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Most AI automation projects do not fail because the model is weak. They fail because the operating system around the model is missing.
Before I let an agent touch a live workflow, I define four things:
01 / The source of truth.
02 / The decisions the model may make.
03 / The actions a deterministic gate must approve.
04 / The evidence that proves the result.
That pattern now runs across three public builds:
BUILD 01 / Organizational AI Memory with source-backed retrieval.
BUILD 02 / Monday + Zoom + RTMP meeting automation with transcription and AI scoring.
BUILD 03 / Browser-first Telegram operations with Postgres truth, Qdrant retrieval, and deterministic gates.
I have packaged the entry point as a focused AI Workflow Audit: one process, one success metric, a risk map, and a 30-day implementation roadmap.
What is the first workflow in your business that is still held together by copying, checking, and chasing?
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Designed a modern Health Biomarkers Dashboard for Vital Trend Health.
Clean and insightful UI that turns complex medical data into an easy-to-understand experience. Features clear optimal vs sub-optimal indicators, detailed trend graphs for key biomarkers (Hemoglobin, ALT, HbA1c, etc.), overall health overview, and a smart AI health assistant that provides personalized guidance and explanations.
Really nice work!
Does someone on your team lose a day or two building the sales report every week or month?
That was the starting point at Mokobara, the premium travel brand. Pulling sales across all their stores, checking returns, reconciling the numbers and emailing each manager took 1 to 2 days every cycle, with 8+ hours of that spent just reconciling.
I built them an automation on Make.com a few months ago. It has run on its own ever since, weekly and monthly:
Make pulls every sales record from BigQuery, 100K to 250K per cycle. That is too many for one request, so it reads them page by page and stitches them back together.
It calculates the numbers the team actually uses: net sales per store after returns and discounts, return rates and the change against the last period.
It builds a CSV with the full breakdown and a short email summary you can read in 60 seconds.
It reads a Google Sheet of store representatives and emails every one of them the report for their own store.
The result: the reporting problem is gone. The report went from 1 to 2 days of manual work to fully automatic, and the 8+ hours of reconciliation dropped to zero.
Two things I would do the same way again:
Calculate "net sales" in the automation, not in the warehouse. The business rule for what counts as a net sale is not what the raw data stores.
Send people their slice, not the whole report. One report for everyone gets skimmed by everyone.
The figures in the image are placeholders, the real ones stay with the client.
What report is your team still building by hand? Tell me where the data lives and I will tell you how I would automate it.
Niva - AI Automation Agency Template (Contact page)
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13+ fully designed pages, covering the entire buyer journey:
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Services — a full breakdown of each service line with outcomes and use cases
Case Studies — a CMS-powered index plus a detailed single-case-study template (problem → approach → solution → results)
Blog — a CMS-powered index and article template, ready for SEO content
Contact — a conversion-focused form paired with direct calendar booking
404, and legal pages — Privacy Policy, Terms of Service, and Cookie Policy included
📌 Ideal For
AI automation agencies and consultancies
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Custom AI agent developers
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SaaS companies offering AI-powered services to B2B clients
Framer Website DesignFramer Website DevelopmentLow-Code/No-Code DevelopmentFramerFigmaAdobe Illustrator
Professional work