Microsoft Solutions Partner

Agents that act, with guardrails

AI App Development Services

Build AI-powered applications with AI at the core — grounded in your data, connected to your systems, and engineered for production across OpenAI, Anthropic, Google Cloud, AWS and Microsoft Azure.

AI applications built across OpenAI, Anthropic, Google Cloud, AWS and Microsoft Azure — selected for fit, security, cost and scalability.

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Years in Software

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Developers

OpenAI, Anthropic, Google Cloud, AWS & Microsoft Azure

Built across the major agent ecosystems

Copilot Studio

Azure AI Foundry

AWS Bedrock

Google Vertex AI

OpenAI

LangChain / LangGraph

AI Application Challenges That Need the Right Engineering

Knowledge trapped in documents — employees can’t find answers without asking someone?

Document processing manual and slow — invoices, contracts, forms all hand-processed?

Want to build an AI-powered product but don’t have the AI engineering team?

Built a chatbot that hallucinates — answers aren’t grounded in your data?

What We Do

Grounded in Your Data

Connect AI to your documents, databases, and business data using RAG for accurate, relevant, and grounded responses.

Production-Ready From Day One

Build reliable AI applications with error handling, monitoring, content filtering, rate limiting, and cost governance.

The Right AI Platform for Your Needs

Select models, clouds, vector databases, and frameworks based on your requirements—not a forced single-vendor approach.

Secure by Design

Protect enterprise data with SSO, RBAC, data isolation, content filtering, audit logging, and controlled access.

Integrated Into Your Existing Workflow

Connect AI applications with Microsoft 365, Google Workspace, CRMs, ERPs, databases, APIs, and internal business systems.

From POC to Production, Fast

Move validated AI ideas into production through a structured path: 2-week POC, 4–8 week MVP, and production deployment.

Our Services & Capabilities

AI Applications Built Around Your Business Needs

Knowledge Assistant (RAG)

Answer natural-language questions using your trusted documents and business data with permission-aware RAG, vector search, reranking, and grounded responses.

Intelligent Document Processor

Extract, classify, validate, and route information from invoices, contracts, forms, and correspondence while reducing manual data entry.

Conversational AI Interface

Enable customers and employees to complete tasks naturally through AI-powered conversations instead of complex forms, menus, and workflows.

Intelligent Search

Deliver relevant results using semantic understanding, vector search, hybrid retrieval, and natural-language queries across structured and unstructured data.

AI Agent (Multi-Step Reasoning)

Automate complex tasks through AI agents that reason, call tools, access APIs, analyze data, and execute multi-step workflows.

Custom AI Product

Build bespoke AI-powered products where intelligent capabilities become the core value proposition, tailored to your market, data, and business model.

Our AI Application Technology Stack

Layer	Technologies
AI models	OpenAI GPT models · Anthropic Claude · Google Gemini · AWS Bedrock models · Azure OpenAI · Open-source LLMs where appropriate
Search & retrieval	Vector search · Semantic search · Hybrid retrieval · Embeddings · RAG pipelines · Reranking
Orchestration	Document AI · OCR · Form extraction · Classification · Custom extraction models · Human review workflows
Application	React · Next.js · ASP.NET Core · Node.js · Python · Blazor · API-first backends · Mobile-ready interfaces
Data	SQL · PostgreSQL · MongoDB · Cosmos DB · S3 · Azure Blob Storage · Google Cloud Storage · Data lakes · APIs
Security	SSO · RBAC · Tenant isolation · Data encryption · Guardrails · Responsible AI policies · Audit logging
DevOps	GitHub Actions · Azure DevOps · Docker · Kubernetes · Cloud monitoring · Observability · CI/CD

AI Application Patterns We Build

Why Star Knowledge for AI app development: platform-agnostic AI engineering, full-stack product development, RAG and document AI implementation experience, agent architecture, secure integrations and a practical POC-to-production approach that turns validated use cases into reliable applications.

AI Application Strategy & Architecture

Assess your use case and design the AI architecture, data pipeline, model strategy, security, and implementation blueprint.

RAG Application Development

Build knowledge assistants using LLMs, embeddings, vector search, and permission-aware retrieval across documents, databases, and business data.

Document AI Applications

Automate document extraction, classification, routing, and workflow integration using the document AI technology best suited to your environment.

Conversational AI & Chatbots

Build secure AI chatbots and conversational interfaces that understand your data and integrate seamlessly with business systems.

AI Search Implementation

Implement intelligent search using vector search, semantic ranking, hybrid retrieval, and natural-language queries across enterprise data.

AI Agent Development

Build AI agents capable of multi-step reasoning, tool calling, API integration, data analysis, and autonomous workflow execution.

Enterprise Integrations & Workflow AI

Connect AI applications with CRMs, ERPs, Microsoft 365, Google Workspace, databases, APIs, and internal systems.

Need to add AI to an existing app? See AI Modernization. Need AI strategy?

Our AI Application Methodology

Production AI application checklist: permission-aware retrieval, source citations, prompt and response evaluation, content filtering, cost controls, fallback handling, monitoring, audit logging, CI/CD deployment and human review for high-impact decisions.

Free consultation & use case assessment

We discuss your AI application idea, assess feasibility, and recommend the right AI pattern and architecture. No cost, no obligation.

2-week proof of concept

We build a working POC with your data — demonstrating that the AI pattern works and the responses are accurate and useful. Go/no-go decision.

Architecture & data pipeline design

We design the production architecture: RAG pipeline, data ingestion, embedding strategy, security, and cost governance.

MVP build (4–8 week sprints)

We build the production MVP: application, AI pipeline, UI, authentication, monitoring, and error handling — with demos every 2 weeks.

Testing & responsible AI review

We test answer accuracy, retrieval quality, citation usefulness, latency, hallucination rate, content filtering, edge cases and user feedback — ensuring the AI application is reliable, measurable and responsible before production rollout.

Production deployment & optimisation

We deploy to the selected cloud or customer environment, configure monitoring and alerting, optimise model usage for cost, and iterate based on real user feedback.

It starts with a free consultation. No obligation.

What AI App Do You Need?

Your need	What we build	AI pattern
“Employees can’t find answers in our docs”	Knowledge assistant	RAG with LLM + vector search
“Invoices/contracts processed manually”	Document processor	Document Intelligence + workflow
“Customers need self-service AI help”	Customer chatbot	Conversational AI (grounded)
“Search in our app is terrible”	Intelligent search	Vector search + semantic ranking
“Need AI to do multi-step analysis”	AI agent	Agent framework + tool calling
“Building an AI product/SaaS”	Custom AI product	Full-stack AI application
“Want our app accessible from Copilot”	Copilot integration	API / workflow / collaboration integration
“Had a POC, need production”	POC → production	Production architecture + deployment

Case Studies

Knowledge Assistant (RAG) for a Professional Services Firm

Professional services firm, 500+ employees, 10 years of knowledge documents.

Employees spending hours searching for policies, procedures, and past project information — knowledge trapped in SharePoint and file shares.

Built RAG knowledge assistant: Azure OpenAI + AI Search indexing SharePoint, file shares, and Confluence. Entra ID auth, role-based data access.

Invoice Processing AI for an Insurance Company

Insurance company, 2,000+ claims documents/week.

Claims documents (medical reports, invoices, forms) processed manually — 6-person team, slow, and error-prone.

Built document processing application: Azure Document Intelligence for extraction, Azure OpenAI for classification and summarisation, Power Automate for routing.

AI-Powered Product Search for a Retail Company

Retail company, 20,000+ products, e-commerce platform

Implemented Azure AI Search with vector embeddings and semantic ranking. Natural-language product queries returning intent-matched results.

Built orchestrated agents with human approval gates at key decision points.

Industries We Serve

Professional Services

Knowledge assistants, document automation, contract analysis

Financial Services

Compliance AI, document extraction, risk analysis

Insurance

Claims processing, document classification, underwriting AI

Healthcare

Clinical document processing, patient Q&A, coding assistance

Manufacturing

Quality document processing, operational knowledge bases

Technology

AI-powered products, SaaS with AI features, developer tools

Retail

Intelligent product search, personalisation, recommendation engines

Legal

Contract analysis, case research, document review AI

From a focused RAG knowledge assistant to a custom AI product serving thousands of users.

Insights

Blog

Automate Business Processes Using Microsoft AI Builder

Blog

From AI POC to production — what most teams get wrong

Checklist

AI application architecture blueprint template

guide

Building production AI applications — a CTO's guide to models, RAG, agents and cloud choices

RAG Is The Pattern That Changed Everything (2026)

In 2026, Retrieval-Augmented Generation (RAG) remains one of the most practical enterprise AI application patterns because it connects a language model to approved business content. It retrieves relevant passages from documents, databases and knowledge sources, passes that context to the selected model, and returns answers with citations so users can verify the source. Production RAG depends on more than a model: it requires ingestion, embeddings, vector or hybrid search, reranking, permissions, evaluation, monitoring and cost controls.

RAG solves hallucination — responses grounded in your data, not model imagination; citations included so users can verify

AI agents are the next step — multi-step reasoning, tool calling, and autonomous task execution extend RAG from Q&A to action

Framework choice matters — LangChain, LlamaIndex, Semantic Kernel, MCP and cloud-native agent tooling each fit different engineering patterns

Production is harder than POC — the gap between a working demo and a reliable production AI application is architecture, security, monitoring, and cost governance

Can't we just use ChatGPT directly?

For general knowledge, yes. For business workflows, private data and production use, no. A custom AI application connects a selected model to your approved data sources, business systems and security controls. RAG, access control, audit logging, evaluation and monitoring make the experience reliable enough for enterprise use.

Frequently Asked Questions

What AI applications do you build?

Knowledge assistants, RAG applications, document processors, conversational AI, intelligent search, AI agents and custom AI products. We build across OpenAI, Anthropic, Google Cloud, AWS, Azure and open-source options based on the use case.

What is RAG (Retrieval-Augmented Generation)?

RAG is an architecture that connects an AI model to your data. Documents and records are indexed, relevant content is retrieved when a user asks a question, and that context is sent to the model so the answer is grounded in approved sources instead of only model training data.

How do you prevent AI hallucination?

RAG architecture grounds responses in retrieved documents, with citations. Content filtering blocks inappropriate outputs. Evaluation testing measures accuracy. But no AI is 100% accurate — we design applications so users can verify answers.

How much does an AI application cost?

A proof of concept typically runs $5,000–$15,000 over 2–3 weeks. A production MVP often runs $25,000–$50,000 over 2–4 months. Larger enterprise AI applications can run $100,000–$300,000+ over 4–8 months. Model, cloud and infrastructure consumption are separate and usage-based.

How long does it take to build an AI application?

POC: 2–3 weeks. Production MVP: 2–4 months. Enterprise: 4–8 months. We start with a 2-week POC to prove the pattern works with your data before committing to a full build.

Which AI models and cloud platforms do you support?

We support OpenAI, Anthropic Claude, Google Gemini, AWS Bedrock, Azure OpenAI and open-source models where appropriate. We also build on Azure, AWS, Google Cloud or customer-managed infrastructure depending on security, compliance, cost and integration needs

Are you locked into one AI vendor?

No. We are platform-agnostic. We recommend the model, retrieval layer, framework and cloud architecture that best match your accuracy, latency, compliance, cost and integration requirements.

What data sources can the AI app use?

SharePoint, file shares, databases (SQL, Cosmos DB), APIs, PDFs, Word documents, web content, Confluence, and custom data sources. If it’s text, it can be indexed and searched.

Book My Free AI App Consultation

Tell us the task you have in mind. We’ll assess whether an agent suits it, at what autonomy level, and what the first step should be.

+1 (312) 843-7397

info@star-knowledge.com

Typically responds within 8 business hours · Confidential