AI Development Company, From the Team Behind Kenerate AI

It depends on scope. The main drivers are the type of product, how much of your data the AI must use, the number of integrations, the evaluation and safety work needed, and expected usage. Elgrith sends one fixed quote in USD, GBP or INR after a free call. Model usage is billed by providers, so we estimate the cost per request before you commit.

A focused AI MVP, such as an assistant over your documents or one AI feature inside an existing app, often takes 6 to 12 weeks. Larger products with agents, several models or heavy integrations take longer. You get a dated plan with your quote, a working prototype early, and a demo every two weeks so you can change direction before it gets expensive.

We are model-agnostic. We build with OpenAI, Anthropic Claude, Google Gemini and open-weight models such as Llama, Mistral and Qwen, plus leading image, video and voice models. We pick per task on quality, speed and cost, and design so you can switch providers later. Kenerate AI, our flagship build, runs 200+ models behind one app.

Retrieval-augmented generation (RAG) lets an AI answer from your own documents and data instead of only what the model learned in training. It retrieves the relevant passages, then writes an answer with citations. You need it when answers must match your policies, products or records. We build RAG with access controls, so people only see what they are allowed to see.

A chatbot answers questions. An AI agent takes actions: it plans steps, calls your tools and APIs, checks the results and continues until the task is done. We build agents with clear limits, human approval for risky actions and full logs. We also build MCP servers, so assistants like Claude, ChatGPT and Cursor can use your product directly.

We define “good” before we build: a test set of real questions and expected answers that scores every change. Guardrails block unsafe or off-topic output, PII is masked where needed, and risky actions need approval. For cost, we use caching, smaller models where they score well and per-user limits, and we show spend in a dashboard.

You do. We sign your NDA before discovery, the contract assigns all code, prompts, evaluation sets and IP to you, and everything lives in your repository and cloud accounts. Your data stays yours: we configure providers so it is not used to train their models where they offer that option, and we never reuse it for other clients.

Yes, the USA and UK are our main markets. Client partnership runs from New York and London, engineering from our New Delhi headquarters, with daily overlap with US Eastern and UK hours. We contract in USD or GBP, schedule demos inside your working day, and keep data in US, UK or EU cloud regions when you need it.