Generative AI & LLM Development Services
Venizor builds generative AI applications on large language models: private knowledge assistants with cited answers (RAG), content and document generation tools, and fine-tuned models for specialised tasks — deployed securely on your data.
Generative AI & LLM Apps at a glance
| What it is | RAG knowledge assistants, content tools and fine-tuned models on your data. |
|---|---|
| Typical timeline | 4–12 weeks |
| Ways to engage | Fixed-scope project, dedicated team or hire developers |
| Main technologies | Python, LangChain, Hugging Face, PostgreSQL, AWS |
| Delivered from | T-Hub, Hyderabad, India — for clients in India and worldwide |
| You own | All code, designs and documentation, under an NDA |
What's included in generative AI & LLM apps?
- Use-case design and model selection
- Retrieval-augmented generation (RAG) pipelines
- Prompt design and evaluation datasets
- Fine-tuning where it improves results
- Secure deployment with access control
- Cost, latency and quality monitoring
Who is it for?
Internal knowledge assistants
Proposal, report and content drafting
Search across contracts and policies
Adding AI features to existing products
Which technologies do we use?
Python
LangChain
Hugging Face
PostgreSQL
AWSClear steps,
no surprises
Two-week sprints, a demo every week and a shared board — you always know what's done, what's next and where the budget goes.
Discover
Workshops to map goals, users, scope and risks.
Week 1–2Design
Wireframes, clickable prototypes and a design system.
Week 2–4Build
Agile sprints with staging access and weekly demos.
Sprint by sprintTest & launch
QA, security checks, app-store and cloud release.
LaunchGrow
Monitoring, improvements and growth marketing.
OngoingStart with a discovery sprint
A focused 2–3 week engagement that turns your idea or problem into a clear scope, architecture, prototype and fixed estimate — so you can invest in a large build with confidence.
What you receive
- Stakeholder workshops and goals
- User journeys and scope definition
- Solution architecture and tech stack
- Clickable prototype of key screens
- Risk register and delivery plan
- Fixed estimate and phased roadmap
What is RAG (retrieval-augmented generation)?
RAG lets a language model answer using your own documents: relevant passages are retrieved first and given to the model, so answers are grounded in your content and can cite their sources.
Will our data be used to train public AI models?
No. We use enterprise APIs or private deployments configured so your data is not used for training, and we restrict access by role.
More AI & machine learning services
Have an idea?
Let's build it together.
Tell us about your project and we'll get back to you with next steps and a free consultation slot.
- Free discovery call
- Clear estimate before any work
- NDA on request
Knowledge City, Raidurgam, Hyderabad, Telangana 500081, India
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