Hanabi Technologies

Hanabi Technologies is an AI consultancy in Bangalore that builds custom RAG pipelines, LLM integrations, and model APIs for organizations. Its services center on tailored delivery, with Hana AI as the flagship product and consulting work spanning architecture, deployment, and maintenance.

AI Консультант Ассистент Разработка ИИ Агентов Дизайн AI API

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AI consultancy for custom RAG and LLM systems

Hanabi Technologies is an AI consultancy based in Bangalore that focuses on RAG pipelines, LLM integration, and model API engineering. Its services are built on a MERN stack foundation and are aimed at organizations that need custom AI systems connected to their own data and workflows.

The site presents Hanabi as both a consultancy and a product builder. Its flagship product, Hana AI, is positioned as a Google Chat team assistant with multi-tenant deployment, broad app integrations, and enterprise-oriented security features, while the consulting practice covers custom AI architecture, delivery, and maintenance.

Core capabilities

RAG pipeline development

Build retrieval-augmented generation systems that answer from proprietary documents and databases rather than relying only on model memory.

LLM integration and fine-tuning

Connect and tune large language models for domain-specific use cases, including model selection, prompt optimization, and multi-model orchestration.

Model API engineering

Design REST and GraphQL APIs on a MERN stack foundation to serve AI models with authentication, rate limiting, caching, and observability.

Multi-source data ingestion

Ingest content from PDFs, docs, databases, APIs, and knowledge bases, with support for vector search and relevance scoring.

End-to-end delivery process

Support custom deployments with agile sprints, client reviews, testing, deployment, and post-launch maintenance.

Common use cases

Internal knowledge assistants
Teams with scattered internal knowledge can centralize documents and app data into a retrieval layer that supports contextual answers and chat-based assistance.
RAG-powered business workflows
Organizations can build or improve AI features that need to pull from documents, databases, or tools while keeping responses grounded in their own source data.
AI model APIs for applications
Product teams can expose AI capabilities through reliable REST or GraphQL APIs, with security, rate limiting, and monitoring built into the delivery stack.
Workflow and tool integration
Companies with existing workplace tools can connect systems such as Google Workspace, Jira, Confluence, Notion, Slack, and Zapier through custom integrations.
Enterprise deployment and support
Regulated or security-sensitive teams can use the consultancy’s deployment process and security measures to design controlled AI systems with maintenance and support.

Pros and Cons

Pros

Covers the full stack from discovery and architecture through deployment and support.
Supports custom RAG, LLM, and API work rather than a single narrow use case.
Builds on a modern MERN stack and offers integration experience with common workplace tools and APIs.
Publishes a defined onboarding process with timeline ranges and maintenance coverage.
Backed by case studies showing production use at scale, including Hana AI and other client projects.

Cons

It is not a self-serve SaaS product; work starts with consultation and a custom proposal.
Public pages give only partial detail on pricing, so cost expectations are handled case by case.
Many technical specifics depend on the project scope, so readers may need a discovery call to confirm fit.

FAQ

How do you start a project?

Hanabi Technologies offers a free consultation and custom proposal process. The onboarding page describes a discovery call first, followed by architecture, planning, and a scoped proposal rather than a self-serve checkout flow.

How long does delivery usually take?

The onboarding page says most projects range from 6 to 12 weeks, depending on complexity. Development is delivered in agile sprints with regular client reviews.

What kinds of systems can it integrate with?

The services page shows work across RAG pipeline development, LLM integration and fine-tuning, and model API engineering on the MERN stack. The case studies also mention Google Workspace, Jira, Confluence, Notion, Zapier, OpenAI, Qdrant, Pinecone, Weaviate, and cloud deployment on GCP Kubernetes or AWS Kubernetes where applicable.

Is this a standard off-the-shelf product?

The company positions itself for custom AI consultancy rather than a packaged software product. The process, pricing, and architecture are tailored to each project, so the fit depends on the specific data sources, deployment needs, and security requirements.

What does the team specialize in?

The service emphasizes RAG pipelines, LLM integrations, model APIs, and MERN stack implementation. The onboarding FAQ also notes security measures such as encryption, JWT authentication, and CASA Tier-2 compliance.