Forward Deployed Engineering

We Write Production Code From Day One.....

Direct commit access to your repository, typically granted inside the first week
Embedded in your existing standups, sprint rituals, and review process, not run alongside them as a parallel track
Outcome-based engagement pricing, with success metrics defined before day one, not headcount or hours logged
Every engagement closes with a formal capability handoff, so the team runs it without us

Senior engineers embedded directly in your team, writing production code, owning workstreams, and transferring capability as they go. Not consultants filing a report from the sidelines. People who write the code alongside your engineers, and make sure your team can pick it up once we’re gone.

Two Roles, One Mission

Complete Execution Coverage

The Discovery Engineer

Maps the gap between what was asked for and how people actually use the product, keeps stakeholders and executives aligned, and feeds the delivery engineers the discovery insight that keeps the build pointed at the right problem.

Typically delivers: stakeholder interview synthesis, an AI opportunity scorecard, a phased roadmap, and the executive-facing narrative that keeps sponsors bought in for the length of the engagement.

The Delivery Engineer

A fast prototyper and production builder. Ships working, production-worthy code in days instead of weeks, pairs with your engineers on the hardest parts of the problem, and writes the ADRs, tests, and runbooks as the work happens, not after.

Typically delivers: production commits to your repository, test coverage for the workstream owned, architecture decision records, and hands-on pairing sessions with your engineers.

Enterprise AI deployments usually run into trouble because of organizational dynamics, not technical limits. Invovia’s FDEs help clients get the right executive sponsorship, work alongside existing IT and security teams instead of around them, and price engagements on outcomes rather than headcount or hours logged. Success metrics are defined upfront, so there’s no scope creep and no billable-hours theater, for example: reduce ticket resolution time from 3 days to 4 hours.

Days to first production commit from an embedded FDE

Typical velocity lift in FDE-owned workstreams

Months in a typical Embedded Partnership

Engagements close with a formal capability handoff

Deployment Modes

We Deploy to Match the Problem

In every mode, FDEs blend into how your team already works, your rituals, your tools, your review process, rather than importing an outside process on top of it.

Crisis & Acceleration: 4 to 12 weeks. For deadlines at risk, live incidents, or architectural blockers. We're typically on-site or online within days, triaging the immediate risk before we touch anything else.
Sustained Embedded Partnerships: 3–12 months. Multi-quarter transformation programs run against a quarterly roadmap, with capability transfer built into every sprint so the gains stay after we leave.
Greenfield AI System Builds: New AI-native products, built alongside your team from zero: architecture, data pipeline, model integration, and the first production release, with your engineers in the room for every decision.

Core FDE Offerings

What FDEs Typically Own Once They're Embedded

These are the recurring workstreams our FDEs pick up inside client teams, whatever the deployment mode. Most engagements combine two or three of these rather than a single one in isolation.

AI / LLM Integration

Wiring production LLM calls, tool use, and retrieval into existing systems: RAG pipelines tuned for accuracy, agent orchestration that survives real traffic, and evaluation harnesses that catch regressions before users do.

SDLC Acceleration

Wiring Copilot, Cursor, and AI-assisted review into the existing dev cycle, plus the testing and CI/CD changes needed to ship faster without lowering the bar on quality.

Legacy & Architecture Modernization

Untangling monoliths, retiring tech debt, and re-platforming systems piece by piece, using AI-assisted code comprehension to move faster through code nobody fully remembers writing.

Safe Agentic Architecture

Multi-agent systems with human-in-the-loop checkpoints, guardrails, and audit trails built in from the design phase, not retrofitted after an incident.

AI-AMS & Platform Engineering

Observability, predictive alerting, and automated remediation that turn application management from reactive ticket triage into a largely self-healing operation.

Embedded Capability Transfer

Structured pairing, ADR libraries, and runbooks written continuously through the engagement, so the skills stay on your team’s roster, not just in our engineers’ heads.

How We Deploy: The Engagement Model

Five phases. No wasted motion.

Days 1–5

Rapid Context Immersion

Codebase and architecture review, stakeholder interviews, and we're usually contributing code by day three.

Days 5–14

Problem Prioritization & First Demos

Rough, working demos surface real requirements that no workshop ever could.

Ongoing

Workstream Ownership & Delivery

We take direct ownership of defined workstreams. Code ships to your repository, built to your standards.

Continuous

Embedded Knowledge Transfer

ADRs, prompt libraries, and runbooks get written continuously, not scrambled together at closeout.

Graduation

Capability Graduation

A formal review where your engineers demonstrate they can run it without us.

“Can this team operate everything we built without us, and are they measurably stronger than before we arrived?” That’s the question every FDE engagement gets measured against.

Invovia Delivery Standards