AI Development

AI Web Application Development for Modern SaaS and Portals

We design and build web applications where AI is a core capability — from RAG-powered knowledge portals to SaaS products with AI drafting, analysis and search.

When AI is the product

Some web applications use AI for one feature. Others are built around it: a legal research portal that answers questions from thousands of documents, a sales platform that drafts proposals from CRM data, a compliance tool that reviews policies against regulations. Those products need a different architecture from the start, and that is what this service covers.

It combines our web application development experience with the specialist work in our AI development practice.

Architecture for AI-first web apps

Retrieval-augmented generation (RAG)

Most business AI apps need answers grounded in private data. We build ingestion pipelines that chunk, embed and index documents into a vector store (Azure AI Search, PostgreSQL with pgvector or a dedicated vector database), then retrieve the most relevant passages for each question and cite them in the answer.

Permission-aware retrieval

In a multi-user or multi-tenant app, users must only see answers drawn from content they are allowed to access. We enforce this at retrieval time using document-level ACLs and tenant filters — not by asking the model to behave.

Streaming, responsive UX

Users expect answers to appear as they are generated. We use server-sent events or SignalR to stream tokens into React or Next.js interfaces, with clear source citations, feedback buttons and the ability to edit and retry.

Usage metering and cost control

AI calls have a real marginal cost. We build per-tenant usage tracking, quotas and plan limits into the back end — essential if you charge customers on a SaaS model.

Typical AI web applications

  • Internal knowledge portals for policies, procedures and technical documentation
  • AI-assisted SaaS products for drafting, analysis and review
  • Customer portals with conversational search over product and order data
  • Research and due-diligence tools that summarise large document sets

Our AI web application development guide goes deeper into architecture choices, costs and pitfalls.

Quality you can measure

Every AI web application we ship includes an evaluation harness: a set of real questions with expected answers that runs on each release. When you change a prompt, model or chunking strategy, you see immediately whether quality went up or down.

    • What you get

      • AI-powered web application

        Production deployment on Azure or AWS with staging environment.

      • Ingestion and indexing pipeline

        Automated, monitored pipeline for keeping content current.

      • Evaluation suite

        Question sets and scoring scripts integrated into CI.

      • Architecture and runbooks

        Documentation covering data flows, security model and operations.

    • Industries we serve

      • Professional Services

        Client portals, CRM, project and time tracking, billing and AI-assisted document workflows for consultancies, agencies, law and accountancy firms.

      • Finance

        Secure software for lenders, brokers, accountancy practices and fintech teams: client portals, workflow automation, reporting and integrations with financial systems.

      • Education

        Software for schools, colleges and training providers: admissions, attendance, fees, timetables, gradebooks and parent communication in one connected platform.

      • E-commerce

        Custom e-commerce platforms, headless storefronts and back-office integrations for online retailers who have outgrown templates and manual order handling.

    Benefits

    Why invest in ai web application development

    • Answers from your own data

      Retrieval grounds every response in your documents and records, with citations users can check.

    • Differentiated product

      AI capabilities built into the core give SaaS products a feature set competitors cannot easily copy.

    • Safe for multi-tenant use

      Strict tenant isolation and permission-aware retrieval prevent data leaking between customers.

    • Predictable running costs

      Metering, quotas and model routing keep AI spend aligned with revenue.

    Capabilities

    What we build and deliver

    • RAG pipelines

      Document ingestion, chunking, embedding and hybrid search with source citations.

    • Streaming chat and copilots

      Real-time streamed responses in React or Next.js with feedback and retry controls.

    • Multi-tenant architecture

      Tenant-scoped data, indexes and configuration within one scalable platform.

    • Usage metering

      Per-user and per-tenant token tracking, quotas and billing integration.

    • Evaluation harness

      Automated regression tests for answer quality on every release.

    • Admin and content controls

      Tools to manage sources, re-index content, review conversations and tune behaviour.

    Process

    How we deliver

    A transparent, milestone-based process with working software at every stage.

    • Product and data discovery

      Define user journeys, content sources, tenancy model and quality targets.

    • RAG proof of concept

      Index a representative document set and measure answer quality on real questions.

    • Platform build

      Build the web app, ingestion pipelines, permissions, metering and admin tools in sprints.

    • Evaluation and security testing

      Run the evaluation suite, test prompt-injection resistance and tenant isolation.

    • Launch and optimise

      Staged rollout, usage analytics and continuous tuning of retrieval and prompts.

    Technologies

    Technology we use for this service

    • Next.js

      Next.js lets us build fast, search-friendly React websites and web apps with server rendering, ideal for marketing sites, storefronts and SaaS products that need SEO.

    • React

      We build responsive, accessible React front ends for dashboards, portals and SaaS products, backed by clean APIs and a component library you can maintain.

    • ASP.NET Core

      ASP.NET Core is our default framework for secure web applications and REST APIs: fast, cross-platform and well suited to complex business logic and integrations.

    • OpenAI

      We integrate OpenAI models into business software for assistants, document processing, search and content workflows, with grounding, evaluation and cost controls.

    • Anthropic Claude

      We build AI assistants, agents and document workflows on Anthropic's Claude models, known for long-context reasoning, careful instruction following and strong writing.

    Related work

    Projects we've delivered

    • AI Sales Assistant for B2B Teams

      An LLM-powered sales assistant that qualifies inbound leads, drafts tailored follow-ups and summarises accounts from CRM data for a B2B industrial supplier.

    • Smart Education ERP

      A unified education management platform for schools, colleges, universities, and multi-campus institutions, covering admissions, attendance, timetabling, fee management, examinations, academic records, and parent and student portals in one integrated system.

    • Modern POS System for a Regional Retail Chain

      A cloud-connected point-of-sale platform with offline tills, central pricing and real-time stock visibility, built for a 60-store retail chain replacing ageing on-premise terminals.

    Case studies

    Results from similar engagements

    • AI Support Assistant for a B2B SaaS Company

      How a B2B SaaS company deployed a grounded AI support assistant that resolves 42% of conversations on its own and hands the rest to agents with full context.

    • School Group Digital Transformation

      How a five-school trust replaced six disconnected tools with one school management platform and parent app, cutting admin time on attendance and fees by around 40%.

    What our clients say

    • We used to lose half a day's takings every time a store's broadband went down. Since the new tills went in, outages barely register, and head office can change a price at lunchtime and see it on every shelf-edge promotion by the afternoon.

    • Parents now have one app instead of four, and our office teams have stopped spending Monday mornings chasing absence calls. The migration was the part we worried about most, and the trial runs meant there were no surprises on go-live day.

    • The assistant resolves the repetitive how-do-I questions and hands everything else to us with a proper summary. Our agents spend their time on the tricky tickets now, and customers get answers at 2am without us adding a night shift.

    Solutions

    Related software solutions

    • Custom SaaS Platform

      A production-ready SaaS foundation — multi-tenancy, authentication, billing, admin and APIs — that we customise into your own software product, so you launch sooner.

    • AI Customer Support

      AI customer support assistant that answers questions from your own knowledge base, takes actions through your systems and hands off to human agents with full context.

    • AI Business Automation

      AI business automation platform that reads documents, classifies requests and runs multi-step workflows across your systems, with human approval where it matters.

    FAQ

    Frequently asked questions

    • What is a RAG application?

      Retrieval-augmented generation (RAG) combines search over your own content with a language model. The app finds the most relevant passages first, then asks the model to answer using only those passages and cite them, which greatly reduces made-up answers.

    • Can an AI web app keep each customer's data separate?

      Yes. We enforce tenant isolation in the database and the search index, and filter retrieval by user permissions, so one customer's content can never appear in another's answers.

    • Which front-end frameworks do you use for AI web apps?

      Usually React or Next.js for streaming, interactive interfaces, with an ASP.NET Core back end handling retrieval, permissions and metering.

    • How do you stop quality getting worse after changes?

      Every release runs an evaluation suite of real questions with expected answers. If a prompt or model change lowers the score, we see it before users do.

    Insights

    Further reading

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