We embed senior engineers directly inside your organization to build, ship, and operationalize AI systems—in your environment, on your infrastructure, measured by your outcomes. No slide decks. No strategy reports. Just working software.
The models are good. The demos are impressive. The PoC looked great. And then it sat on a server for six months while your team tried to figure out how to connect it to your actual data, your actual workflows, and your actual compliance requirements.
The deployment gap between a working AI model and a production system that delivers measurable business value is the single biggest bottleneck in enterprise AI adoption—and traditional consulting firms can't bridge it.
You don't need another strategy presentation. You need engineers who can sit in your environment, understand your constraints, and ship software that works.
See what we build →of enterprise AI pilots produce zero measurable ROI — MIT Sloan research
growth in demand for Forward Deployed Engineers in 2025 — LinkedIn data
typical hiring cycle to build an internal FDE capability from scratch
ROI from AI systems that never reach production — the real cost of the deployment gap
Every engagement starts with embedding inside your organization. We don't advise from a distance—we work in your environment, with your data, inside your constraints.
We connect AI models to your data, workflows, and compliance requirements and ship production systems your team will actually use—not sandboxed demos.
A dedicated team of 2–6 FDEs becomes part of your engineering org for 3–12 months. We attend standups, ship on your sprint cadence, and own our deliverables end to end.
Design, build, and deploy autonomous AI agents for customer support, operations, back-office workflows, and domain-specific decision-making—fully integrated with your stack.
Your pilot works. Now it needs to scale to 10,000 users, comply with SOC 2, connect to your CRM, and not break when someone uploads a malformed CSV. We do that.
A structured 2–4 week sprint to map your environment, identify the highest-value AI use cases, and produce a build-ready technical roadmap with defined success criteria.
We build internal capability, not dependency. Every engagement includes pairing, documentation, and a structured handoff so your team owns and can extend what we build.
Every engagement follows the same pattern: embed, understand, build, transfer. The details change. The discipline doesn't.
We immerse in your environment—your stack, your data, your workflows, your team dynamics. We run a technical audit, map stakeholders, and define success metrics before writing a line of code.
Based on discovery, we design the system: integration patterns, model selection, data pipelines, compliance controls, and the minimal viable scope to reach production fastest.
Embedded sprints. Bi-weekly releases. Real code in your environment. We work with your engineering team throughout—not around them. Every two weeks, something new is in users' hands.
Knowledge transfer is a first-class deliverable, not an afterthought. Daily pairing, collaborative documentation, and a structured handoff playbook. You own what we build.
Fixed outcome, fixed timeline. Ideal for companies validating FDE value or shipping a specific AI system with clear success criteria. $150K–$500K depending on scope.
A dedicated team embedded in your org for 3–12 months, operating like an internal AI engineering function. Best for enterprises with multiple concurrent AI programs. From $50K/month.
One or two senior FDEs available monthly for iteration, new use cases, and ongoing optimization. Suits companies post-initial build who need continued depth without full-time headcount. From $20K/month.
We measure success in production metrics, not document counts. Here's what we've shipped.
A regional insurer had a working AI model and a compliance team that wouldn't let it touch production data. We embedded for 10 weeks, built the governance layer, integrated with their legacy claims system, and shipped a fully compliant AI review agent now handling 78% of tier-1 claims automatically.
A national retailer had a ChatGPT prototype that could answer product questions in a sandbox. Their production environment had 11 different data systems, a GDPR-sensitive customer database, and a CX team that needed the agent to match their brand voice exactly. We shipped it. In 12 weeks.
A mid-market lender wanted AI-assisted credit decisioning but had an IT procurement process designed for waterfall projects. We embedded with their risk and engineering teams, mapped the regulatory requirements, and delivered a production system that cleared their internal governance in 14 weeks—the fastest any new system had moved through their approval chain.
A healthcare network was manually processing payroll for 8,000 clinical staff across 14 legacy HR systems. We built an AI-powered integration layer, automated exception handling, and documented every decision for audit purposes. The implementation that their IT team estimated at 18 months took 8 weeks.
We're often compared to things we're not. Here's what actually separates forward deployed engineering from the alternatives.
| Embed Engineering | Big Four Consulting | Staff Augmentation | In-House Hiring | |
|---|---|---|---|---|
| Ships production code | ✓ Always | ✗ Rarely | ✓ Sometimes | ✓ Eventually |
| Works inside your environment | ✓ Day one | ✗ Mostly remote advisory | ✓ Yes | ✓ Yes |
| Accountable for outcomes | ✓ Measured on ROI | ✗ Billable hours | ✗ Time & materials | ✓ Partly |
| Time to first result | Weeks | Months (after strategy phase) | Weeks (but no playbook) | 12–18 months to hire & onboard |
| FDE-specific playbooks | ✓ Battle-tested | ✗ Generic frameworks | ✗ None | ✗ Built from scratch |
| Builds internal capability | ✓ First-class deliverable | ✗ Often creates dependency | ✗ Leaves with the team | ✓ Yes |
Our team comes from the companies that invented the FDE model. We hire for engineering depth, customer fluency, and the ability to operate at the intersection of both.
Led enterprise deployments across financial services and healthcare at scale.
Specializes in AI infrastructure, data pipelines, and production agentic systems.
Embedded with 12+ enterprises across retail, logistics, and manufacturing.
Regulatory AI specialist. Built compliant AI systems for banking and insurance.
Embed Engineering was founded in 2024 by engineers who'd spent years at Palantir, OpenAI, Stripe, and Databricks doing exactly this work—sitting inside enterprise environments, building AI systems that actually operated in production, and watching the same patterns repeat: great models stuck in pilot because the integration work was too hard, too specific, and too unglamorous for anyone to own.
We exist because the deployment gap is real, the cost is enormous, and the solution is engineering depth deployed at the point of the problem—not advisory memos sent from a distance.
We are a small, deliberate team. We take on a limited number of engagements at any time so that every client gets senior engineers, not junior staff. We don't subcontract. We don't pitch and disappear. We build.
We measure engagement success in production deployments and business outcomes, not documents and recommendations.
We work in your environment, with your data, under your constraints—not in a sandboxed demo environment that bears no resemblance to reality.
Every engagement ends with your team owning what we built. Knowledge transfer is not optional—it's a contractual deliverable.
We are not a staffing firm. Every engineer embedded in a client is someone we'd trust to lead the engagement ourselves.
Your environment is not like anyone else's. We don't bring off-the-shelf playbooks and force-fit them to your reality. We build for you.
What we've learned embedding inside enterprises. Engineering-depth perspectives on AI in production.
95% of AI pilots produce zero ROI. The root cause is almost always integration complexity, compliance constraints, and workflow mismatch—not model quality.
A technical walkthrough of the six integration patterns we've evolved across 40+ enterprise deployments—what works, what doesn't, and why.
The origin of forward deployed engineering, why it works at the enterprise level, and what the OpenAI Deployment Company signals about where the market is going.
Autonomous AI agents look simple in demos and break in production in consistent, predictable ways. Here's what we've learned building 20+ production agent systems.
Not all FDE firms are equal. A practical guide for CTOs and VPs of Engineering on how to assess technical depth, reference-check outcomes, and structure the engagement.
The firms that treat compliance as an afterthought are the ones whose AI systems never reach production. Here's how to build governance into the architecture from day one.
We take on a small number of engagements at any time. If you're working on a problem worth solving, we'd like to hear about it.
Every conversation starts with one of our senior engineers—not a sales team. We'll tell you honestly whether we're the right fit.