Frequently asked questions
Private AI Projects: Cost, Timelines, Ownership & Integration FAQ
Direct answers to the questions B2B teams ask before they start: deployment options, cost, timelines, IP ownership, integration and data protection.
What is an on-premise LLM, and how is it different from a public AI API?
An on-premise LLM is a large language model that runs on your own servers or in your private cloud tenant instead of a provider’s shared cloud. Prompts and documents stay inside your network, which helps you meet data-residency and compliance requirements (GDPR, HIPAA, SOC 2) and keeps proprietary data out of other companies’ model training. A public AI API needs no infrastructure, but every prompt is sent to the provider. Naringhi deploys and fine-tunes open-weight models such as Llama, Mistral and Qwen.
| Criterion | On-premise | Private cloud (VPC) | Public AI API |
|---|---|---|---|
| Where the model runs | Your own servers and GPUs | A dedicated tenant in your cloud account | The provider’s shared infrastructure |
| Data leaves your network | No | No — it stays in your cloud account | Yes — prompts are sent to the provider |
| Infrastructure to run | GPU servers | Cloud GPU instances | None |
| Time to first prototype | Weeks, depending on hardware | Days to weeks | Hours |
| Best fit | Strict residency, air-gapped sites | Regulated teams already in the cloud | Non-sensitive data, quick experiments |
What is an autonomous AI agent and how is it different from a chatbot?
A chatbot responds to messages. An autonomous AI agent makes decisions, calls tools, executes multi-step workflows and completes routine tasks on its own — such as resolving support tickets end-to-end or handling logistics exceptions around the clock. Actions above an agreed cost or risk threshold are escalated to a person for approval.
How does Naringhi handle data privacy and compliance?
We deploy private LLMs on your own infrastructure (on-premise or your private cloud tenant), and client data is not sent to external AI providers unless you agree in writing. We support GDPR, HIPAA and SOC 2 workflows, and sign NDAs and DPAs before any data is exchanged.
Do we retain full intellectual property (IP) and source code ownership?
Yes. Under our standard agreement you own 100% of the project deliverables: source code, infrastructure configuration, documentation, prompts and evaluation sets, and any model weights fine-tuned on your data. There is no vendor lock-in — everything runs in your environment, and your team can operate, change or move it without us. Open-source components and open-weight base models keep their own licences, which we list for you at hand-over.
Can Naringhi integrate with our existing legacy ERP, CRM, or on-premise databases?
Yes. We connect through the interfaces your systems already offer: REST or SOAP APIs, direct database access (read replicas where possible), message queues, scheduled file exports, or the Model Context Protocol (MCP) for AI agents. Where a system has no usable interface, we build a thin integration layer around it, so the core system does not need to be replaced. Each system is assessed during discovery, before any fixed price is agreed.
What is Edge-Native software?
Edge-Native software runs its logic on globally distributed edge servers close to each user instead of a single central origin. This cuts round-trip latency for global audiences and improves Core Web Vitals, which matter for both user experience and search ranking.
What is GEO (Generative Engine Optimization)?
GEO is the practice of optimizing a brand’s content and structured data so that AI engines such as ChatGPT, Perplexity, Google AI Overviews, Claude and Gemini can understand it and cite it as a source. It combines entity schema, factual Q&A content, author credibility signals and technical clarity.
How much does a custom AI project cost?
Cost depends on scope, data volume and infrastructure. A focused AI agent or RAG system typically starts in the mid-five-figure USD range; enterprise on-premise LLM deployments are scoped individually. We provide a fixed-price architecture proposal after a free 30-minute consultation and discovery.
How long does a typical engagement take?
Discovery and architecture take 2–3 weeks. A first production-ready AI agent or Edge-Native application typically ships 8–12 weeks after that. Enterprise on-premise LLM rollouts take 4–6 months, including fine-tuning, evaluation and integration.
Which industries does Naringhi serve?
We work with B2B organizations in fintech and banking, healthcare, legal, logistics, manufacturing, SaaS and professional services. Our on-premise and private-cloud delivery model suits regulated industries where data residency is a hard requirement.