Generative AI Development, GPT-5 and LLM Apps
LLM apps with GPT-4o, Claude, Gemini, Llama. RAG pipelines, fine-tuning, prompt eval. 2-6 wk pilot, then 6-12 wk production.
Retrieval with citations
What is the notice period for UAE contractors?
Answer
Sources
- [1]hr-policy.pdfp.4
- [2]uae-annex.mdsec. 2
- [3]contract.docxclause 9
| Typical cost | $8,000–$40,000 (content generator to fine-tune) |
|---|---|
| Timeline | 2-6 wk pilot, then 6-12 wk production |
| Starting rate | From $25/hr |
| Platforms | OpenAI, Azure OpenAI, Anthropic, Google Vertex |
| Approach | RAG first; fine-tune when gains justify cost |
What can you build with generative AI development?
We build LLM-powered applications: knowledge assistants that answer questions over your policies and manuals with sources, document search and summarization tools, content generation with review steps, and code and data helpers that explain logs or write SQL starters. Each app includes prompt evaluation, retrieval grounded in approved sources, guardrails and moderation, and feedback telemetry.
How much does it cost to build an LLM application?
An LLM-powered content generator typically costs $8,000 to $20,000, a RAG-based knowledge assistant $12,000 to $30,000, and a custom fine-tuned model $15,000 to $40,000, according to our 2026 India cost guide. A full AI SaaS product starts at about $40,000. Rates start at $25 per hour. Exact cost depends on data readiness, integrations, and compliance needs.
AI development cost in India: full 2026 breakdownHow long does a generative AI project take?
A generative AI project typically takes a 2 to 6 week pilot, then 6 to 12 weeks to production. Smaller builds can be faster: an LLM content generator takes 4 to 8 weeks and a RAG knowledge assistant takes 6 to 10 weeks. A full AI SaaS product takes 3 to 8 months. Data cleaning or labeling work can extend the timeline.
Which LLMs and platforms do you work with?
CognyX AI works with OpenAI, Azure OpenAI, Anthropic, Google Vertex, and self-hosted models. Model families include GPT, Claude, Gemini, and Llama. We choose the model for each use case, and we deploy in your cloud when required. Off-the-shelf model APIs usually cost less than fine-tuning a custom model or hosting an open-source LLM yourself.
What is the process for a generative AI project?
The process has four parts: prompt design and evaluation, retrieval over your approved data (RAG), guardrails and moderation, and telemetry with feedback loops. We start with RAG and fine-tune only when measured gains justify the cost. A proof-of-concept at $5,000 to $15,000 validates the use case before you commit to a larger production build.
Is our data safe in a generative AI application?
Yes, when the architecture keeps data inside approved boundaries. Models can run in your own cloud, retrieval uses only approved sources, and policies and filters block harmful or off-topic output. Sensitive actions keep a human review step. HIPAA, SOC 2, or GDPR compliance work typically adds 15 to 25% to project cost because of audit trails, encryption, and access controls.
What generative AI can do
Content generation at scale
Draft emails, product descriptions, support replies, and more with review steps.
Knowledge assistants for teams
Ask natural questions over policies, manuals, and past cases, get sourced answers.
Document search and summarization
Upload files, extract key points, and share concise briefs.
Code and data helpers
Explain logs, write SQL starters, and document pipelines.
How we deliver safely
- 01
Prompt design and evaluation
Prompt design and evaluation — clear, testable, and easy to tune
- 02
Retrieval with your data
RAG keeps answers grounded in approved sources.
- 03
Guardrails and moderation
Policies and filters reduce harmful or off topic content.
- 04
Telemetry and feedback loops
Score outputs, collect feedback, and improve continuously.
Platforms we support
OpenAI
Azure OpenAI
Google Vertex
Anthropic
Self-hosted models
FAQ
Do we need to fine tune?
Start with RAG. Fine tune when gains justify cost.
How do you prevent bad outputs?
Policies, filters, domain rules, and human review for sensitive actions.
Can models run in our cloud?
Yes. We deploy in your cloud when required.
Articles on Generative AI Development, GPT-5 and LLM Apps
CognyX shipped a production-ready AI agent that integrates with our LMS and support stack. Clean architecture, secure data handling, on-time delivery and fewer support tickets.
Jerry Xiao, AI Code AcademyRecent Case Studies
See how we've shipped production AI for teams that need generative AI development — with the architecture and measured outcome behind each build.
Legal Tech
Enterprise RAG System for a Legal Tech Startup
Reduced research time by 70%.
Media
Generative AI Content Pipeline for a Media Company
Increased content output by 5x while maintaining editorial standards.
EdTech
Autonomous AI Agent for an EdTech Academy
Clear uplift in engagement and fewer support tickets, delivered on time.
Tell us what you want to automate.
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