You will get AI Workflow Audit and Automation Plan for Your Business
Project details
Most AI automation fails because it's a demo, not a system. This audit gives you a clear, low-risk plan before you commit to a build: I map your current workflow, find where it breaks, and define exactly what is worth automating — with acceptance criteria agreed up front. You get a written automation plan, a risk map, and a first-step scope you can act on with me or anyone. Higher tiers add a prototype architecture, test cases, and handoff documentation. Stack: n8n, Python, LLM agents, RAG, and API integrations — with a human-review gate wherever a wrong result would cost you. This is a two-person studio: I own delivery and communication; the architecture is built by a senior systems lead whose work is open-source and CI-tested, so you can verify the engineering, not just trust it.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Content Creation, AI-Enhanced Classification, Conversational AI, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Hugging FaceAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$149
|
Standard
$450
|
Advanced
$900
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 2 |
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 | - | - | - |
About Dmytro
AI Automation Engineer | n8n, Python, APIs, RAG Workflows
Kharkiv, Ukraine - 3:43 am local time
We move fast, then we try to break what we built. A result that works once is not the bar; fault-tolerant and deterministic is. Every output that passes is attacked at least three times, calibrated in software, and hardened against its own failure modes — and only then automated and shipped to production. If a claim cannot be tested, it does not ship as a claim — it ships marked UNKNOWN.
Our operating discipline:
• Verification, validation, falsification — applied to every output, not printed in a tagline.
• Benchmarked, not believed — measured numbers, a reproducible run, and the exact conditions under which it fails.
• Evidence over opinion — sources ranked (open data, official documentation, peer-reviewed work) above anyone's intuition, ours included.
• Tiered inference — every conclusion labeled by the strength of the evidence behind it. No confident guessing.
• No filler, no theater — if a result is weak, we say so and fix it before it leaves our hands.
What we deliver: AI automation and LLM agents (OpenAI / Anthropic Claude) for retrieval-augmented generation, document understanding, classification, and multi-step agents gated by human review; n8n and workflow automation for lead capture, follow-up, reply tracking, and data synchronization across Google Sheets, Notion, and CRMs; Python services, API and webhook integration, and data pipelines that ingest, transform, validate, and deliver; repository audits, CI/CD on GitHub Actions, and documentation written to be read.
This is a senior two-person team with a single point of contact. I own delivery, scope, and the client relationship. My technical lead is an AI systems architect whose work is public, MIT-licensed, and CI-tested: multi-agent orchestration, falsification-first verification, reproducible benchmarks, and architectures informed by neuroscience and cognitive engineering. The rigor is not a claim — the code is open.
If automation will not outperform your current process, we say so; we do not sell builds you do not need. And we do not vanish — while your system runs, you get fast, direct answers to any question: support, not silence. We work async and text-first, so clear written updates matter more than calls.
Send us the workflow you are tired of supervising, or the system that keeps failing, and you'll get a straight assessment of what is worth automating — and what is not.
Steps for completing your project
After purchasing the project, send requirements so Dmytro can start the project.
Delivery time starts when Dmytro receives requirements from you.
Dmytro works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery
You share the workflow and tools; we agree acceptance criteria in writing before any work starts.
Audit
I map the flow, find failure modes, and decide what is safe to automate — with the risks ranked.