Full-Stack AI Products

Custom AI Product Development

From concept to production-ready AI product — engineered end to end.

Core Capabilities

AI App Development for Every Stage and Scale

From AI MVP to production SaaS platform — full-stack AI development that covers strategy, engineering, and deployment in one engagement.

AI-Web-App-Development

AI Web App Development

Full-stack AI-powered web applications built with React, Node.js, Python, and cloud-native architecture — production-ready, scalable, and engineered around your users’ actual workflows. 

  • React & Node.js full-stack  
  • Cloud-native architecture  
  • Production from day one 
AI-MVP-Development

AI MVP Development

A working AI product in 6–10 weeks — validated with real users before full investment is committed. From concept and architecture through to a live, testable product your team can ship. 

  • 6–10 week delivery  
  • Real-user validation  
  • Investor-ready build 
AI-SaaS-Platform-Development

AI SaaS Platform Development

End-to-end development of AI-powered SaaS products — multi-tenant architecture, subscription and billing infrastructure, and AI features built into the core product, not bolted on after. 

  • Multi-tenant architecture  
  • Billing & auth included  
  • AI-native from the start 
AI-Feature-Integration-Into-Existing-Products

AI Feature Integration Into Existing Products

LLM-powered features — chat, search, summarisation, recommendations, and automation — integrated into your existing web app or platform without rebuilding what already works. 

  • LLM feature integration  
  • No full rebuild required  
  • Works with existing stack 
AI-Agent-Powered-Internal-Tools

AI Agent-Powered Internal Tools

Custom internal tools powered by AI agents — workflow automation, data extraction, reporting dashboards, and approval systems that replace manual processes with autonomous, reliable operations. 

  • Workflow automation  
  • Internal process agents  
  • Reporting & dashboards 

AI Product Strategy & Technical Scoping

Not sure what to build or where to start? We run a structured discovery sprint — use-case validation, architecture options, build timeline, and a written scope document before any development commitment. 

  • Use-case validation  
  • Architecture options  
  • Written scope document 
Real-World Applications

Built for Clients. Shipped to Production.

From autonomous document processors to intelligent enterprise platforms – here is what we have delivered.
View All Case Studies
Credit & Lending

AI Credit Underwriting Platform - Fintech SaaS

An SME lender deployed a six-stage AI agent pipeline from document ingestion to explainable decisions. Analysts review flagged cases only. Fast decisions, consistent underwriting, and full FCA audit compliance.

View Case Study →
AI Credit Underwriting
AI Infrastructure

LLM Routing Platform - Cost, Quality & Latency Optimisation

Task-aware routing classifies requests, estimates complexity, and selects optimal models via LiteLLM. All decisions are logged while dashboards provide visibility and optimisation.

View Case Study →
LLM Routing Platform
Government & Public Sector

On-Premise LLM & RAG Platform - Government Enterprise AI

An on-premise LLM on NVIDIA DGX hardware with a secure RAG pipeline over internal data. Staff query in natural language with zero data leakage.

View Case Study →
Government AI Platform
How It Works

From Use Case to Production

No black boxes. No surprises. Working agents in your hands, sprint by sprint.

Product Discovery & Scoping

Step 1
We define what to build and why — use-case validation, user journey mapping, AI feature architecture, and a written scope document with timeline and cost estimate before any development commitment.

Architecture & Technical Design

Step 2
Full-stack architecture specified — frontend, backend, AI integration layer, data model, and infrastructure. LLM selection, API design, and security model defined before the first sprint begins.

Agile Build Sprints

Step 3
Two-week sprints with a working, demonstrable product every fortnight. AI features built and integrated against real data — not mocked endpoints or synthetic test cases.

Testing, Validation & Performance

Step 4
Functional testing, AI output validation, load testing, and edge-case coverage completed before any production release. LLM output parsing, fallback logic, and cost monitoring configured at this stage.

Production Launch & Handover

Step 5
Product deployed to your cloud infrastructure. Full documentation, architecture diagrams, and codebase handover completed. 100% code ownership transferred — no licence fees, no lock-in, no ongoing dependency on Khired.

Reach Out

Contact Us

Contact us (#6)

We typically respond within 24 hours.