Technology
AI & LLMs (OpenAI, LangChain, RAG) development.
AI that does a real job in your business, not a demo that impresses once.
MeetOnChai Studio builds AI features with large language models: assistants that answer from your own documents, extraction and classification that replace manual data entry, and automations that draft, summarise and route work. We use OpenAI, Anthropic and open models, with LangChain or plain SDKs depending on what the job needs.
Why we use it
Why AI & LLMs (OpenAI, LangChain, RAG), and when not.
Language models are genuinely good at a specific set of tasks: reading messy text, pulling out structure, summarising, drafting, and answering questions from a known set of documents. Most businesses have plenty of that work, done by hand. Pointing an LLM at a well-defined slice of it, with a human checking the output where it matters, is where we see real time saved.
We build these features as normal software, not magic. Retrieval-augmented generation (RAG) grounds answers in your own content so the model is not guessing. Structured outputs and tool calling make results predictable enough to feed into the rest of your system. And we write evaluations, real examples with expected answers, so a model or prompt change is measured instead of eyeballed.
When it's the wrong call: If the task needs to be exactly right every time, such as calculating tax, moving money or making a medical or legal decision, an LLM should not be the one deciding. Rules and plain code are cheaper, faster and auditable. AI is also a poor fit when you do not yet have the content or data it would rely on. We will say so if a chatbot is not what your problem needs.
What we build
What we build with AI & LLMs (OpenAI, LangChain, RAG).
- 01
Assistants over your documents
RAG-based chat that answers from your policies, product catalog, manuals or knowledge base, with sources shown.
- 02
Customer support helpers
Draft replies, triage tickets and answer common questions, handing off to a person when confidence is low.
- 03
Data extraction
Turning invoices, forms, emails and PDFs into structured records your system can use.
- 04
Classification and routing
Tagging enquiries, leads or orders automatically and sending each one to the right person or queue.
- 05
Content and catalogue tools
Generating first drafts of product descriptions, translations and summaries for a human to approve.
- 06
Agentic workflows
Multi-step automations that call your APIs and tools, with clear limits on what they are allowed to do on their own.
The stack around it
What it ships with.
- Models
- OpenAIAnthropic ClaudeGoogle GeminiOpen models via Ollama or hosted APIs
- Orchestration
- LangChainLangGraphVercel AI SDKPlain provider SDKs
- Retrieval
- PostgreSQL with pgvectorEmbeddingsHybrid keyword and vector search
- Quality and ops
- Evaluation setsTracing and loggingRate limits and cost caps
Honest comparison
AI & LLMs (OpenAI, LangChain, RAG) vs the alternatives.
vs Off-the-shelf AI tools
A ready-made support bot or AI writing tool is often the fastest start, and we will suggest one if it fits. Custom work makes sense when it needs your data, your workflow or your system's APIs.
vs Fine-tuning a model
Rarely the first step. RAG and good prompting solve most problems at a fraction of the effort. Fine-tuning is worth it for narrow, high-volume tasks once you have good training data.
vs Classic rules and code
For anything deterministic, rules beat a model on cost, speed and auditability. Many good systems use both: rules where the answer is known, an LLM where the input is messy.
Services
Services that fit.
- AI Integration & AutomationChatbots, AI features and automations added to the product you already have.
- SaaS DevelopmentMulti-tenant SaaS with accounts, roles and subscription billing built in from day one.
- MVP & Product DevelopmentThe smallest real version of your product, in front of users in 4-8 weeks.
- Web App DevelopmentNext.js web apps and SaaS products that hold up under real traffic.
FAQ
AI & LLMs (OpenAI, LangChain, RAG) questions.
How do you stop the AI from making things up?
You cannot eliminate it completely, so we design around it. Answers are grounded in your own documents with RAG and show their sources, the model is told to say it does not know, and outputs that feed other systems are validated against a schema. For high-stakes answers, a person reviews before anything is sent.
Is our data safe if we use OpenAI or another provider?
Business API terms from major providers state that API data is not used to train their models by default, but you should still read them for your case. We send only what each request needs, strip personal data where possible, and can use self-hosted open models when data must not leave your infrastructure.
What does running an AI feature cost each month?
It depends on how many requests you handle and which model you use, so we estimate it from your real volume before building. We also add caching, use smaller models for simple steps, and set spending caps so a bug cannot run up a surprise bill.
Can it work in Nepali or Hindi?
Current frontier models handle Hindi well and Nepali reasonably, though quality varies by task. We test on real examples in the language your users write in before promising anything, and keep a human check where accuracy matters.
Do we need LangChain?
Not always. For a single model call with retrieval, plain provider SDKs are simpler and easier to debug. LangChain and LangGraph earn their place in multi-step agents and pipelines with many tools. We pick whichever leaves you with less code to maintain.
Every project starts with a conversation
Got an idea? Let's talk over chai.
A free 15-minute call. No pitch deck, no obligation. You leave with an honest read on what to build first.
- We reply within 24 hours, usually much sooner.
- NDA signed before you share the details.
- Based in Kathmandu, working with Nepal, India and worldwide.