Glossary

The vocabulary of engineering, without the smoke.

We define the terms that show up in our work with the same precision we bring to our code, each one explained in a single sentence first, no filler jargon.

The model

Forward Deployed Engineer (FDE): An elite engineer who embeds inside the client’s business (its context, data and constraints) and owns a problem end to end, rather than writing code at a distance.; Palantir originated the model (internally they call them “Deltas”) to solve problems no off-the-shelf product could. In 2026, OpenAI and Anthropic institutionalized it with billions in capital. AXENOR runs it as an elite boutique firm.
Staff augmentation: A model where a vendor supplies “hands” that work under your direction: you still own the problem, the architecture, the risk and the job of coordinating everyone.; It contrasts with the Forward Deployed model, where the vendor takes on the whole problem and answers for the final metric, not for billed hours.
Nearshore: A collaboration model with a team in a nearby, time-zone-compatible country, enabling real-time work versus the lag of distant offshore.; For U.S. clients, Colombia runs on U.S. Eastern Time (ET) and shares the working window, without the friction of the 10-12 hour gap of traditional offshore.
Pilot to production: The leap from a proof of concept that works in a controlled setting to a system that runs reliably, measurably and maintainably inside the real business.; It’s where most technology initiatives stall, AI included: 88% of companies use it, yet few pilots reach production or measure their impact.

Artificial Intelligence

RAG (Retrieval-Augmented Generation): A technique that connects a language model to your own sources, so it answers with data retrieved from them (with citations) instead of only its training memory.; It reduces hallucinations and enables verifiable answers over private documentation, without retraining the model.
MLOps: A set of practices to version, deploy, monitor and retrain machine-learning models in production reproducibly, what DevOps does for software, applied to models.; It’s what holds a model up over time: without MLOps, an AI pilot quietly degrades the moment the data shifts.
AI agents: AI systems that carry out multi-step tasks autonomously (deciding, calling tools and acting) rather than just answering a question.; In production they require guardrails (action limits), traceability and scoped tasks; without those, autonomy becomes risk.

Cybersecurity

Zero Trust: A security model that trusts no user, device or network by default (not even inside the perimeter) and verifies every access explicitly and continuously.; It replaces the notion of a “safe internal network” with least-privilege applied to every request.
Penetration testing (pentesting): A controlled exercise in which specialists attack your systems, with the owner’s consent, to find real vulnerabilities before an attacker does.; Unlike an automated scan, it assesses how weaknesses chain together in a realistic attack.
Hardening: The process of reducing a system’s attack surface: closing unnecessary ports and services, applying least privilege and configuring securely by default.; It’s preventive security: fewer things exposed means fewer things an attacker can exploit.
SIEM (Security Information and Event Management): A platform that centralizes and correlates logs from across the infrastructure to detect and alert on suspicious activity in near real time.; It’s the nervous system of a SOC: it turns millions of scattered events into actionable signals.
SOC (Security Operations Center): The function (team, processes and tools) that continuously monitors, detects and responds to security incidents, around the clock.; It isn’t a product you install: it’s a practice you operate, and the risk you don’t monitor is the one that ends up costing you.

Cloud & infrastructure

IaC (Infrastructure as Code): The practice of defining and provisioning infrastructure (servers, networks, cloud) through versioned code files instead of configuring it by hand.; It makes deployments repeatable and auditable: the same code produces the same environment, with no surprises.
CI/CD (Continuous Integration / Continuous Delivery): Automation of the path from code to production: every change is tested (continuous integration) and deployed (continuous delivery) frequently and reliably.; It shortens the loop between writing a change and seeing it in production, lowering the risk of each release.
FinOps: A discipline that brings financial accountability to cloud spend: giving real-time cost visibility and optimizing it without sacrificing performance.; Migrating to the cloud without FinOps tends to relocate the mess into a higher bill; with it, workloads cost what they should.
High availability (HA): Designing a system to keep running despite component failures, through redundancy and automatic failover, minimizing downtime.; It’s measured in “nines” of uptime (for example, 99.99%); when operations rest on the platform, availability is the norm, not the exception.
DRP (Disaster Recovery Plan): A documented set of procedures to restore systems and data after a major incident (an attack, a major failure, a physical disaster) within an agreed time and data-loss target.; It defines two key goals: the RTO (how long recovery takes) and the RPO (how much data can be lost).

Delivery & operations

Strangler pattern: A strategy to modernize a legacy system gradually: new pieces are built around the old one and traffic is redirected to them over time, replacing it without a single big rewrite.; It allows migration with no outage windows and none of the risk of a “big bang” cutover that halts operations.
SLA (Service Level Agreement): A measurable commitment between a provider and a client on the level of service (availability, response times) that will be met, with defined metrics and consequences.; A serious SLA is reported and met; without measurement, it’s a brochure promise.
Observability: The ability to understand a system’s internal state from its outputs (metrics, logs and traces) to diagnose problems, including ones you didn’t anticipate.; It goes beyond monitoring: it doesn’t just tell you something failed, it lets you ask why.

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