LLM APPLICATION DEVELOPMENT

LLM Applications that make your product smarter

Add AI-powered search, document intelligence, and natural language interfaces to your software - without building an AI team from scratch.

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Built for LLM application development, semantic search development, custom AI copilot workflows, and production-grade GPT-4 or Claude integration.

Live architecture pattern

Document to answer flow
RAG + embeddings
1. Documents

Product manuals, policies, contracts, wiki pages, tickets, transcripts

2. Embeddings

Text is chunked, tokenized, and converted into vectors for retrieval

3. Vector Database

Pinecone, Weaviate, pgvector, or Chroma stores semantic context

4. LLM Layer

GPT-4o, Claude, or open-source inference assembles the answer

5. Intelligent Output

Streamed response with source citation, structured output, or tool action

Plain-English explainer

What exactly is an LLM application?

An LLM application connects a large language model like GPT-4 or Claude to your data and systems. Instead of a generic chatbot, users get intelligent, context-aware responses grounded in your documents, database, APIs, and product logic.

Your Data

Policies, product docs, CRM records, call transcripts, knowledge bases, SQL data, and private files become the context layer for the application.

LLM + RAG Layer

The application uses embeddings, retrieval-augmented generation, context windows, and routing logic so answers are grounded before inference happens.

Intelligent Output

Users get semantic search, summarisation, copilots, content generation, or structured actions instead of generic text output with no business context.

Delivery scope

What we build for you

We build LLM application development projects that tie models to real business workflows rather than shipping isolated demos.

RAG Systems (Document Q&A)

Ingest PDFs, Word docs, wikis, Notion pages, and Confluence spaces. Users ask questions in plain English and get answers with source citations.

Use cases
  • Legal due diligence
  • Product manuals
  • Internal knowledge bases
  • Compliance documentation

AI-Powered Search & Summarisation

Replace keyword search with semantic search inside your SaaS product or internal tool. Summarise long documents, email threads, call transcripts, or reports on demand.

Use cases
  • CRM note summarisation
  • Research platforms
  • Media archives

Content Generation Pipelines

Auto-generate product descriptions, blog posts, email sequences, social captions, and job postings at scale in your brand voice.

Use cases
  • E-commerce catalogues
  • Marketing agencies
  • Content platforms
  • HR teams

AI Copilots & In-App Assistants

Embed a natural language interface directly into your product. Users ask questions, run analyses, or trigger actions in plain English.

Use cases
  • Analytics platforms
  • Project management tools
  • Finance software
  • CRM systems
Live RAG demo

Ask our demo AI anything

This demo simulates a lightweight RAG system over a fictional product manual and streams an answer with a source citation.

Sample Product Manual Q&A
Streaming answer with source reference
Demo RAG endpoint

Loaded sample document

Refund policy: Returns are accepted within 30 days for unused purchases. Refunds process within 3-5 business days after inspection.

Password reset: Users can reset passwords from Settings, then Security, then Reset Password. SSO users must contact an admin.

Integrations: The platform currently supports Slack, HubSpot, Salesforce, Google Drive, and Zapier.

I am a sample RAG assistant. Ask about the loaded product manual, and I will respond with an answer plus the source section I used.

We'll build this trained on your data. Book a 30-minute demo call →

Enterprise-grade AI stack

Enterprise-grade AI stack

LLM Models

  • OpenAI GPT-4o for fast multi-purpose inference and tool use
  • Anthropic Claude 3.5 for strong reasoning and structured output workflows
  • Llama 3 for open-source and on-premise deployment options

RAG Infrastructure

  • LangChain and LlamaIndex orchestration layers
  • Pinecone, Weaviate, pgvector, and Chroma vector database options
  • Streaming responses, function calling, structured outputs, tool use, and multi-modal text plus image handling
Model-agnostic. We select the best model for your privacy, latency, and cost requirements. On-premise deployment is available for regulated industries or teams that cannot send confidential data to hosted APIs.
Industries and use cases

Who benefits most from LLM applications

SaaS Companies

Add AI features that retain users, reduce churn, and justify premium pricing tiers through smarter product experiences.

Legal & Professional Services

Automate document review, contract analysis, and due diligence at scale while preserving source visibility and review controls.

Financial Services

Summarise reports, parse regulatory documents, and automate client query workflows with grounded answers and lower manual review load.

Healthcare & Life Sciences

Support clinical note summarisation, patient FAQ systems, and research literature search while respecting controlled deployment needs.

E-commerce & Retail

Generate product descriptions, improve search relevance, and automate customer queries across large catalogues and support content.

Education & EdTech

Deliver tutoring systems, course content generation, and student question answering without building a dedicated ML team.

Delivery process

From data to deployed LLM app - in 5 stages

1

Data Audit

1-3 days

We assess your documents, databases, APIs, and current context gaps before model work begins.

2

Architecture Design

2-4 days

We define the RAG pipeline, model selection, retrieval strategy, and integration map for your use case.

3

Build & Embed

1-3 weeks

We develop the LLM layer and integrate it into your product, internal tools, or workflow surface.

4

Evaluation

3-5 days

We test accuracy, hallucination rate, latency, edge cases, and answer quality against a controlled evaluation set.

5

Deploy & Monitor

Ongoing

We launch, instrument analytics, update retrieval quality, and handle monthly retraining as data changes.

FAQ

Frequently asked questions

RAG, or retrieval-augmented generation, means the model retrieves relevant information from your data before generating a response. That grounds answers in facts, reduces hallucinations, and makes outputs specific to your business rather than generic.
Yes. We can deploy the entire stack in your own cloud environment or on-premise. Your data stays inside your infrastructure, and we use open-source models such as Llama 3 or Mistral when hosted LLM APIs are not permitted.
Accuracy depends on data quality, chunking, retrieval strategy, and evaluation design. In strong implementations with well-structured sources, answer quality can reach very high accuracy against a ground-truth evaluation set. We deliver an evaluation report before launch.
Usually through REST APIs or WebSockets. We provide a streaming endpoint that your frontend can call directly, and standard web application integration typically takes 1-3 days once the backend layer is ready.
Ongoing maintenance includes monthly retraining as documents change, monitoring latency and usage, updating prompts and retrieval settings, and upgrading models as newer versions become worthwhile. We usually package this into a managed service retainer.
Yes. We have experience deploying Llama 3, Mistral, and Mixtral on private infrastructure. This is useful for teams with strict privacy or data residency rules. Performance can be slightly lower than frontier hosted models, but it improves quickly.
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Book a Free LLM Application Discovery Call

Smart Genesis designs RAG system development, semantic search development, AI copilots, and content generation pipelines that fit your existing product and data landscape.

Book a Free LLM Application Discovery Call