You will get a scoping sprint and working prototype for your AI SaaS
Rising Talent
Project details
Most AI SaaS ideas die in the build, not the idea. Someone commits the budget, the work starts before anyone agreed what it actually needed to do, and months later the thing technically runs but solves the wrong problem. The expensive mistakes are almost always made in the first week, before a line of code, when nobody asked the hard questions.
That is the part I am built for. I came up as a business analyst and project manager before I was a developer, and I have scoped, architected and shipped two AI SaaS products solo: an assessment tool live in Australian schools, and a consumer research platform. I have made these decisions for real, not in theory.
In this sprint I work out what your product actually needs to be: the core problem, the AI architecture, the stack, the data and privacy design, and a realistic build estimate. On the higher tiers I build a working prototype of the hardest part so you can see it run before you commit to the full build.
You walk away knowing exactly what you are building, what it costs, and whether it is worth it. That clarity is the cheapest money you will spend on the whole project.
That is the part I am built for. I came up as a business analyst and project manager before I was a developer, and I have scoped, architected and shipped two AI SaaS products solo: an assessment tool live in Australian schools, and a consumer research platform. I have made these decisions for real, not in theory.
In this sprint I work out what your product actually needs to be: the core problem, the AI architecture, the stack, the data and privacy design, and a realistic build estimate. On the higher tiers I build a working prototype of the hardest part so you can see it run before you commit to the full build.
You walk away knowing exactly what you are building, what it costs, and whether it is worth it. That clarity is the cheapest money you will spend on the whole project.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Content Creation, AI Mobile App Development, AI-Generated Code, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Hugging Face, PyTorch, Replit, StreamlitAI Models
ChatGPT, GPT-4, LLaMA, OpenAI Codex, WhisperWhat's included
| Service Tiers |
Starter
$1,500
|
Standard
$3,000
|
Advanced
$5,000
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | |||
Detailed Code Comments | - | ||
Image Upscaling | - | - | - |
MLOps | - | - | |
Model Deployment | - | - | |
Model Documentation | |||
Model Monitoring | - | - | - |
Model Testing & Optimization | - | ||
Model Tuning | - | - | - |
Natural Language Processing | |||
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | - | - | - |
Source Code | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$400 - $900
Extra Feature Prototype
(+ 5 Days)
+$1,000
Investor-ready architecture deck
(+ 3 Days)
+$500
Full written build specification
(+ 4 Days)
+$750Frequently asked questions
About Samuel
AI Developer & SaaS Builder | Python, Next.js, APIs | Shipped Solo
Point Cook, Australia - 11:38 am local time
Australian schools. Speccie is an AI research app with an autonomous trading
engine running unattended. I do the same for clients: scope it, build it,
prove it works.
WHAT I BUILD
AI features that ship. Retrieval pipelines, structured output, validation
gates, evaluation harnesses. On Kindred, every statement in a generated plan
traces back to a cited source, and uncited claims are stripped before a human
sees them. That is the difference between an AI demo and something a
regulated buyer will actually put into service.
SaaS MVPs end to end. Auth, tenancy, quotas, admin surface, deployment.
Kindred and Speccie are both multi user products I scoped, built and now run.
Internal tools and automation. Manual processes that eat hours a week, turned
into something that takes minutes. Document pipelines, data extraction,
workflow automation.
Integrations and data migration. REST, webhooks, WebSockets, broker APIs,
third party feeds, and the ugly undocumented legacy kind. In an employed role
I reverse engineered a 6,500 property database with no documentation on
either side and reconciled trust money to the cent.
AI, IN SPECIFICS
"AI" covers a lot of ground, so: retrieval augmented generation and vector
search, structured output with schema validation, agent workflows, prompt
caching to hold cost down, model gating and routing, fine tuning a small
model where a frontier model is overkill, and evaluation harnesses that
replay real cases against candidate models. Anthropic and OpenAI APIs,
LangGraph, CrewAI, AutoGen, MCP, and local inference where data cannot leave
the building. IBM certified in RAG and agentic AI.
One example of the judgement involved. On Speccie's trade decision gate I
replaced a frontier model with a small classifier fine tuned on the engine's
own history. Cheaper, deterministic, and it improves every session. Use the
big model to learn the shape of the problem, then replace it with something
small and owned once you know the shape.
WORKING WITH REGULATED OR PERSONAL DATA
If your product touches health, financial or children's data, the code is
only half the job. For Kindred I produced the governance suite that adoption
actually requires: a data protection impact assessment a privacy officer can
complete, a responsible AI framework, a mapping against the OWASP machine
learning and LLM risk lists, and an incident plan aligned to the Notifiable
Data Breaches scheme. Australian data residency and sovereign inference where
the data cannot leave the country.
HOW I WORK
Scope before build. I work out what you actually need and find the one
constraint that should drive the design, before a line of code is written. If
I think you are buying the wrong thing, I will tell you before you pay for it.
Proven, not just done. Done means measured, not "it looks right". On a recent
client build I traced every requirement from the brief, to the code that
implements it, to the evidence it works, and handed the matrix over with it.
We agree what right looks like first, in writing and in plain English. Fixed
price work runs in milestones with a definition of done on each, so you can
see progress and stop at any point. I send updates you can check yourself
rather than status reports you have to take on trust. Everything comes with
documentation written for whoever inherits it, technical or not.
Ten years in enterprise delivery first. Business analysis, project management
and data migration on regulated systems, before I moved into building. I can
read a business problem, work with non technical people, and document what I
hand over.
RECENT WORK
Kindred. AI platform live in Australian schools. Privacy first architecture,
no identifiable data reaches the model, model calls server side, citation
validation on every claim.
Speccie. Consumer AI research app plus an autonomous ASX trading engine that
has run a full daily loop unattended on a live paper account for a year.
Real time broadcast overlay for a live aviation stream. Multi device control,
under a second from operator to air, zero application bugs in the pre handoff
QA pass. Delivered, accepted and retained for further work.
Compliance intelligence engine for the construction industry. Ingests
regulatory assessment reports and extracts structured findings against a
completeness standard.
WHEN I AM NOT THE RIGHT FIT
I am not the cheapest option and I will not pretend to be. If the work is a
well specified ticket queue and you already know exactly what you want built,
you can get that done for less elsewhere. I earn my rate on the builds where
the hard part is working out what should be built, or where being wrong is
expensive.
Stack: Python, TypeScript, JavaScript, SQL, FastAPI, Node, React, Next.js,
Anthropic and OpenAI APIs, RAG, Firebase, Oracle Cloud, Vercel.
Fixed price on defined scope, hourly on open ended work. Australia based, I work across US and European hours
Steps for completing your project
After purchasing the project, send requirements so Samuel can start the project.
Delivery time starts when Samuel receives requirements from you.
Samuel works on your project following the steps below.
Revisions may occur after the delivery date.
You tell me about your product
A working session where you walk me through your idea: what you want, who it is for, what it should do, and what success looks like.
I present my plan and we talk it through
I come back with how I would build it and why, laid out plainly, with the risks, trade-offs and open questions on the table upfront. We agree on the approach together before any real work starts.