AI Consultant · Raleigh, NC

Hire an AI consultant in Raleigh.

Research Triangle companies -- SAS spinouts, Red Hat alumni ventures, NC State and Duke research commercializations -- have real technical talent. The strategic mistakes they make are not technical. They ship AI features without checking what competitors actually built. They raise money on research prototypes without an honest assessment of the production gap. They default to a model choice without running the cost math.

An independent consultant who has done these assessments before can compress months of internal deliberation into a written analysis in three to five weeks. The deliverable is not a recommendation to buy a product. It is an analysis your technical team can act on or argue with.

Fixed-price engagement, scoped to your question. Five to eight weeks. Written deliverables yours to keep.

How we work

Tell us about your AI strategy or architecture question.

What technically-informed companies still get wrong.

SaaS companies building AI features skip the competitive intelligence step. Not because they lack the capability to do the research, but because it feels less productive than building. The result: shipping an AI feature that matches what a competitor launched 18 months ago, without knowing whether that feature actually worked for the competitor's customers.

The Research Triangle's university spinout pipeline creates a specific variant of this problem. A research prototype with published validation results looks like a production-ready product to a founder who built it. It rarely is. The gap between a prototype that works on a curated dataset and a product that works reliably on live customer data is almost always larger than it appears.

Open-source vs. proprietary model selection is often made by instinct rather than analysis. Engineers with strong open-source values default to self-hosting. Engineers who have worked in well-funded environments default to frontier API providers. Neither instinct is wrong in general, but both can be wrong for a specific use case when the cost math is actually run.

The right answer depends on token volume, latency requirements, data privacy constraints, and the quality gap between model tiers for your specific task. A written analysis that runs those numbers takes two to three days and produces a defensible recommendation.

Research prototype vs. production: the honest gap.

A research prototype that performs well on a held-out test set from the training distribution is not evidence of production readiness. It is evidence the model learned something from the training data. Those are different claims.

The gaps that matter in a production assessment are: data pipeline reliability under live input conditions, latency requirements vs. inference time, evaluation benchmarks for out-of-distribution inputs, and the documentation required to make the model maintainable by a team that did not build it. A Duke or NC State spinout that has cleared IRB review and published validation results has done none of these things. That is not a criticism. It is what research produces.

Raising a seed round on AI capability claims before this analysis is done creates a specific kind of investor risk. If due diligence surfaces a significant production gap after the raise, the company is committed to a roadmap that may require re-architecture. The assessment is most useful before fundraising materials are written, not after a term sheet is signed.

Open-source vs. proprietary model economics.

The API is almost always cheaper at moderate volume

Below roughly 10 million tokens per month, self-hosting a model on GPU infrastructure costs more than the API after accounting for compute, engineering overhead, and operational burden. Most early-stage SaaS products are well below that threshold.

Data privacy changes the calculation

If your use case involves data that cannot leave your infrastructure -- customer PII, regulated health information, trade secrets -- the cost comparison is irrelevant. Self-hosting is the only viable option and the question shifts to which open-source model performs well enough on your task.

Quality gap depends on the task

For complex reasoning, code generation, and multi-step analysis, GPT-5 class models outperform Llama 4 8B by a meaningful margin. For structured extraction from well-formatted documents, the gap is much smaller. The model selection should follow the task requirements, not a general preference.

Fine-tuning adds cost and complexity

A fine-tuned open-source model on your proprietary data can outperform a frontier API model on your specific task, but fine-tuning requires a labeled dataset, compute for training runs, and ongoing maintenance when the base model is updated. These costs are rarely included in the initial comparison.

How we work.

1
Discovery call
One hour. We map the technical question -- prototype assessment, model selection, competitive analysis, or strategy -- and scope the engagement. Fixed price after this call.
2
Technical review
For prototype assessments: access to the codebase, training data documentation, and evaluation benchmarks. For model selection: usage projections, data privacy requirements, and latency specs. For competitive analysis: competitor product access and your feature roadmap.
3
Written analysis
The analysis is written to be shared. It includes the methodology, the data behind any cost calculations, and the reasoning behind prioritization decisions. Your team can agree with it, argue with it, or use it as a starting point for their own analysis.
4
Review session and handoff
Walk through the findings with your technical and product leads. All materials are yours. No retainer, no ongoing obligation.

Questions about AI consulting in Raleigh.

How do you assess whether a research AI prototype is actually convertible to a production system -- what are the common gaps?: Four gaps appear in almost every research-to-production assessment. First, data pipeline reliability: a research prototype typically runs on a fixed, curated dataset. A production system needs a data pipeline that ingests live data, handles schema changes, and recovers from upstream failures. That infrastructure does not exist in the prototype and takes weeks to months to build properly. Second, latency requirements: a research model that takes 30 seconds to return a result may be acceptable for a paper. A product used by customers typically needs sub-second or low-single-digit-second response times. If the model was not designed with inference latency in mind, hitting those targets may require re-architecture. Third, evaluation benchmarks: research models are evaluated on held-out test sets from the same distribution as the training data. Production systems encounter distribution shift -- real user inputs that look different from the training data. A model without an evaluation suite designed for out-of-distribution inputs will degrade in production without obvious warning signs. Fourth, model cards and documentation: production AI requires documentation of training data, known failure modes, and evaluation results that research prototypes rarely have. The honest assessment of a prototype identifies which of these gaps exist and gives a realistic estimate of the engineering work required to close them.
What does a total cost calculation look like for running Llama 4 on your own hardware vs. paying for GPT-5 API?: The calculation has four components. First, inference compute cost: estimate your monthly token volume and calculate the API cost at current pricing (GPT-5 is priced per input and output token; check the current OpenAI pricing page for exact figures). Then calculate the equivalent compute cost for self-hosted Llama 4: an A100 80GB GPU runs approximately $2.50 to $3.50 per hour on major cloud providers, and an 8B parameter Llama 4 model can process roughly 1,000 to 2,000 tokens per second on a single A100 depending on quantization. Second, operational overhead: self-hosted models require someone to manage infrastructure, handle GPU failures, update the model, and monitor performance. At a Research Triangle SaaS company, this is typically 10 to 20 percent of a senior engineer's time. Third, model quality: Llama 4 8B performs comparably to GPT-3.5-class models on general tasks. GPT-5 performs significantly better on complex reasoning tasks. If your use case requires GPT-5 quality output, the self-hosted alternative would be Llama 4 70B, which requires significantly more GPU memory and compute. Fourth, data privacy: if your use case involves data you cannot send to a third-party API -- customer PII, trade secrets, regulated health data -- the cost comparison is moot. Self-hosted is the only viable option. At moderate token volumes (under 10 million tokens per month), the API is almost always cheaper after accounting for operational overhead. At very high volumes or with strict data privacy requirements, self-hosted becomes worth analyzing seriously.
How do you evaluate AI competitive intelligence for a SaaS company -- what should you know about competitors before building?: Three things to investigate before committing to an AI feature roadmap. First, what the competitor's AI feature actually does vs. what the marketing claims. Read their changelog and their technical documentation, not their product page. Many AI feature announcements describe aspirational capability. The changelog tells you what shipped. If a competitor announced an AI feature six months ago and it has not appeared in the changelog, it has not shipped. Second, how the competitor is positioning the AI feature commercially -- is it included in the base product, a premium tier, or a separate add-on? That tells you whether AI is core to their product strategy or a defensive feature built to check a box. Third, what customer reviews say about the AI feature. G2, Capterra, and Reddit product communities often have candid assessments of whether an AI feature works as advertised. If customers are not mentioning the AI feature in positive reviews, it is probably not driving retention or acquisition. The strategic mistake is building an AI feature that matches what a competitor shipped without evaluating whether that feature is actually working for the competitor's customers.
What is the right AI strategy engagement for a Research Triangle company with strong technical talent but unclear strategic direction?: The problem this describes is not a technical problem. A team with strong technical talent can build almost anything. The missing piece is a clear theory of what AI investment produces a business outcome. The right engagement for this situation is a half-day structured workshop with the product and engineering leads, followed by a written use case analysis. The workshop maps each candidate AI use case to a specific customer problem, a measurable outcome (reduced churn, higher activation, faster time-to-value), and an honest estimate of the build effort. Most Research Triangle companies with strong AI talent have three or four credible use cases and six to eight ideas that sound interesting but do not have a clear business outcome. The written analysis separates them. The deliverable is a prioritized roadmap with the two or three use cases worth building in the next six months, a competitive analysis of what peers in the same SaaS category have actually shipped, and a model selection recommendation covering whether the use case warrants a frontier model API, a fine-tuned open-source model, or a simpler retrieval-based approach.

Ready to work through your AI strategy or architecture question?

Hire an AI consultant in Raleigh.

Fixed-price engagement, scoped to your question. Five to eight weeks. Written deliverables yours to keep.

What technically-informed companies still get wrong.

Research prototype vs. production: the honest gap.

Open-source vs. proprietary model economics.

The API is almost always cheaper at moderate volume

Data privacy changes the calculation

Quality gap depends on the task

Fine-tuning adds cost and complexity

How we work.

Discovery call

One hour. We map the technical question -- prototype assessment, model selection, competitive analysis, or strategy -- and scope the engagement. Fixed price after this call.

Technical review

For prototype assessments: access to the codebase, training data documentation, and evaluation benchmarks. For model selection: usage projections, data privacy requirements, and latency specs. For competitive analysis: competitor product access and your feature roadmap.

Written analysis

The analysis is written to be shared. It includes the methodology, the data behind any cost calculations, and the reasoning behind prioritization decisions. Your team can agree with it, argue with it, or use it as a starting point for their own analysis.

Review session and handoff

Walk through the findings with your technical and product leads. All materials are yours. No retainer, no ongoing obligation.

Questions about AI consulting in Raleigh.

How do you assess whether a research AI prototype is actually convertible to a production system -- what are the common gaps?

Four gaps appear in almost every research-to-production assessment. First, data pipeline reliability: a research prototype typically runs on a fixed, curated dataset. A production system needs a data pipeline that ingests live data, handles schema changes, and recovers from upstream failures. That infrastructure does not exist in the prototype and takes weeks to months to build properly. Second, latency requirements: a research model that takes 30 seconds to return a result may be acceptable for a paper. A product used by customers typically needs sub-second or low-single-digit-second response times. If the model was not designed with inference latency in mind, hitting those targets may require re-architecture. Third, evaluation benchmarks: research models are evaluated on held-out test sets from the same distribution as the training data. Production systems encounter distribution shift -- real user inputs that look different from the training data. A model without an evaluation suite designed for out-of-distribution inputs will degrade in production without obvious warning signs. Fourth, model cards and documentation: production AI requires documentation of training data, known failure modes, and evaluation results that research prototypes rarely have. The honest assessment of a prototype identifies which of these gaps exist and gives a realistic estimate of the engineering work required to close them.

What does a total cost calculation look like for running Llama 4 on your own hardware vs. paying for GPT-5 API?

The calculation has four components. First, inference compute cost: estimate your monthly token volume and calculate the API cost at current pricing (GPT-5 is priced per input and output token; check the current OpenAI pricing page for exact figures). Then calculate the equivalent compute cost for self-hosted Llama 4: an A100 80GB GPU runs approximately $2.50 to $3.50 per hour on major cloud providers, and an 8B parameter Llama 4 model can process roughly 1,000 to 2,000 tokens per second on a single A100 depending on quantization. Second, operational overhead: self-hosted models require someone to manage infrastructure, handle GPU failures, update the model, and monitor performance. At a Research Triangle SaaS company, this is typically 10 to 20 percent of a senior engineer's time. Third, model quality: Llama 4 8B performs comparably to GPT-3.5-class models on general tasks. GPT-5 performs significantly better on complex reasoning tasks. If your use case requires GPT-5 quality output, the self-hosted alternative would be Llama 4 70B, which requires significantly more GPU memory and compute. Fourth, data privacy: if your use case involves data you cannot send to a third-party API -- customer PII, trade secrets, regulated health data -- the cost comparison is moot. Self-hosted is the only viable option. At moderate token volumes (under 10 million tokens per month), the API is almost always cheaper after accounting for operational overhead. At very high volumes or with strict data privacy requirements, self-hosted becomes worth analyzing seriously.

How do you evaluate AI competitive intelligence for a SaaS company -- what should you know about competitors before building?

Three things to investigate before committing to an AI feature roadmap. First, what the competitor's AI feature actually does vs. what the marketing claims. Read their changelog and their technical documentation, not their product page. Many AI feature announcements describe aspirational capability. The changelog tells you what shipped. If a competitor announced an AI feature six months ago and it has not appeared in the changelog, it has not shipped. Second, how the competitor is positioning the AI feature commercially -- is it included in the base product, a premium tier, or a separate add-on? That tells you whether AI is core to their product strategy or a defensive feature built to check a box. Third, what customer reviews say about the AI feature. G2, Capterra, and Reddit product communities often have candid assessments of whether an AI feature works as advertised. If customers are not mentioning the AI feature in positive reviews, it is probably not driving retention or acquisition. The strategic mistake is building an AI feature that matches what a competitor shipped without evaluating whether that feature is actually working for the competitor's customers.

What is the right AI strategy engagement for a Research Triangle company with strong technical talent but unclear strategic direction?

The problem this describes is not a technical problem. A team with strong technical talent can build almost anything. The missing piece is a clear theory of what AI investment produces a business outcome. The right engagement for this situation is a half-day structured workshop with the product and engineering leads, followed by a written use case analysis. The workshop maps each candidate AI use case to a specific customer problem, a measurable outcome (reduced churn, higher activation, faster time-to-value), and an honest estimate of the build effort. Most Research Triangle companies with strong AI talent have three or four credible use cases and six to eight ideas that sound interesting but do not have a clear business outcome. The written analysis separates them. The deliverable is a prioritized roadmap with the two or three use cases worth building in the next six months, a competitive analysis of what peers in the same SaaS category have actually shipped, and a model selection recommendation covering whether the use case warrants a frontier model API, a fine-tuned open-source model, or a simpler retrieval-based approach.

Ready to work through your AI strategy or architecture question?