LATAM · USA Production-grade

Intelligence layer for enterprises, innovators, and governments.

Socradata is the intelligence layer between operational data and accountable decisions. AI and data practice in three pillars — Enterprise Transformation for the corporate operating core, Applied Innovation for the frontier, Smart Cities for the public good — bound by one discipline: production-grade outcomes, KPIs before APIs, governance before deployment, from pilot to policy.

Start a 14-day diagnostic See the three pillars
62%
Of enterprise AI pilots never reach production. We measure to that gap, not around it.
−38%
Median forecast error reduction across deployed inventory models, audited at the 90-day mark.
11
Active engagements across retail, energy, logistics, and the public sector.
100%
Engagements shipped with model card, evaluation suite, and documented rollback path.
01 — Three pillars

One firm. Three audiences. A single operating discipline.

Socradata practices in three pillars, each anchored in a strategic question and a distinct buyer. The method is shared; the time horizon, the procurement logic, and the standard of accountability differ. Self-identify below and read the pillar that fits your mandate.

Pillar 01 Enterprise transformation

Operational AI for enterprise systems.

The strategic question: how does a private enterprise convert decades of ERP, WMS, and SCM investment into AI-driven operational decisions — without pilot theater, without rip-and-replace, and without governance debt?

Serves CIOs, COOs, CFOs, and VPs of supply chain inside mature enterprises with an installed base of Infor, SAP, Oracle, or Microsoft Dynamics — operators accountable for inventory turns, OTIF, and working capital, not for AI experimentation.

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Pillar 02 Applied innovation

Where new technologies are tested, broken, and made operational.

The strategic question: which emerging technologies are crossing from research curiosity to business value — and how should we run a exploration without falling into proof-of-concept theater?

Serves Chief Innovation Officers, Chief Digital Officers, heads of R&D, corporate-venture leads, and scale-up CTOs — leaders accountable for optionality and learning, with a mandate to engage new technology on graduate-or-kill terms.


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Pillar 03 Smart cities

Digital transformation for cities, public services, and the academy.

The strategic question: how does the public sector and academia adopt AI, blockchain, and digital transformations in a way that brings a faster, decentralized service to accountable to citizens — rather than vendor-led modernization without a governance backbone?

Serves city CIOs, secretaries of innovation, multilateral program officers at the IDB, World Bank, CAF, OECD and ECLAC, and academic deans.

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So what: the buyer self-identifies in the question, not in the technology. Three pillars, three procurement logics, one shared discipline of measurement and governance.

02 — The stack

One stack. Three audiences. Different intensities by design.

Four capabilities flow across all three pillars and are applied with different weight in each. Enterprise Transformation runs them at production intensity. Applied Innovation runs them as graduate-or-kill experiments. Smart Cities runs them under stricter institutional governance. The stack is the firm's architectural signature — and the reason a finding from one pillar is legible to the other two.

Capability Enterprise transformation Applied innovation Smart cities
AI / ML / LLMs / Agents Forecasting, anomaly detection, decision support, agentic workflows under human-in-the-loop checkpoints. Dominant Experimental edge Governed
Blockchain & DLT Tokenization, programmable contracts, decentralized identity, on-chain provenance and registries. Selective First-class Structural
Digital transformation Operating-model redesign, decision rights, governance forums, change management. Central Product-shaped Institutional
Data & analytics Instrumentation, decision intelligence, KPI baselining, public dashboards, lineage and observability. Foundational Foundational Foundational

So what: data and analytics are foundational in every pillar — the rest of the stack is calibrated to the buyer's standard of proof. KPIs before APIs, in three different procurement languages.

03 — What unifies us

Three disciplines that hold across the three pillars.

A shared method is what allows a private-sector inventory model, an agentic systems sprint, and a municipal operating-model redesign to be delivered by the same firm without a loss of standard. Three disciplines do that work — and they are the reason every engagement ships with a model card, an evaluation suite, and a documented rollback path.

Discipline 01 Production-grade by default

The artifact ships when the rollback path ships.

Every engagement is built to live in someone's workflow under a service-level objective, not to perform on a stage. Model cards, evaluation harnesses, drift monitors, and tested rollback procedures are scoped from day one and audited at the ninety-day mark. The unit of work is an operator decision, dated and measured — whether the operator is a planner, a procurement officer, an innovation lead, or a city director.

From pilot to policy
Discipline 02 Governance before deployment

Policy is written before models ship, not after.

Decision rights, evaluation criteria, escalation paths, and the human-in-the-loop checkpoints are co-authored with the client's risk and audit teams in the framing stage. We design under recognized frameworks — the NIST AI Risk Management Framework, the EU AI Act high-risk obligations, public-procurement statutes where they apply — and we publish refusal lists. The standard rises with the stakes: stricter under public procurement, stricter again where decisions affect citizens.

Interoperability or it doesn't scale
Discipline 03 Scholar-practitioner method

Frameworks are stress-tested in peer review before they meet a client.

The principal holds a postdoctoral research appointment at IAE Business School in Buenos Aires and an adjunct faculty appointment at NYU. Findings from anonymized engagements feed the research; the research feeds the next engagement. Smart Cities work routes through that academic discipline by design, and the same intellectual standard is applied to private-sector mandates and innovation sprints. References include published work, not only client testimonials.

So what: the 100% figure on the navy plate above is not a marketing claim — it is the consequence of these three disciplines applied without exception, across all three pillars.

04 — Pilot to production

POC theater vs. productionization. The line that runs through every pillar.

A pilot is a stepping stone, not a destination — whether the pilot lives in an enterprise warehouse, an innovation lab, or a municipal pilot district. The clean way to tell theatre apart from production is to look at what is missing on the day the consultants leave. The contrast below is the discipline that holds across Enterprise Transformation, Applied Innovation, and Smart Cities.

POC theater

  • Demo-grade notebook, never deployed
  • KPI defined after the model is built
  • No model inventory, no model card
  • Rollback procedure missing or undocumented
  • Operator, citizen, or board never sees confidence or drivers
  • Hand-off is a deck, not a runbook

Productionization

  • Live in the operator's or institution's workflow, with SLOs
  • KPIs before APIs — measurement defined first
  • Model card, evaluation suite, drift monitoring
  • Documented rollback, tested before launch
  • Confidence and feature drivers visible by default
  • Hand-off is a runbook, owned by named people
“Most LATAM AI programs are not failing on the model — they are failing on the institutional plumbing around the model. That is the work.”
Operating principle 01
KPIs before APIs.
Operating principle 02
From pilot to policy.
Operating principle 03
Interoperability or it doesn't scale.
05 — The Operational AI Dispatch

Dispatches on operational AI, enterprise systems, and the workforce that runs them.

A practitioner blog. Cited. Footnoted. Free of vendor influence. Read by operators, CIOs, and policy teams across Latin America and United States.

Read the full blog
06 — FAQ

Questions we are asked before, during, and after every engagement.

Do you build models, or do you advise on them? +

Both, but the framing matters. We build only when the operating model around the model is in place. A model that ships into a vacuum is a liability. A model that ships into a measured workflow is an asset.

How do you integrate with the systems we already run? +

Socradata is an intelligence layer on top of the platforms you already operate, not a replacement for them. We integrate through standard APIs and event streams — enterprise systems of record, public-sector service platforms, and innovation lab toolchains — and write back only through approved interfaces. Interoperability is a precondition, not a feature.

Why a postdoc affiliation? Isn't that academic baggage? +

The opposite. The IAE postdoc is what keeps the practice honest. Frameworks are stress-tested in peer review before they meet a client. Findings from anonymized engagements feed the research, and the research feeds the next engagement.

How do you handle data residency for LATAM clients? +

Region-pinned compute by default — typically São Paulo or Santiago. We design around Argentina's PDPA, Brazil's LGPD, and the relevant municipal data ordinances. Cross-border processing requires explicit, documented client consent.

What does the smallest engagement look like? +

A 14-day diagnostic. One senior consultant, one analyst, one written deliverable: a model inventory, a capability map, and a candidate KPI set. No follow-on commitment is required.

Are you available for board-only sessions? +

Yes. Closed-door, single-day formats for boards, audit committees, and ministerial cabinets. Built around your artifacts, under NDA, and never recorded.

About Socradata

We Understand the Shop Floor and the Data Lake

Socradata was founded on the thesis that AI for enterprise operations requires both deep technical capability and genuine domain knowledge. Most AI firms have one. We built an organization that has both.

Start with the diagnosis. Then decide whether to build.

Two weeks. One written diagnosis. No deck theater. If the answer at the end of the fortnight is that you do not need us, we will say so in writing.