GENERATIVE AI CONSULTING + ADVISORY
I help brand and growth teams decide where generative AI is actually useful, prove the workflow on real output, and turn the parts that work into a repeatable production system.
12+
years in growth and GTM
15+
brands worked with
1.2M+
followers on Meta AI
4
startups founded · ex-YC
ANSWER FIRST
A generative AI consultant helps a team choose a valuable use case, test it against production conditions, and design a workflow the organization can operate responsibly.
Useful consulting begins with the business job, not a tour of popular tools. The first questions are what the team is trying to produce or improve, who owns the result, which inputs are trusted, where quality currently fails, and what cannot be delegated to a model. That diagnosis prevents a fashionable prototype from becoming an expensive workflow with no durable purpose.
My preferred engagements combine advisory with hands-on implementation. A small, representative pilot exposes the real constraints: model reliability, brand consistency, review time, rights, data access, integration effort, and the difference between an impressive demo and repeatable output. The recommendation is then based on observed production behavior rather than a generic slide deck.
The final handoff should make decisions legible. Teams need documented inputs, model choices, prompts or system instructions, evaluation criteria, review gates, failure handling, roles, and maintenance expectations. The aim is not permanent dependency on a generative AI consultant; it is a system the team understands and can improve.
WATCH, THEN JUDGE
Consulting claims are easy to write and difficult to verify. These finished outputs show the hands-on production context behind the advisory work while keeping their evidence boundary explicit: they demonstrate craft and systems thinking, not undisclosed client outcomes.
A 73-second horizontal launch film that demonstrates the production side of the practice: shaping an idea, sustaining a visual direction, assembling a longer cut, and finishing for an actual product narrative.
What to inspect
One finished concept from a six-direction SaaS campaign archive. It provides a concrete example of why model selection, references, creative direction, iteration, and post-production must be considered as one workflow.
What to inspect
FIT CHECK
01
Your team is testing models, but quality, ownership, and production steps still change from project to project.
02
You want to validate AI video, images, UGC, or content automation on an actual campaign before scaling it.
03
You need model selection, creative direction, growth context, and implementation decisions in the same room.
SCOPE
Identify the jobs worth automating, define quality bars, and map the handoffs from brief to approved output.
Choose the stack for the job instead of forcing every project through the same fashionable model.
Build the first working campaign or asset set so the recommendation is tested against real constraints.
Turn proven steps into an agentic or CLI-based system with clear human review points.
HOW IT WORKS
01
Clarify the business goal, audience, current workflow, constraints, and definition of good.
02
Create a small, production-representative pilot and document what succeeds or fails.
03
Standardize the models, prompts, review gates, templates, and responsibilities.
04
Hand over a workflow the team can understand, operate, and improve.
QUALITY BAR
A generative AI consultant should make the decision process clearer, reduce avoidable experimentation, and leave evidence that the proposed workflow works under the team's real constraints.
01
Define the current effort, bottleneck, quality bar, volume, risk, and expected operational value before selecting tools or promising automation.
02
Pilots should use realistic source material, formats, stakeholders, and review requirements. Sanitized demo inputs hide the failure modes that matter later.
03
Accuracy, brand fit, continuity, claims, rights, sensitive data, approval ownership, logs, and fallbacks need explicit treatment rather than a vague human-in-the-loop promise.
04
A useful handoff records architecture, instructions, examples, quality checks, owner responsibilities, known failure modes, and what should trigger future review.
PROOF, NOT PROMISES
BUYER GUIDES
BUYER DECISION
The right delivery model depends on whether the hard problem is deciding what to build, producing a finished campaign, operating a recurring system, or simply exploring a tool.
01
Use a generative AI consultant when the team needs to prioritize use cases, compare approaches, design a pilot, or make architecture and governance decisions.
02
If the brief already calls for a finished video, image set, or campaign asset, a production scope is clearer than an open-ended advisory engagement.
03
An internal owner is essential when the workflow will touch sensitive data, publish frequently, require daily judgment, or become core operating infrastructure.
04
Individuals can test ideas directly when the output is disposable, the inputs are safe, and there is no requirement for production consistency, governance, or team adoption.
STRAIGHT ANSWERS
A generative AI consultant connects a business goal to a workable AI system. That can include selecting use cases and models, designing the workflow, creating a production pilot, defining review gates, and helping the team adopt what works.
Both are possible. My strongest engagements combine advice with a real pilot, because working output reveals constraints that a slide deck cannot.
No. AI video and images are a major part of the work, but engagements can also cover AI UGC, content strategy, model selection, and agentic content automation.
Availability changes as I build Masonry AI and work with selected brands. Send the problem, desired outcome, timeline, and budget range by email and I will confirm fit and timing.
Send the workflow you want to improve, what the finished result needs to achieve, and the constraints your team is working with.
Email Gaurav →