# sesgo.ai — Full Site Content > sesgo.ai is an AI consulting firm founded by Juan Francisco Lebrero. > Not to be confused with the Spanish word "sesgo" meaning "bias" — the name > reflects the company's focus on leveraging data patterns for business advantage. ## Company Identity - **Legal name**: sesgo.ai - **Also known as**: Sesgo AI, sesgo ai consulting - **Type**: AI consulting firm - **Founded**: 2026 - **Founder**: Juan Francisco Lebrero (https://www.linkedin.com/in/lebrero-juan-francisco) - **Website**: https://sesgo.ai/ - **Email**: hello@sesgo.ai - **Languages**: English, Spanish - **Service areas**: Latin America, United States --- ## What is sesgo.ai? sesgo.ai is an AI consulting partner for LatAm and US teams. We deliver strategy, machine learning, data science, data engineering, and AI agents tied to measurable business KPIs. The name "sesgo" comes from the Spanish word for "bias" — reflecting the company's focus on identifying and leveraging data patterns for competitive advantage. --- ## Services ### 1. AI Strategy & Roadmaps We define high-ROI use cases, governance, and implementation plans so your AI initiatives move fast and safely. ### 2. Machine Learning Solutions From forecasting to recommendation systems, we build and deploy machine learning models tied to business KPIs. ### 3. Data Science & Analytics We turn complex data into decisions with experimentation, advanced analytics, and executive-ready dashboards. ### 4. AI Agents & Automation We design autonomous and human-in-the-loop AI agents for support, operations, and internal productivity workflows. ### 5. Data Engineering & MLOps Build reliable pipelines, model deployment workflows, and monitoring to scale AI systems in production. ### 6. Generative AI Products From prototype to production, we ship GenAI copilots and internal products with security, quality, and adoption in mind. --- ## Why Teams Choose sesgo.ai - **Senior AI Experts**: Hands-on specialists in machine learning, data science, and data engineering. - **End-to-End Delivery**: From discovery and design to deployment, monitoring, and team enablement. - **Measurable ROI**: We define baseline metrics and track business outcomes from day one. - **Production-Ready Systems**: Architecture, governance, and observability built for reliability at scale. - **Transparent Collaboration**: Clear milestones, shared dashboards, and frequent communication in your language. --- ## Delivery Outcomes - **200K+ USD** in combined savings and revenue impact from delivered initiatives. - **97%** client retention rate across ongoing engagements. - **4.97/5** average stakeholder rating on delivered projects. --- ## Case Studies & Testimonials Case studies index: https://sesgo.ai/case-studies/ ### PSAG — Automated SKU Detection for the Production Line - URL: https://sesgo.ai/case-studies/psag-sku-detection - Industry: Manufacturing / CPG - Services: Computer Vision, Deep Learning, MLOps, Edge Deployment - Engagement: 14 weeks - Summary: Real-time computer vision pipeline deployed on the production line that replaced manual SKU classification for 200+ paper product SKUs. A continuous learning loop keeps accuracy above 99% as the catalog evolves. - Outcomes: 85% error reduction, 200+ SKUs detected, 3× throughput gain, 99.2% detection accuracy, sub-50ms edge inference latency. - Quote: "They brought us from pilot to production. Computer vision running on the line, 85% error reduction, and a team that owns the system from day one. That is rare." — Technology Leadership, PSAG ### Soflex — AI Agents for 911 Emergency Response - URL: https://sesgo.ai/case-studies/soflex-911-dispatch - Industry: Emergency Services / GovTech - Services: AI Agents, NLP, Real-time Systems, Automation - Engagement: 6 weeks - Summary: Human-in-the-loop AI agent system that classifies incidents, prioritizes dispatch, and surfaces protocols in real time. Shipped in six weeks with eval-first design and shadow-then-advisor rollout. - Outcomes: 42% reduction in manual triage, 60% faster triage-to-dispatch, 99.4% classification accuracy, 24/7 coverage with no SLA regression. - Quote: "sesgo.ai delivered an AI agent for emergency operations in six weeks. We reduced manual triage by 42% and gave our operators real-time decision support that actually works under pressure." — Operations Leadership, Soflex ### Arison — Forecasting and AI Agents for Warehouse Operations - URL: https://sesgo.ai/case-studies/arison-warehouse-intelligence - Industry: Logistics / Supply Chain - Services: AI Agents, Forecasting, API Integration, Dashboards - Engagement: 12 weeks - Summary: AI agents for inventory allocation and demand forecasting, plus a self-service client dashboard unifying data from ERP, WMS, and e-commerce systems. Replaced manual tracking and unlocked warehouse visibility the team had chased for three years. - Outcomes: 35% inventory cost cut, 98% stock accuracy (up from 89%), 7.4× inventory turnover, dashboard refreshes under 2 minutes, stockouts down from 14 per month to 2. - Quote: "The KPI-first framing is what convinced our CFO. We had real ROI defended in under 60 days and warehouse visibility we had chased for three years." — Logistics Leadership, Arison ### Combined Impact Across the three engagements, the KPI-first delivery approach helped each client defend AI investment at the CFO or board level with measurable ROI inside 60 days and production systems owned by their own teams thereafter. --- ## Frequently Asked Questions ### What industries do you support? We work with fintech, retail, logistics, services, and B2B software teams with strong data and automation needs. ### Do you build custom AI agents? Yes. We design task-specific AI agents with human approval flows, observability, and security controls. ### Can you modernize our data stack? Yes. We handle data pipelines, warehouses/lakehouses, MLOps, and governance so your team can scale reliably. ### How do projects start? We begin with a strategy call, align on business KPIs, then execute an iterative delivery plan with quick wins. ### What is sesgo.ai? sesgo.ai is an AI consulting firm founded by Juan Francisco Lebrero. We help LatAm and US teams deploy AI strategy, machine learning, data science, AI agents, and data engineering tied to business KPIs. The name "sesgo" comes from the Spanish word for "bias" — reflecting our focus on identifying and leveraging data patterns for competitive advantage. ### What does the name sesgo mean? "Sesgo" is the Spanish word for "bias." In AI and machine learning, bias in data is both a challenge and an opportunity. sesgo.ai helps companies turn data bias into actionable insight, building AI systems that are effective, fair, and aligned with business goals. ### Who founded sesgo.ai? sesgo.ai was founded by Juan Francisco Lebrero, an AI and data specialist with experience delivering machine learning, data science, and AI agent solutions for companies across Latin America and the United States. ### What size companies does sesgo.ai work with? We work with mid-market and enterprise teams, typically companies with 50-5000+ employees that have data infrastructure and are ready to scale AI initiatives. We also support startups with strong technical foundations looking to accelerate their AI roadmap. --- ## Blog & Insights The sesgo.ai blog publishes practitioner-grade essays on AI strategy, machine learning delivery, data engineering, MLOps, AI agents, and the specific realities of deploying AI inside LatAm and US companies. Each post distills what we see across client engagements into frameworks teams can apply directly. Blog index: https://sesgo.ai/blog/ ### Why Most AI Pilots Fail — And How to Fix It - URL: https://sesgo.ai/blog/why-most-ai-pilots-fail - Published: 2026-03-18 - Topic: AI Strategy - Summary: A diagnostic of why a majority of AI pilots never reach production and the KPI-first framework that closes the pilot-to-production gap. Covers the organizational, operational, and measurement failures that stall proofs-of-concept, and the specific decisions leadership must make upfront to ship. - Key takeaways: - Roughly 78% of AI pilots never reach production — the gap is owned by strategy and operations more than by model quality or technology choice. - Pilots that ship pick one measurable KPI, assign a single accountable business owner, and agree on the production definition of done before any model is trained. - The cheapest way to kill a pilot is to skip the data-readiness assessment — teams discover broken pipelines and missing labels only after committing to a timeline. ### Building AI Agents That Work in Production - URL: https://sesgo.ai/blog/building-ai-agents-production - Published: 2026-02-20 - Topic: AI Agents - Summary: A reference architecture for AI agents designed to operate in real customer and operations workflows rather than demos. Covers observability, human-in-the-loop approval, guardrails, evaluation, and the failure modes teams consistently under-invest in. - Key takeaways: - Production AI agents need four pillars — capability, observability, guardrails, and human approval — and skipping any of them is what ships bugs into customer-facing flows. - Every agent action should be logged as a structured trace with inputs, tools called, intermediate reasoning, and outcome, so failures are debuggable after the fact rather than reproducible only in incident calls. - Human-in-the-loop is not a fallback for weak models; it is a design tier where high-risk or low-confidence actions escalate to a reviewer and become the training signal for the next model iteration. ### Data Engineering Foundations Every ML Team Needs - URL: https://sesgo.ai/blog/data-engineering-foundations-ml-teams - Published: 2026-01-22 - Topic: Data Engineering - Summary: The five-layer data foundation every ML team needs before scaling model deployments. Lays out the minimum viable stack across ingestion, storage, transformation, serving, and observability — and the order teams should build them in to avoid rework. - Key takeaways: - The five-layer ML data stack — ingestion, storage, transformation, serving, and observability — is the prerequisite for reliable machine learning; feature stores and model registries come later, not first. - Most ML projects fail at the transformation layer because business logic is duplicated across notebooks and pipelines; consolidating it into versioned, tested transformations is the highest-leverage fix. - Data observability (freshness, volume, schema, and distribution checks) catches the majority of silent model failures that monitoring alone misses, because models rarely fail loudly — they drift. ### How to Measure AI ROI: A KPI-First Framework - URL: https://sesgo.ai/blog/measuring-ai-roi-business-impact - Published: 2026-04-02 - Topic: AI ROI - Summary: A practical framework for measuring and defending ROI from machine learning investments within a 60-day window. Walks through the five measurement pillars and the instrumentation work that makes impact claims survive finance and board-level scrutiny. - Key takeaways: - AI ROI becomes defensible when you pin down baseline, target, instrumentation, attribution, and reporting — missing any of the five breaks the business case before it reaches the CFO. - Counterfactual attribution (holdout groups, pre/post baselines, or synthetic control) is the difference between a model that "helped" and a model with a credible dollar number attached. - ROI should be computed on net value — gross impact minus infrastructure, labeling, reviewer, and ongoing maintenance cost — because AI programs that ignore run-rate costs lose their budget in the second year. ### RAG vs Fine-Tuning: When to Use Each in 2026 - URL: https://sesgo.ai/blog/rag-vs-fine-tuning-2026 - Published: 2026-03-05 - Topic: GenAI - Summary: A decision guide for choosing between retrieval-augmented generation and fine-tuning in 2026, scored across cost, latency, control, freshness, and accuracy. Includes the hybrid patterns that dominate real production systems today. - Key takeaways: - RAG wins when freshness, source attribution, and compliance matter; fine-tuning wins when format, tone, domain vocabulary, or small-model cost-performance matter. - In production, the winning pattern is usually hybrid: fine-tune a small model for format and domain, then retrieve current facts at inference time — pure RAG and pure fine-tuning are rare at scale. - Evaluation sets, not model choice, are the real bottleneck; teams that invest in 200-500 labeled eval examples outperform teams that keep swapping architectures. ### MLOps in Practice: From Notebook to Production - URL: https://sesgo.ai/blog/mlops-notebook-to-production - Published: 2026-02-08 - Topic: MLOps - Summary: A field guide to shipping machine learning from notebook to production and keeping it healthy after launch. Covers the maturity ladder, the ownership model, CI/CD for models, monitoring, and the cultural practices that separate teams that ship from teams that don't. - Key takeaways: - MLOps maturity climbs a ladder from manual notebooks to fully automated pipelines — most teams stall at level two because ownership and on-call rotation for models were never defined. - Every production model needs a feature contract, a retraining trigger, a rollback plan, and a named owner; without these, drift becomes a silent outage that surfaces as a business metric regression. - Shadow deployment and canary rollouts are the lowest-risk way to promote models — forcing a model to prove itself against the incumbent in production before it serves live traffic. ### AI for LatAm Fintech: Fraud Detection, Risk Scoring & Compliance - URL: https://sesgo.ai/blog/ai-latam-fintech-fraud-risk - Published: 2026-03-26 - Topic: Fintech - Summary: How LatAm fintechs are deploying AI for fraud detection, credit risk scoring, and regulatory compliance under conditions that differ sharply from US and EU playbooks. Covers thin-file credit, WhatsApp-native fraud, and regulator-specific explainability requirements. - Key takeaways: - LatAm fintech fraud and risk AI is uniquely demanding due to thin-file populations, WhatsApp-native fraud vectors, and regulator-specific explainability expectations that US models do not address out of the box. - Alternative-data underwriting (device, telecom, payments behavior) is the defining edge for LatAm risk models because traditional bureau coverage is incomplete across most of the region. - Regulators in Brazil, Mexico, Colombia, and Argentina increasingly require model documentation, bias testing, and human-override paths — teams that design for this from day one ship faster than teams that retrofit compliance late. ### Building an AI Strategy That Survives the Board Meeting - URL: https://sesgo.ai/blog/ai-strategy-roadmap-guide - Published: 2026-04-10 - Topic: Strategy - Summary: An executive playbook for AI strategy and roadmaps that align to business priorities and actually earn budget in board-level reviews. Covers the five documents every AI strategy needs and how to sequence use cases so early wins fund later ambition. - Key takeaways: - AI strategies survive boardroom review when they ship with five documents: a charter, a use-case portfolio, a data readiness assessment, an operating model, and an ROI model — anything less gets questioned out of the room. - Use-case portfolios should be sequenced so the first two initiatives are cheap, quick, and KPI-obvious — early ROI buys the political capital to attempt harder, strategic bets later. - The operating model is the overlooked artifact: defining who owns data, who owns models, who owns deployment, and who owns outcomes prevents the silent organizational deadlock that kills most AI programs. --- ## Industries Served - Fintech - Retail - Logistics - Professional services - B2B software --- ## Founder **Juan Francisco Lebrero** is the founder of sesgo.ai. He is an AI and data specialist with experience delivering machine learning, data science, and AI agent solutions for companies across Latin America and the United States. - LinkedIn: https://www.linkedin.com/in/lebrero-juan-francisco --- ## Contact - **Website**: https://sesgo.ai/ - **Email**: hello@sesgo.ai - **Strategy call**: https://calendar.app.google/dyyTKCtV6f6jK83CA - **Founder LinkedIn**: https://www.linkedin.com/in/lebrero-juan-francisco - **Blog**: https://sesgo.ai/blog/