AI Consulting Services in Raleigh, NC Build, Deploy, and Secure Private AI

RAG (retrieval-augmented generation) keeps your data separate from the model and retrieves relevant pieces at query time. Fine-tuning bakes specific knowledge or behavior into the model weights during a training process. RAG is the right choice when your data changes frequently, when you need the model to cite specific source documents, or when the volume of data is too large to fit in a model's context window. Fine-tuning is the right choice when you need the model to consistently adopt a specific style, terminology set, or task format that differs substantially from its default behavior. Most production systems we build use RAG for knowledge retrieval and may add fine-tuning for style and format consistency on top of that base.

There are two approaches we use depending on the sensitivity level. For moderate-sensitivity data, we design the RAG retrieval layer so that only the specific chunks relevant to a query are included in the API call, not the full document. We also use data sanitization steps to strip identifying information before it reaches the API. For high-sensitivity data, particularly PHI and CUI, we run the full system on local or on-premises infrastructure using open-source models on our GPU fleet or your own hardware. No call to a third-party API happens at all in that architecture.

A proof of concept for a well-defined use case typically takes two to four weeks. A production build with full engineering, security controls, compliance documentation, and handoff runs three to six months for most projects. Timeline is primarily driven by the clarity of the use case, the condition of your data, and how much integration work is required with your existing systems. We scope before we start, and the scope drives the timeline.

It depends on the approach. RAG systems do not require training data: they use your existing documents as the knowledge source. Fine-tuning requires labeled training examples, which we help you create and quality-check as part of the engagement. Many systems use RAG with no fine-tuning and work effectively using base models from Anthropic, OpenAI, or open-source providers. We assess whether fine-tuning is actually needed for your use case before recommending it, because it adds cost and complexity that is not always justified.

For organizations already running PostgreSQL, pgvector is frequently the right choice because it adds vector search to an existing database without adding infrastructure complexity. Weaviate is a good fit for systems that need multi-tenancy, complex filtering alongside vector search, or hybrid keyword-plus-vector retrieval. Qdrant performs well under high query volumes and has a straightforward API. We recommend based on your existing infrastructure, query volume targets, and filtering requirements, not based on which is newest or most popular at the moment.

Private inference pricing depends on the model size you need, the query volume you are targeting, and whether your use case requires dedicated capacity or can share infrastructure. We scope these costs during the engagement conversation. As a general frame: private inference costs more per query than commercial APIs at low volume, but becomes cost-competitive or cheaper at high volume, particularly for use cases with high data sensitivity that would otherwise require expensive compliance architecture on commercial platforms. We model the cost comparison honestly during scoping.

We design error handling, fallback behavior, and monitoring into the system from the start. For production systems, this means defined behavior when the model returns an output that fails validation, when retrieval returns no relevant results, when an API call fails or times out, and when output confidence metrics fall below a threshold. We also build human escalation paths for consequential decisions so that a system failure routes to a human rather than producing a bad automated outcome. Monitoring covers latency, error rates, output quality sampling, and retrieval quality metrics.

Yes. Most of our work is integration work rather than greenfield applications. We design API layers that connect AI capabilities to your existing CRM, ERP, document management system, or custom internal tools. We build with your existing authentication and authorization model so AI features respect the same access controls as the rest of your application. We provide documentation for your internal engineering team to maintain the integration over time, and an optional support retainer for ongoing changes.

The first step is a scoping conversation where we understand what you are trying to build, what data is involved, what your compliance environment looks like, and what definition of done looks like for your project. That conversation is free. From there we put together a scoping document that outlines the approach, timeline, and investment range for your review before any commitment. Contact us through the form below or call (919) 348-4912 to set up the initial conversation.

If you come to us with a specific use case and it is clear you have done the strategy work already, we proceed directly to scoping the implementation. If during that conversation we identify gaps in the strategy layer, data that is not ready, compliance obligations that were not accounted for, or a use case definition that would benefit from refinement, we say so before starting engineering. We would rather slow down to fix a strategy problem than build the wrong thing correctly. Our AI advisory page describes the strategy work separately for teams earlier in the process.