A Forward Deployed Engineer (FDE) is an engineer who works directly with customers to take a product from pilot to production; embedding alongside stakeholders to understand real requirements, integrate systems, and ship working deployments fast. FDEs combine strong technical depth with the ability to operate in ambiguous environments, translating customer needs into production-ready solutions and measurable outcomes

Traditional consulting often focuses on strategy, recommendations, and slideware. FDEs are execution-first: they build, integrate, and deliver production systems. An FDE is accountable for shipping results, working hands-on with your stack, your product, and your users, rather than providing advice from the sidelines.

We offer two primary SOW engagement models:

1) FDE Individuals
Best when you need an embedded operator to accelerate a specific account, deployment, or integration. Individuals integrate into your team and move quickly with minimal overhead.

2) FDE Pods
Best for larger deployments where you need a full delivery unit (e.g., integration + product + infrastructure support). Pods are pre-assembled teams that can scope, build, and launch end-to-end deployments with clear ownership and faster delivery cycles.

We support enterprise-grade deployments across the AI delivery lifecycle, including:

• Customer onboarding and product implementation

• Data integrations (warehouse, APIs, streaming, ETL)

• Model deployment (batch, real-time, edge, LLM apps)

• Workflow and systems integration (CRMs, ERPs, internal tools)

• Security, compliance, and reliability work (auth, logging, monitoring)

• Production hardening (scaling, failure handling, observability)

If it’s required to turn a pilot into a real, repeatable deployment - we can support it.

Typically, we can start within 1–2 weeks, depending on the engagement type and complexity. For urgent deployments or critical customer timelines, we can often mobilize faster within 24 hours. We aim to move quickly without sacrificing quality—early discovery, clear scope, and rapid iteration from day one.

Because Palantir deployments require a distinct set of skills: working inside complex enterprise environments, navigating data and security constraints, and delivering operational workflows, not just prototypes. We offer Palantir-specific expertise to help customers accelerate Foundry/Gotham/AIP deployments, reduce delivery risk, and move from early builds to production-grade, scalable implementations with Ex Palantir engineers & foundry experts

We support end-to-end Palantir delivery work across Foundry, Gotham, and AIP, including:

• Platform implementation and rollout support

• Data onboarding and integration (pipelines, sources, ontology design)

• Workflow development (apps, operational tooling, user flows)

• Deployment hardening (monitoring, reliability, access controls)

• Production enablement (documentation, handover, internal training)

• Scaling delivery across teams, sites, or business units

If your goal is to make Palantir usable, adopted, and production-ready - we can support it

We can plug in alongside internal teams, Palantir teams, and other delivery partners by providing extra resource. We typically operate as an execution layer: bridging product requirements, technical implementation, stakeholder coordination, and delivery timelines. The goal is to accelerate delivery without disrupting governance or existing ownership structures.

Yes. We can support ontology design, entity modeling, and the translation of domain requirements into operational workflows and apps. We focus on making the ontology practical: something that supports real use cases, scales across teams, and remains maintainable as the deployment grows.

Yes, this is one of the most common areas where deployments slow down. We support:

• integrating source systems (APIs, DBs, warehouse, logs, streaming)

• implementing ingestion pipelines and transformations

• improving data quality, reliability, and refresh cadence

• establishing repeatable onboarding patterns for future sources

The goal is to reduce time-to-value for new datasets and keep the platform stable as it scales.

Yes. We support AIP-focused work such as:

• connecting LLM workflows to enterprise data and governance

• building and operationalizing agent-like workflows

• ensuring observability, evaluation, and safety guardrails

• aligning deployment with security, access controls, and compliance requirements

We help teams move beyond demos into production-grade AIP workflows.