You will get Usable GenAI Workflow to Replace Manual Processes
Project details
Production GenAI Workflow Sprint is a fixed-scope, execution-focused service to turn messy manual processes into usable, production-ready AI systems in 7–14 days.
I design and implement end-to-end GenAI workflows that actually get used by real teams — not demos, experiments, or isolated prompts. The focus is on operational reliability, clear logic, and seamless integration with your existing tools.
Each sprint replaces one manual or chaotic process with a structured system that covers intake, reasoning, output delivery, logging, and basic QA/guardrails. The result is faster turnaround, consistent outputs, and less manual coordination.
This service is ideal for founders and operators who want practical AI adoption without ML training, heavy infrastructure, or long consulting cycles. You get a working system, clear documentation, and a clean handoff so your team can own it confidently.
Built for real operations — not demos or experiments.
I design and implement end-to-end GenAI workflows that actually get used by real teams — not demos, experiments, or isolated prompts. The focus is on operational reliability, clear logic, and seamless integration with your existing tools.
Each sprint replaces one manual or chaotic process with a structured system that covers intake, reasoning, output delivery, logging, and basic QA/guardrails. The result is faster turnaround, consistent outputs, and less manual coordination.
This service is ideal for founders and operators who want practical AI adoption without ML training, heavy infrastructure, or long consulting cycles. You get a working system, clear documentation, and a clean handoff so your team can own it confidently.
Built for real operations — not demos or experiments.
AI Algorithms
Feedforward Neural Network, Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Content Creation, AI-Generated Code, AIOps, Conversational AI, Natural Language Generation, Natural Language Understanding, Sequence Modeling, Text RecognitionAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Gradio, Hugging Face, Microsoft 365 Copilot, Replit, StreamlitAI Models
ChatGPT, GPT-3, GPT-4, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$2,500
|
Standard
$4,500
|
Advanced
$7,500
|
|---|---|---|---|
| Delivery Time | 5 days | 14 days | 21 days |
Number of Revisions | 1 | 1 | 3 |
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.
Post-delivery optimization
(+ 10 Days)
+$1,500
Workflow adoption & handoff support
(+ 10 Days)
+$1,000Frequently asked questions
About Maca
Applied AI Operator building GenAI workflows teams actually use
Santiago, Chile - 5:30 pm local time
I’m not an ML engineer and I don’t do research or experiments.
I work as an Applied AI Automation & Workflow Builder, focused on execution, adoption, and real operational results.
What I do
- Design end-to-end GenAI workflows (intake → reasoning → output → logging)
- Orchestrate tools like Make, Zapier, Google Workspace, APIs, Wix Velo
- Implement QA, guardrails, and templates so workflows don’t break or get abandoned
- Translate business needs into working AI systems for non-technical stakeholders
Typical problems I help solve:
- Too much manual coordination and back-and-forth
- AI ideas that never make it into daily operations
- Processes that are slow, error-prone, or depend on one person
- Teams overwhelmed by tools but lacking usable systems
Proof of work:
1/ ADA — AI-Assisted Workflow System
Designed and implemented a full production GenAI workflow:
onboarding → structured intake → AI reasoning → QA → WhatsApp delivery → logging & metrics
Stack: Wix Velo, Make, Google Workspace, LLMs
Impact: faster turnaround time, reduced manual coordination, real user adoption
I work best with early-stage to mid-stage teams that need things built fast, clean, and usable — without heavy infrastructure or ML pipelines.
If you’re looking for AI that actually runs inside your operation, we’ll work well together.
Steps for completing your project
After purchasing the project, send requirements so Maca can start the project.
Delivery time starts when Maca receives requirements from you.
Maca works on your project following the steps below.
Revisions may occur after the delivery date.
Intake & Scoping
Review your requirements, confirm the workflow scope, success criteria, and tools involved. Align on timeline and communication.
Workflow Design
Design the end-to-end GenAI workflow, including intake structure, routing logic, prompt strategy, outputs, and logging.