Forward-Deployed Engineering
Forward-deployed engineering is a model where engineers work directly alongside a client, embedded in their environment, building custom solutions against real problems rather than shipping generic product from a distance. Palantir popularized the term, but the approach is older than the label—it is how defense contractors, management consultancies, and bespoke software shops have always operated when the problem is too specific for off-the-shelf tools. Alephic practices forward-deployed engineering because the gap between what AI can do in a demo and what it needs to do in a specific company's workflow is where most AI projects die. That gap does not close with better documentation or more features in a SaaS dashboard. It closes when an engineer who understands the model also understands the business context, the data, and the humans who will use the system.
Related terms:
Gravity Wells
Gravity wells describe economic dynamics where scarce resources flow disproportionately to entities with the greatest ability to pay and deploy, creating self-reinforcing concentrations of power. In the AI economy, they form around critical bottlenecks in compute, power, and talent, determining who captures resources and who scrambles for scraps.
AI Strategy
AI strategy is an organization’s plan for how it will use—and what it will not use—AI to achieve business outcomes, answering concrete questions about workflow automation, human judgment, ROI measurement, infrastructure, and ownership. It means sequencing investments (for example, custom models to leverage proprietary data versus off-the-shelf solutions) rather than simply selecting vendors.
Chain-of-Thought
Chain-of-thought prompting, introduced by Google Research in 2022, transforms AI from an answer machine into a reasoning partner by explicitly modeling the problem-solving process step by step. By decomposing complex queries into sequential reasoning steps and making implicit thinking explicit, it fundamentally improves AI performance.