Forward Deployed AI Engineers, Embedded in Your Team

Your AI Roadmap Is Bigger Than Your Team

AI demos are easy to build. Getting them into production — working with real data, real systems, and real users — is where most teams stall.

• Hiring is slow: Engineers who can both build AI and deploy it in production are scarce, expensive, and take months to hire.

• Your core team is at capacity: Every AI project competes with your product roadmap.

• Agencies hand over and leave: Project shops build a prototype, then leave you to integrate and maintain it.

A forward deployed engineer closes that gap. They work inside your team, in your codebase and tools, until the AI actually works for your users.

What Our Forward Deployed Engineers Do

AI Agents & Workflows

Build agents that call your APIs, use tools, and automate multi-step work, with guardrails and human review where it matters.

RAG & Knowledge Systems

Connect LLMs to your documents, databases, and internal knowledge, with retrieval tuned for accuracy on your data.

Integrations Into Your Stack

Wire AI into your product, CRM, support desk, data warehouse, and internal tools, using your APIs, auth, and infrastructure.

Prototype to Production

Turn proofs of concept into reliable features with logging, error handling, testing, and deployment in your environment.

Workflow Automation

Automate operations with n8n, custom pipelines, and LLM-powered processing of documents, emails, and tickets.

Fixing & Hardening AI Features

Debug, stabilize, and optimize AI features that were built quickly and now break under real usage.

How It's Different From Hiring or an Agency

How It Works

1. Scoping Call

We learn your product, stack, AI goals, and what "shipped" means for you.

2. Engineer Match

We propose engineers whose experience fits your stack and use case. You meet them before they start.

3. Onboarding

The engineer joins your Slack, repo, ticketing, and standups, and learns your systems and data.

4. Build & Ship

They work from your backlog, ship in your release cycle, and report progress weekly.

5. Scale or Hand Over

Add engineers as scope grows, or hand over to your team with documentation and walkthroughs.

Engagement Options

Dedicated FDE

One full-time engineer embedded in your team. From $6,000 per engineer per month

FDE Pod

Two or more engineers plus a technical lead, for larger builds or parallel workstreams. Custom monthly pricing

White-Label Capacity

Our engineers work under your brand for your clients. Built for system integrators and consultancies. Custom monthly pricing

Who It's For

AI Startups (Seed to Series B)

You have customers asking for AI features and not enough engineers to deploy them. Add capacity without a long hiring cycle.

SaaS Companies Adding AI

You want AI inside your existing product, built properly into your architecture, not bolted on as a demo.

System Integrators & Consultancies

You've sold AI projects and need engineers to deliver them. Our engineers work under your brand and process.

Why Codersarts

• Delivery experience: 500+ apps shipped for clients worldwide since 2018.

• AI production work: Our team builds RAG systems, AI agents, automation workflows, and document-processing products.

• Full-stack depth: AI features need frontend, backend, data, and DevOps work, and our engineers cover the whole stack.

• Rescue experience: We regularly fix and stabilize AI-built apps, so we know where AI features break in production.

• Managed support: Every engineer is backed by Codersarts technical leads for reviews, escalation, and continuity.

Working Together

• Your tools: Slack, Teams, GitHub, GitLab, Jira, Linear, or whatever your team already uses

• Your process: Standups, sprints, code reviews, and your release cycle

• Working hours: Overlap with your team's time zone agreed during scoping

• Reporting: Weekly progress updates on shipped work, blockers, and next steps

• Continuity: If an engineer needs to change, we handle the handover

Confidentiality & Ownership

• An NDA with every client before any system access

• All code and work product belongs to the client

• Access limited to what the engineer needs, and revoked when the engagement ends

• Client code and data are never reused across engagements

FAQ

What is a forward deployed engineer?

An engineer who works directly inside a customer's team and systems to build and deploy software, especially AI, so it works in the customer's real environment.

How is this different from staff augmentation?

Our FDEs specialize in getting AI into production: agents, RAG, and integrations. They are also backed by Codersarts technical leads, so you get individual capacity with team support behind it.

How fast can an engineer start?

After a scoping call, we propose matching engineers for you to meet. Start dates depend on your stack and requirements.

Can I interview the engineer first?

Yes. You meet the proposed engineer before they start.

What does it cost?

A dedicated FDE starts from $6,000 per engineer per month. Pods and white-label engagements are priced by scope.

Who owns the code?

You do. All code and work product belongs to the client.

Can you work under our brand for our clients?

Yes. Our white-label option lets system integrators and consultancies deliver AI projects using our engineers under their brand.

What happens when the work is done?

You can scale the team down or hand over to your own engineers. We provide documentation and walkthroughs to make the handover smooth.

Add an AI Engineer to Your Team

Tell us what you're building and your stack. We'll propose engineers who fit.

Book a Scoping Call