Question 1

OpenAI vs Anthropic Claude vs open-source — which should we use?

Accepted Answer

The honest answer is that it depends on three variables: task type, data-privacy requirements, and cost ceiling. OpenAI GPT-4o is the default recommendation for most product teams — it has the widest ecosystem, the most mature function-calling implementation, and the largest community of practitioners who have solved the problems you will hit. Anthropic Claude is our first choice for long-context tasks — processing large documents, multi-turn reasoning over extensive context windows — and for teams that want a provider with a strong public safety track record. Open-source models (Llama 3, Mistral, Phi-3) make sense when you have a data-residency requirement that rules out third-party APIs, when you want to fine-tune on proprietary data without your training set leaving your VPC, or when you need to drive inference cost below what managed APIs offer at scale. In practice, most production systems we build use OpenAI or Claude as the primary model and an open-source model as a cost-optimised fallback for high-volume, lower-stakes tasks. We recommend starting with GPT-4o unless you have a specific reason not to — you can switch providers later if the need arises.

Question 2

What is the difference between an AI integration and custom AI development?

Accepted Answer

An AI integration means adding a specific, bounded capability to an application that already exists — dropping a semantic search endpoint into your Rails app, adding a summarisation feature to your document management system, or wiring a contextual chatbot into your support portal. The existing application stays intact; we add an API route, a prompt template, and a vector index if retrieval is involved. Timeline is one to three weeks. Cost is $5k–$15k. Custom AI development means building something new from the ground up — a product whose core value proposition is the AI behaviour itself. That requires architecture decisions about retrieval design, agent orchestration, evaluation strategy, multi-tenancy, observability, and cost management that do not apply to a simple integration. Timeline is six to twelve weeks for an MVP and three to six months for a production platform. The right choice depends on whether AI is a feature in your existing product or the product itself. If you are unsure, a Free AI Audit call will answer it in under an hour.

Question 3

How do you handle data privacy and security for AI projects?

Accepted Answer

This is the question that separates a serious AI engineering team from a rapid-prototyping shop, and it is one we take seriously from architecture day. On the provider side, we use the no-training opt-out endpoints available on OpenAI Enterprise and Anthropic API by default — your data does not train their next model. On the infrastructure side, data in transit is TLS-encrypted; data at rest uses AES-256 encryption on the vector store and the document store. For multi-tenant RAG systems, we implement index isolation at the namespace or collection level so one tenant's embeddings are never retrievable by another tenant's queries — even if they share the same vector database. For teams with strict data-residency requirements — EU GDPR, India DPDP Act, or US federal requirements — we architect with region-locked infrastructure on AWS or GCP and can route inference through Azure OpenAI's regional deployments for European data residency. We document the full data-flow diagram, encryption boundaries, and audit-log strategy in the architecture phase. SOC-2-ready posture is available as part of the Production AI Platform tier.

Question 4

How does RAG on private data actually work — and how do you make it accurate?

Accepted Answer

RAG — retrieval-augmented generation — is the pattern of fetching relevant passages from your document corpus at query time and including them in the model's context window, rather than asking the model to recall facts from training. The quality of a RAG system lives and dies in three places: chunking strategy, retrieval quality, and evaluation. Chunking strategy determines how documents are split before embedding — naive fixed-size chunking loses context at boundaries; semantic chunking, hierarchical chunking, and sentence-window chunking each trade off differently against retrieval precision and index size. Retrieval quality depends on the embedding model, the vector database (Pinecone, Weaviate, and pgvector each have different performance profiles at scale), and whether you are using dense retrieval alone or a hybrid that combines dense embeddings with BM25 keyword search. Accuracy comes from evaluation — we build an eval harness with a golden Q&A set and measure precision, recall, and answer faithfulness before expanding scope. Citation-linked answers — where the UI shows which document passage supported each claim — are included as a default because they let end users verify answers rather than trust blindly.

Question 5

How much does AI development cost?

Accepted Answer

The honest answer is that it depends heavily on what you are building. An AI integration — dropping a single feature (semantic search, summarisation, a chatbot) into an existing application — costs $5k–$15k and ships in one to three weeks. A custom AI MVP — a full application with retrieval or agent design, evaluation harness, observability, and a 30-day post-launch window — costs $20k–$80k and ships in six to twelve weeks. A production AI platform — multi-tenant, with RBAC, usage billing, fine-tuning, multi-model fallbacks, and a SOC-2-ready posture — starts at $80k and scales to $500k+ for complex, high-availability platforms with ongoing engineering retainers. For a detailed cost breakdown by tier with real numbers from shipped projects, see our [AI development cost guide for 2026](/blog/ai-development-cost-2026). Pricing is quoted in USD, fixed-scope, with a weekly-demo delivery cadence so you can see exactly what you are getting before each payment milestone. The fastest way to get an accurate number is the Free AI Audit call.

Question 6

Which AI model do you recommend — OpenAI, Anthropic, or open-source?

Accepted Answer

There is no single right answer — the model you pick should match your quality bar, volume profile, compliance requirements, and tooling stack. For most production deployments in 2026 we route across at least two providers: a cheap-and-fast model (GPT-4o-mini or Claude Haiku 4.5) for high-volume calls and a smart-and-expensive model (GPT-4o or Claude Sonnet 4.5) for hard cases. OpenAI wins on multimodal (vision + text + audio in one stream) and broad ecosystem support. Anthropic wins on long-context retrieval, instruction-following nuance, and tool-use reliability. Open-source (Llama 3.1, Mistral, Qwen) wins on cost at scale (above 5M tokens/day) and strict data residency. For a detailed practitioner comparison including cost-per-token tables and capability matrices, see our [OpenAI vs Anthropic vs open-source guide](/blog/openai-vs-anthropic-vs-opensource). We will recommend a specific routing strategy for your project on the Free AI Audit call.

Question 7

Can you build a RAG system for our private data?

Accepted Answer

Yes — RAG (retrieval-augmented generation) is the dominant pattern for AI systems that answer from private data without fine-tuning, and we ship RAG layers in most production projects. A typical Vellumarc RAG stack uses LlamaParse for messy PDF ingestion, recursive character chunking with hierarchical retrieval, OpenAI text-embedding-3-small for embeddings, Supabase pgvector or Pinecone for the vector index, hybrid search (vector + BM25), Cohere rerank-v3 for re-ranking, and routed generation across Claude Haiku and Sonnet depending on query complexity. Build typically runs 4–8 weeks depending on corpus size and complexity. Quality benchmark is 80–90% answer correctness on typical knowledge-base use cases. For the full architecture playbook including all seven pipeline stages, evaluation strategy, and common failure modes, see our [production RAG systems guide](/blog/building-production-rag-systems).

Question 8

What can you ship in 6 weeks vs 12 weeks for an AI MVP?

Accepted Answer

In six weeks we can ship a complete, deployed AI application with a single retrieval source, a single model, a core UI, and an evaluation harness covering the primary user flows. What that looks like in practice: a RAG-powered internal knowledge base with document upload, semantic search, citation-linked answers, and a basic admin panel — or an AI copilot for a specific workflow with function calling, streaming UI, and conversation history. What we do not try to fit into six weeks: multi-tenant isolation, RBAC, usage billing, fine-tuning, or multi-model fallbacks — those are twelve-week-plus scope. The most common cause of timeline slip in AI projects is not engineering complexity — it is data readiness. If your documents are in inconsistent formats, your golden evaluation set does not exist yet, or access to the relevant systems requires procurement cycles, those are the long poles. We run a data-readiness check in the discovery workshop and flag blockers before the build starts, not three weeks into the sprint.

Question 9

What does ongoing maintenance and prompt tuning involve after launch?

Accepted Answer

AI systems degrade in ways that traditional software does not: model providers update their underlying models, your document corpus grows and goes stale, user query patterns shift, and what was a 92% accuracy system six months ago may be 84% today without anyone touching the code. Our monthly AI engineering retainer covers four areas. First, eval monitoring — running your golden test suite against the live system on a weekly cadence and alerting when accuracy or latency crosses a threshold. Second, prompt and retrieval tuning — adjusting system prompts, retrieval parameters, chunk sizes, and re-ranking logic as the failure modes evolve. Third, model upgrade management — when OpenAI ships a new model version or Anthropic deprecates a Claude variant, we handle the migration, regression-test against your eval suite, and validate before the cutover. Fourth, cost optimisation — as your usage scales, we look for opportunities to route lower-stakes queries to cheaper models and batch non-latency-sensitive workloads. Retainer pricing is available on request and is calibrated to the size and complexity of your production system.

Question 10

When does fine-tuning make sense vs prompt engineering or RAG?

Accepted Answer

Fine-tuning is the right tool in a narrow set of circumstances: when you need the model to consistently produce a very specific output format or style that prompt engineering cannot reliably enforce at scale; when you have thousands of labelled input-output examples and the domain is sufficiently specialised that the base model underperforms; or when latency or cost requirements mean you need a smaller, faster model that performs at the level of a larger one. In most cases — and this is the uncomfortable truth for teams that have read about fine-tuning as a silver bullet — prompt engineering combined with RAG will outperform fine-tuning on knowledge-intensive tasks, be easier to maintain, and cost less to develop. Fine-tuning does not inject facts into a model reliably; it changes behaviour and style. If your problem is "the model doesn't know our internal product catalogue," RAG is the answer, not fine-tuning. If your problem is "the model's output doesn't match our brand tone and structured format," fine-tuning may be the answer — but only after you have exhausted few-shot prompting and retrieval augmentation. We make this call explicitly in the architecture phase and document the reasoning.

AI Development Company in India for Global Brands

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How Much Does Custom AI Development Cost in 2026? Honest Pricing Guide

OpenAI vs Anthropic vs Open-Source: Which Model for Production AI?

Building Production-Grade RAG Systems: A Practical Guide for 2026

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