New content, drafting, and personalization features built and deployed as part of your product. $3,999/mo retainer.
- Scope
- Build
- Evaluate
monthly retainer
Who this is for
You want to build a new capability that did not exist before: a feature that drafts, personalizes, or generates something specific to your business, not a bolt-on to an existing product. Generative AI development means designing and building that capability from the ground up, choosing the model, the data it draws from, and the review step around it, together, before a line of code ships.
The pain today
- An idea for a generation feature has no clear starting point without engineering help
- Generic AI writing products cannot be shaped around your specific content and voice
- Building the wrong review process around generated content creates a real risk, not a shortcut
- No one to weigh model choice, cost, and output quality against each other honestly
- A first version that never gets past a prototype because nobody owns turning it into something real
The outcome you get
- A generation feature designed around your content and your voice, not a generic template
- A model choice tested against real examples before it is committed to
- A review step sized to the actual risk of the content, not skipped for speed
- A working feature deployed on infrastructure you own, not a prototype that stalls
- Full ownership of the build under Work Made for Hire terms from day one
Building the feature, not just calling the API
Calling a generation API is the easy part; most of the work worth paying for sits around that call. What information should shape the output. How should the output be reviewed before it reaches a real person. What happens when the model produces something wrong, and how is that caught before it causes a problem. Generative AI development is the discipline of answering those questions and building the system around the answers, not just wiring up a call and hoping for the best.
A generation feature designed around your content and your voice, not a generic template
Choosing a model honestly, not by default
OpenAI and Claude both handle generation well, and the right one depends on your content type, your budget, and the tone you actually need. I test both against real examples from your case before committing to either, rather than defaulting to whichever is easiest to integrate. Cost matters too: a feature that generates thousands of outputs a month needs a different model choice than one generating a handful a day.
+500%: Lead base growth.
Sizing the review step to the actual risk
Not every generated output needs the same level of review. A first-draft internal note carries little risk if it is slightly off; a customer-facing message or a legal-adjacent document carries much more. I size the review step to match: some output can post automatically once confidence is proven, other output always stops for a person first. Getting that sizing wrong in either direction, too loose or too cautious, is the most common mistake in a first build, and it is a design decision made with you up front.
From prototype to something real
A lot of generative AI ideas stall at the prototype stage: a working demo that never becomes a deployed feature because nobody owns turning it into production infrastructure. I build directly on infrastructure you own, typically AWS or Vercel with a Next.js or Node.js front door and PostgreSQL or MongoDB underneath, so the result is a working feature inside your product, not a standalone demo that gets shelved.
Pricing, delivery, and ownership
Generative AI development runs under the AI Development retainer at $3,999 a month, delivered in cycles of two to four days with daily async updates and a response inside 24 hours. The engagement carries a 14-day money-back guarantee, and you can cancel anytime after. The code, the model configuration, and the documentation are yours under Work Made for Hire terms from the day you pay.
Recent proof
A comparable engagement, delivered and documented.
A custom CRM that turned Google Maps into a lead machine
A custom CRM that captures leads from Google Maps, reaches them via WhatsApp, and uses AI to suggest replies. Lead base grew over 500%, 250 new leads a day.
Read the case studyRelated services
The same work, framed for a different situation.
Frequently asked questions
The questions prospects ask before they book.
Development means building a new capability that did not exist before, designed from scratch around your content. Integration means adding an existing generation capability into a product you already run. Many engagements involve both; the starting point differs depending on whether the feature exists yet.
OpenAI or Claude, tested against real examples from your case before either is committed to. The right choice depends on your content type, your budget, and the volume of output the feature needs to produce.
That depends on the risk of the content. Low-risk internal drafts can post with light review; anything customer-facing or higher-stakes gets a review step by default. Sizing that correctly is part of the build, decided with you before anything ships.
That is a fine starting point. Part of the engagement is turning a working demo into a deployed feature on infrastructure you own, rather than leaving it as a prototype that never becomes real.
The AI Development retainer is $3,999 a month, with work landing in cycles of two to four days. There is a 14-day money-back guarantee, and you can cancel anytime after.
You do, in full, under Work Made for Hire terms, from the day you pay. That covers the code, the model configuration, and the documentation.