PostgreSQL, Redis and vector stores for data; AWS, Azure and GCP underneath; and applied AI — LangChain, LlamaIndex, Haystack, CrewAI for orchestration and retrieval, LiteLLM for routing, vLLM and Ollama for self-hosted serving. Each has its own page here, including when not to use it.
Can you work with the stack we already have?
Yes, and often that is the honest recommendation. We inherit React, Node, PHP and legacy estates regularly. We audit first, stabilise, then extend — a rewrite gets proposed only when we can show you why the maths works, not by default.
Do you use AI to write our code?
Yes, through a six-stage pipeline with machine review and two mandatory human gates. No generated code reaches your main branch without a named senior engineer reading it and taking responsibility for it. The speed comes from automating volume, not from removing review.
Who owns the code and the IP?
You do, from day one — assigned contractually, in your repository and your cloud account throughout. There is no proprietary platform underneath and nothing to license back.
Can you recommend a stack before we commit to a project?
Yes. Send the requirement and you get back a functional specification — screens, data model, stack and an estimate — at no charge. If the honest answer is a smaller build than you asked for, that will be in there too.
Need help choosing the right stack?
Tell us the problem. You get back the honest shortlist — including the case for not building it the obvious way — from an engineer, not a salesperson.