AI Service
Generative AI & LLM Applications
We design and develop generative AI applications that use modern LLM capabilities to improve productivity and product experience. From OpenAI GPT integrations and private knowledge retrieval (RAG) to multi-step AI agents and content generation workflows, we build systems with guardrails, quality checks, and clear user experiences. Ideal for businesses that want AI assistants, smart internal tools, or customer-facing generative features.
Overview
Why generative ai & llm applications matters for growing businesses
Large language models are powerful — and dangerous when bolted on without architecture. CodeStudioHub designs Generative AI and LLM applications with retrieval, tools, guardrails, and UX that keep answers grounded in your data.
From internal copilots and customer knowledge assistants to multi-step agents and content workflows, we build systems your teams can supervise and improve.
RAG and private knowledge assistants
Retrieval-Augmented Generation (RAG) lets LLMs answer from your documents, policies, and product data instead of inventing freely. We design chunking, embeddings, access filters, and citation UX so users can verify sources.
Copilots and agents that do work
Beyond chat, we build copilots that draft, classify, extract, or trigger workflows with human approval gates. Agents can call tools — CRM lookups, ticket creation, database queries — under strict permissions.
Quality, cost, and safety
Prompt and evaluation suites catch regressions. Caching and model routing control cost. Safety filters and policy prompts reduce risky outputs before they reach customers.
Fit
Who this service is for
- Companies with large document knowledge bases
- Product teams shipping AI features
- Support and sales orgs needing assistants
- Businesses exploring GPT-powered automation
Results
Outcomes you can expect
- Faster answers from internal knowledge
- Reduced repetitive drafting work
- Grounded responses with source visibility
- Controlled LLM spend in production
Delivery
What we typically deliver
- LLM architecture & prompt systems
- RAG pipelines on private data
- Copilot / agent applications
- Guardrails & eval harness
- Admin analytics for usage & quality
Capabilities
Core capabilities included in this engagement
- LLM app architecture & prompt engineering
- RAG knowledge bases on your private data
- AI copilots & workflow agents
- Content generation & summarization systems
- Safety, guardrails & quality evaluation
How we deliver
A proven process adapted to this service
- 1
1–2 Days
Idea Discovery
We listen first — understanding your vision, goals, challenges, and what success looks like for your project.
- 2
3–5 Days
Strategy & Planning
We define scope, timeline, milestones, and the right tech stack so the path from idea to delivery is clear.
- 3
1–2 Weeks
Design & Prototype
Interactive prototypes and polished UI concepts so you can see and refine the product before build begins.
- 4
4–12 Weeks
Development
Agile development with weekly progress updates. Clean code, tested features, and transparent collaboration.
FAQs
Frequently asked questions about genai & llms
Clear answers for buyers comparing vendors, timelines, and delivery models — written for both people and answer engines.
What is RAG in simple terms?+
RAG retrieves relevant pieces of your own content and feeds them to the model so answers stay closer to your facts instead of relying only on general training.
Can we use our PDFs and Notion docs?+
Yes. We ingest common document sources, clean them, and keep permissions in mind when retrieving.
How do you stop hallucinations?+
You cannot eliminate them entirely, but RAG, citations, refusal behaviour, and evaluation loops dramatically reduce risky answers.
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Your next step
Ready to write the next chapter with us?
Book a free consultation. We’ll map your goals, constraints, and the fastest path to a high-quality outcome.
30 min
Free consultation
< 2 hrs
Typical reply
11–7
Office hours
Global
Coverage