What is generative AI consulting?

Generative AI consulting is help designing and shipping systems built on large language models — retrieval augmented generation, LLM powered applications, evaluation pipelines, and the cost and latency engineering that makes them viable in production. It covers the architecture around the model rather than training a model from scratch.

How is generative AI consulting different from agentic AI consulting?

Generative AI work is about producing a correct answer from a model and your data: retrieval, grounding, evaluation, cost. Agentic AI work is about a system that takes multi-step actions on its own — tool calls, loops, persistent state, and the safety layer that autonomy demands. Most teams need the generative foundation working before agents are worth attempting.

How much do generative AI consulting services cost?

A focused discovery or architecture audit runs $5,000 to $15,000. A full system build runs $20,000 to $120,000 depending on corpus size, integration count, and compliance requirements. Ongoing fractional engagements run $4,000 to $14,000 a month. Scope drives the number more than duration does.

Do you build RAG systems or just advise on them?

Both, and the build is usually the point. Advice without an implementation tends to stall at the first retrieval edge case. Engagements typically start with an audit, move into rebuilding the highest-impact retrieval path, and hand the system over to your team with the evaluation harness that proves it works.

Our RAG demo works but production answers are wrong. What causes that?

Almost always retrieval, not the model. Fixed size chunking splits ideas in half, vector-only search misses exact terms, and without reranking the correct passage sits below the cutoff the model ever sees. The first step is measuring retrieval separately from generation so you know which half is failing.

Can you work with our existing engineering team?

Yes, and that is usually the better model. I embed for the audit and rebuild phases, then hand ownership over progressively. The goal is a team that can extend the retrieval and evaluation stack without me.

Service

Generative AI Consulting Services

Most generative AI projects do not fail at the model. They fail at retrieval, at evaluation, and at the cost curve nobody modelled before launch. I help teams take an LLM prototype that works in a demo and turn it into a system that answers correctly, proves it is answering correctly, and does so at a unit cost the business can defend.

What you walk away with

✓Retrieval architecture — chunking strategy, hybrid search, reranking, and the vector store choice justified against your corpus
✓Evaluation harness — faithfulness, answer relevancy, context precision and recall, run on real traffic rather than a curated demo set
✓Cost and latency model — token accounting per request, caching layers, and model routing so spend scales sub-linearly with usage
✓Observability — traces, span-level failure attribution, and the dashboards needed to debug a bad answer after the fact
✓Governance layer — output validation, escalation paths, and audit trails for regulated or customer-facing use
✓Handoff documentation and a 2-week post-launch support window

Engagement model

Retrieval & Evaluation Audit

Discovery Sprint

2 weeks. I measure what your current pipeline actually retrieves, where it fails, and what it costs per answer. You get a written architecture and a prioritised fix list. Fixed price.

Retrieval Rebuild

Pilot

4-6 weeks. Rebuild the highest-impact path — usually chunking, hybrid search and reranking — with an evaluation harness wired in so the improvement is measured, not asserted.

Production LLM System

Build

8-14 weeks. Full delivery: retrieval, evaluation, caching and routing, observability, governance, and the deployment pipeline. Timeline depends on corpus size and compliance scope.

Fractional Oversight

Scale

Optional monthly retainer for ongoing evaluation review, cost tuning, and architecture guidance as the product and the models underneath it change.

Frequently asked questions

What is generative AI consulting?: Generative AI consulting is help designing and shipping systems built on large language models — retrieval augmented generation, LLM powered applications, evaluation pipelines, and the cost and latency engineering that makes them viable in production. It covers the architecture around the model rather than training a model from scratch.
How is generative AI consulting different from agentic AI consulting?: Generative AI work is about producing a correct answer from a model and your data: retrieval, grounding, evaluation, cost. Agentic AI work is about a system that takes multi-step actions on its own — tool calls, loops, persistent state, and the safety layer that autonomy demands. Most teams need the generative foundation working before agents are worth attempting.
How much do generative AI consulting services cost?: A focused discovery or architecture audit runs $5,000 to $15,000. A full system build runs $20,000 to $120,000 depending on corpus size, integration count, and compliance requirements. Ongoing fractional engagements run $4,000 to $14,000 a month. Scope drives the number more than duration does.
Do you build RAG systems or just advise on them?: Both, and the build is usually the point. Advice without an implementation tends to stall at the first retrieval edge case. Engagements typically start with an audit, move into rebuilding the highest-impact retrieval path, and hand the system over to your team with the evaluation harness that proves it works.
Our RAG demo works but production answers are wrong. What causes that?: Almost always retrieval, not the model. Fixed size chunking splits ideas in half, vector-only search misses exact terms, and without reranking the correct passage sits below the cutoff the model ever sees. The first step is measuring retrieval separately from generation so you know which half is failing.
Can you work with our existing engineering team?: Yes, and that is usually the better model. I embed for the audit and rebuild phases, then hand ownership over progressively. The goal is a team that can extend the retrieval and evaluation stack without me.

Related writing

Production RAG: Why Retrieval Fails and How to Fix It

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RAG vs Fine-Tuning: The Decision Guide for Production

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RAG Evaluation: Metrics That Actually Matter in Production

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Related services

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