Built for the case that does not fit an off-the-shelf tool. $3,999/mo retainer, one senior engineer, work made for hire.
- Scope
- Build
- Evaluate
monthly retainer
Who this is for
You looked at the off-the-shelf AI tools and none of them quite fit. Your data lives somewhere they cannot reach, your process has a step they do not support, or your use case is specific enough that a generic product would need to be bent out of shape to work. Custom AI development means building the exact thing your situation calls for, not adapting your situation to fit someone else's product.
The pain today
- Off-the-shelf AI tools cover the common case and stop at your specific one
- Bending a generic tool to fit an unusual workflow costs more than building it right
- No off-the-shelf product reaches the specific data or system your work actually depends on
- A subscription to five different point tools costs more than one integrated build
- Nobody available to own a custom build end to end without a large team behind it
The outcome you get
- A system built for your exact process, not a generic template stretched to fit
- Direct integration with the data and systems you already depend on
- One senior engineer accountable for the whole build, start to finish
- A single monthly price instead of several point-tool subscriptions
- Full ownership of the code, infrastructure, and documentation from day one
When custom is actually the right call
Custom AI development is not always the answer. If an off-the-shelf tool covers your case well, it is usually the cheaper and faster route, and I will say so honestly on the first call. Custom makes sense once you have checked the generic options and found a real gap: your data lives somewhere the tool cannot reach, your workflow has a step the tool does not support, or your volume and specificity make a generic template genuinely worse than building the exact thing.
That gap check happens before any retainer starts, not after.
A system built for your exact process, not a generic template stretched to fit
What building custom actually involves
A custom build starts with the same three questions every time: what information does the system need to see, what should it do with that information, and where does the result need to land. From there, the work covers model choice between OpenAI and Claude based on what the task needs, integration with your existing data (PostgreSQL, MongoDB, or whatever you already run on), and deployment on infrastructure you own, typically AWS or Vercel.
I have built this kind of system across very different situations: a document pipeline moving over 2,000,000 records into a single destination in under 50 seconds per sync for one of Brazil's largest veterinary networks, and a CRM automation using Claude AI that grew a lead base by over 500% for a digital marketing agency. Different problems, same underlying discipline.
30+: Active users.
Why one engineer instead of several point tools
A common alternative to a custom build is subscribing to several point tools, one for scoring, one for drafting, one for routing, and wiring them together with no single person accountable for the whole thing. That approach often costs more in total subscription fees than a single custom build, and when something breaks, no one owns the fix across tool boundaries.
A custom build under one retainer means one person is accountable for the whole system, end to end, at a single published monthly price.
What you keep, and what it costs to maintain
Everything built, the code, the infrastructure setup, and the documentation, is yours under Work Made for Hire terms from the moment you pay. There is no lock-in to a platform you do not control. Ongoing costs beyond the retainer are limited to what the model providers themselves charge for usage (OpenAI or Claude), billed directly to your account rather than marked up.
Pricing, delivery, and how to start
Custom 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. Availability runs one customer at a time by design, with new customers joining a waitlist for the next quarterly slot.
Recent proof
A comparable engagement, delivered and documented.
An AI knowledge base your whole team uses via MCP
A personal library for Skills, Agents, and Rules, built once, used across Claude, Cursor, and any MCP-compatible AI tool.
Read the case studyRelated services
The same work, framed for a different situation.
Frequently asked questions
The questions prospects ask before they book.
Check the generic options first. Custom makes sense once you find a real gap: your data lives somewhere the tool cannot reach, your workflow has a step it does not support, or a generic template genuinely does not fit your case. That check happens honestly before any retainer starts.
A system built around your specific process and your specific data rather than a template stretched to fit. It could be a scoring model, a document pipeline, a chat interface grounded in your information, or something that does not have a common name yet because it is specific to how your business actually works.
OpenAI or Claude, chosen based on what your task actually needs rather than a default. Both are tested against real examples from your case before either is committed to.
Often, yes, once you add up what several separate subscriptions cost and account for nobody owning the connections between them. A single retainer at $3,999 a month covers the whole build with one person accountable for the result.
That is fine. Readers of this page range from someone with only an idea through to an established business. The engagement is scoped so you are not required to have technical staff to run it; I own the build end to end.
There is a 14-day money-back guarantee. If it is not working for you in the first two weeks, you get a full refund, and you can cancel anytime after that with no long-term contract.