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03 / Services / Cloud / Production

AI-Based Software DevelopmentSoftware that stays up.

Full-stack, AI-native products - LLM and ML capabilities designed into the architecture, not bolted on afterwards.

Discuss this service All services
Area
Cloud / Production
Stages
6
Related projects
3

01 / The problem

What usuallygets in the way.

Turn prototypes into maintainable applications on AWS, GCP or Azure, with infrastructure as code, CI/CD and observability.
01

The prototype works on one laptop only

02

Deployments are manual and scary

03

No visibility into errors, latency or spend

02 / What I build

What you get.

  • Containerised services and infrastructure as code
  • CI/CD pipelines with tests and preview environments
  • Logging, metrics, alerts and cost guardrails

03 / How it works

From commit to observed production

A typical architecture for this kind of work. Scroll to follow the flow, or hover a stage for detail.

  1. Code

    Reviewed changes with tests alongside.

  2. CI/CD

    Build, test and deploy on every merge.

  3. Containers

    The same image runs locally and in the cloud.

  4. Cloud runtime

    Managed services sized for real load.

  5. Observability

    Logs, traces and alerts on what users feel.

  6. Cost controls

    Budgets and scaling rules to avoid surprises.

04 / In depth

The details.

I build AI-native software products where intelligence is part of the architecture - semantic search, copilots, RAG, recommendations, and automation designed in from day one and shipped as polished, production full-stack apps.

AI-Native Architecture

Systems designed around embeddings, vector stores, LLM orchestration, and event-driven pipelines - not a chatbot stapled onto a legacy app.

Full-Stack Delivery

Modern web apps with Next.js, FastAPI, Node.js, PostgreSQL, and vector databases, from data model to responsive UI to deployment.

Intelligent Features

Copilots, semantic search, document Q&A, smart recommendations, and workflow automation embedded directly into your product experience.

Production Engineering

Testing, CI/CD, monitoring, and cloud infrastructure so the AI product is reliable, observable, and ready to scale.

Let's Build Your AI Product

05 / Tech stack

Tools I usefor this work.

  • AWS
  • GCP
  • Azure
  • Docker
  • Kubernetes
  • Terraform
  • Next.js
  • FastAPI

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06 / Related work

Proof,not promises.

Case study
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CrewFare Node.js | AWS Serverless | React | RESTful APIs

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Cloud / Production

Need AI-Based Software Development?Let’s scope it together.

Tell me about the goal, the data and the constraints. I’ll reply with questions and a practical first step.

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