Santiago Forero Vega

Information systems / AI adoption / Product Design

Santiago Forero Vega

I design information
systems in the era of AI.

Four built. Three in production, daily use. 
A handful for personal projects that we can also discuss.

01

What I’ve built

Escociaos

In production · flagship

Product walkthrough · 58 sec · sound on

Information system running a 15,000-tree orchard, a 60-head dairy, and a 200-head cattle operation. Structured data, live dashboards, and an agent that answers complex questions in plain language. Built for a field team with no formal education and intermittent connectivity.

Time returned 15 hrs / week
Input savings >$10k / year
Agronomist dependency Removed
Read the case study

360xRunning

In production

Two agents for a running coach agency. One builds complex training plans and upskills junior coaches. The other pressure-tests plans against 25+ conditional rules the coaches had only ever held in their heads. Claude Cowork with a Google Sheets backend, scaled down deliberately to what two non-technical users could operate.

Complex plan build 1 hr → 15 min
Rule compliance Partial → full
Usage Weekly
Read the case study

Agentic Product Design Studio

Personal project · running

Two agent teams. The first (6 orchestrated sub-agents) scouts problems and validates market opportunities. The second (7 sub-agents) takes the brief and builds a working prototype to test with real users. Full-auto goes from opportunity space to prototype in about two days. I also built a human-in-the-loop version, because the autonomous one makes rational decisions I would not make.

Sustainable Farming Agent

Archived

Took farm data, productivity metrics, soil lab results, and available budget, and returned a prioritized plan for sustainable practices with the business case behind each one. 20 leads over 12 weeks, 5 assessments run, 1 farm acted. The output was well received, but farmers were reluctant to act because they were too ambitious (value prop learning that triggered a pivot).

02

My approach to AI

I believe four things decide whether AI adoption works.

  1. 01 Culture Without willingness to change, there is no team for the job.
  2. 02 Strategy AI is a lever, not a goal. If it does not move revenue or cost, it will die quietly.
  3. 03 Structure Data, processes, and bottlenecks as they actually are, not as documented in SOPs.
  4. 04 Operation Rollout and change management, then a self-improvement loop of the people - tool interaction.

Get enough clarity to act.

Act until the clarity runs out.

Regain clarity on the four principles.

Repeat

03

Career

Escocia Hass

OPS LEAD · '18–PRESENT

Family business. Design, plan, and supervise a 50-hectare and 260-head operation. Built Escociaos to improve management without adding headcount.

think SID

FOUNDER · '25–'26

Built an AI planner for sustainable farming. Farmers engaged but did not pay. Pivoted to advising agtech founders on product decisions and built the PMR framework to diagnose them. The founders who needed it most could not afford it, which is why the practice closed.

CONSULTANT · '23–'25

Hired as a sustainability consultant for Fortune 500 food and beverage companies. Became the internal information systems designer through work I took on beyond the role. Managed $2M USD in projects and delivered internal frameworks, tools, and data insights that shaped 2025 branch strategy.

PRODUCT DESIGNER · '22

Redesigned the UX and UI of Agritecture Designer, all shipped. Defined the product strategy roadmap.

PROGRAM MANAGER ·
'18–'21

20+ prototypes for new banking and insurance prototypes delivered to C-level, 5 launched. Led three team leads and their teams of product design interns.

MS Design Science, Michigan Engineering, 2021–2022

BBA, Universidad de los Andes, Colombia, 2012-2018

Download resume (PDF)
04

How I’ve learned to use AI

I self-taught AI, and then started teaching people who reject it entirely.

5 Hudson Valley farmers, one summer, as volunteer.
Each finished with a working agent.
8 Senior executives, who claimed to be too old to use it.
Went from using AI as Google to running scheduled workflows.
3 Friends, merely scratching their corporate ChatGPT/Claude account.
Now token-maxing while building in record time internally.

My teaching method: 10 minutes of concept, 50 of doing. Use AI to build with AI. Treat it like a smart intern and always say “confirm understanding and ask me clarifying questions”.

100% satisfaction, so far.

05

Where to find me

Happy to connect.

Back to work

Escociaos

IN PRODUCTION SINCE 2025 · 15,000-TREE ORCHARD · 60-HEAD DAIRY · 200-HEAD CATTLE · 5 USERS

Moved a working operation from control to management. The data existed, but it just wasn’t alive.

Product walkthrough · 58 sec · sound on

Before

  • Google Sheets, linked, dozens of formulas
  • WhatsApp and paper forms
  • Spreadsheet calculators assisting ~95% of tasks
  • Off-the-shelf farm software: never a fit

After

  • Responsive web app
  • Structured backend and partially automated data intake
  • Deterministic + LLM-powered engines
  • 100% tailormade and bespoke to Escocia Farms operation

Constraints

Intermittent connectivity

No formal education on the field team

Generalized aversion to technology (usage and big-brother fears)

Build

  • React / Next.js · Supabase · Claude Code · Vercel
  • Telegram for field capture, desktop for reporting
  • Structured data → live dashboards → AI agent
  • MVP in weeks. Full rollout in four months.

Results

Measured, before vs after 15 hrs/week
Spray and fertilizer calibration >$10k/yr
Agronomist dependency Removed
Weekly reports Results plus recommended next steps

What I killed

  • Full automation of data capture. Paper was never the bottleneck. A phone with both hands full was, and it drained batteries.
  • Automated expense logging. Same wall.

Both taught the same thing: user dynamics > feature capabilities.

What it still does not do

Inventory and labor do not reconcile with cashflow. I widened scope to cattle and dairy first in response to business needs.

The part I did not plan

Field team members who resisted it now arrive with feature requests and ideas.

Back to work

360xRunning

IN PRODUCTION · RUNNING COACH AGENCY FOR WOMEN · 2 AGENTS · 2 COACHES, WEEKLY USE

They told me plan building took the most time. After delivery, they told me what actually did.

Sample generated training plan · to come

The client

360xRunning coaches women runners on the premise that training plans have been designed for men by men. They blend multiple frameworks to fit the individual athlete.

Diagnosed

  • Complex plans took 1 hr+ and carried the bias of whoever built them
  • Validation ran on whatever rules a coach remembered
  • 15+ conditional rules existed only in their heads

Built

  • Two Claude Cowork projects, narrow scopes
  • Agent 1: builds complex plans, upskills junior coaches
  • Agent 2: pressure-tests plans against the full rule set
  • Deliverable: system instructions and CLAUDE.md
  • Backend: Google Sheets

Results

Complex plans 1 hr → 15 min
Rule compliance Partial → full, 15+ conditional rules
Output format Standardized template, new
Usage Both coaches, weekly

Why Google Sheets

Using Claude at all was already the change management ceiling for two non-technical users. I scaled the technical side down to match. No maintenance, and they can manipulate it themselves.

What I got wrong

  • I built for the diagnosis they gave me: plan building takes the longest.
  • After delivery they came back. Most plans are copy-paste between clients, and the new tool made those slower.
  • Rescoped to complex plans and validation only. The testing period surfaced where the value actually was.

How the rules came out

Long interviews, past plans, challenging their beliefs, AI to distill it.