Embedded AI Teams & Forward-Deployed AI Engineers

An embedded AI team is a group of AI engineers and specialists who integrate directly into your organization — working inside your systems, attending your meetings, learning your domain — to build AI capabilities alongside your existing team. Unlike traditional consulting engagements where work happens offsite and gets delivered at the end, embedded teams operate as functional members of your organization.

Human Agency deploys forward-deployed AI engineers who build, ship, and transfer knowledge in real time.

Why Embedded Beats Outsourced

The traditional enterprise AI engagement looks like this: hire a consulting firm, brief them on your needs, wait 3-6 months, receive a deliverable. It works for strategy decks. It fails for AI.

AI that works requires deep context. An engineer who understands your business — your data, your workflows, your customers, your organizational culture — builds better AI than one who understands AI theory better but knows nothing about your world.

Context matters more than code. Here's why:

• Domain knowledge shapes every decision.
  What data to use, how to validate outputs, what edge cases matter, what "good" looks like — these are all domain questions, not technical ones. An embedded engineer learns these answers by being there, not by reading a requirements document.
• Trust enables speed.
  When your team knows and trusts the AI engineers working with them, decisions happen faster. There's no back-and-forth through a project manager. No waiting for the next status meeting. Problems surface and get solved in real time.
• Knowledge transfers naturally.
  An embedded engineer who pairs with your people daily transfers skills without formal training sessions. Your team learns by doing, not by watching a presentation from someone who leaves next week.

How Human Agency's Embedded Model Works

Phase 1: Onboarding (Weeks 1-2)

Our engineers immerse in your organization before writing code:

• Shadow key team members to understand workflows and pain points
• Audit existing technology, data assets, and infrastructure
• Map the organizational landscape — who makes decisions, who has domain expertise, who will be the primary users
• Align on goals, success metrics, and initial priorities
• Establish working rhythms — standups, reviews, communication channels

This isn't a box-checking exercise. It's the foundation that determines whether the next 6 months produce something useful or something technically impressive that nobody uses.

Phase 2: Integration (Weeks 2-4)

Engineers become operational members of your team:

• Join existing meetings and communication channels
• Begin building on first-priority use cases identified during onboarding
• Pair with internal team members on implementation
• Establish governance guardrails for what they're building
• Ship first working prototypes for feedback

Phase 3: Building (Months 2-6+)

This is the core engagement — building AI capabilities in rapid iterations:

• Ship working features every 1-2 weeks, not quarterly deliverables
• Continuous feedback from actual users shapes what gets built next
• Engineers solve problems in context, with full access to the systems and people they need
• Custom AI assistants and tools get built with institutional knowledge baked in from day one
• Internal team members build skills through daily collaboration

Phase 4: Knowledge Transfer (Ongoing + Final)

Knowledge transfer isn't a phase at the end. It happens throughout:

• Your team members pair with embedded engineers from day one
• Documentation is written as systems are built, not after
• Internal team members gradually take ownership of systems
• Final transition includes hands-on training, architecture reviews, and a clear operating playbook
• Post-engagement support period ensures your team is fully independent

Who Benefits from Embedded Teams

Organizations that need AI capability now.
Hiring an AI team takes 6-12 months. Building internal capability from scratch takes even longer. Embedded teams give you productive AI engineers in weeks, not quarters.

Companies mid-transformation.
Mergers, restructuring, new strategic initiatives — periods of organizational change are exactly when AI can create the most value, and exactly when internal teams are least able to take on new projects.‍

Organizations with deep domain expertise but no AI expertise.
A construction firm, a humanitarian organization, a biotech company — they have institutional knowledge that's incredibly valuable for AI. They just don't have the engineers to build with it. Embedded teams bring the technical skill; your people bring the domain knowledge.‍

Enterprises at Stage 2-3 of AI readiness.
You've experimented with AI and seen potential. Now you need dedicated engineering capacity to move from experiments to production systems — without the 12-month hiring cycle.‍

Teams that want to build internal AI capability.
The best embedded engagements end with your team able to continue without us. Every engagement includes knowledge transfer as a core objective, not an afterthought.

What Embedded Engineers Build

The work varies by organization, but common projects include:

• Custom AI assistants that work with your proprietary data and workflows
• AI-augmented workflows that automate repetitive tasks and surface insights
• Internal knowledge systems that capture institutional expertise and make it accessible
• AI agents that handle specific operational tasks autonomously within governed boundaries
• Data pipelines that prepare organizational data for AI use
• Evaluation and monitoring systems that ensure AI quality over time

Everything is built on the platforms that fit your environment — we work across OpenAI, Anthropic, Microsoft, Google, and AWS, choosing the right tool for each use case rather than forcing a single vendor.

Team Composition

Embedded teams are sized and composed based on your needs:

• Solo engineer — For focused projects with clear scope. Best for single-use-case builds or technical augmentation of an existing team.
• Small team (2-3 engineers) — For multi-workstream engagements. Typically includes a lead engineer and specialists matched to your technical stack.
• Full squad (4-6 people) — For organization-wide AI programs. Includes engineering, product management, and enablement roles. Suited for Stage 2-3 organizations building comprehensive AI capability.

Frequently Asked Questions

How long do embedded teams typically stay?

Most engagements run 3-12 months, depending on scope. A focused project with clear boundaries might be 3-4 months. An organization-wide AI program with multiple workstreams and knowledge transfer goals is typically 6-12 months. The engagement length is driven by goals, not by contract structure — when the work is done and your team is ready to continue independently, the engagement ends.

Do embedded engineers transfer knowledge to our team?

Yes — it's a core objective of every engagement, not an optional add-on. Knowledge transfer happens through daily pairing, collaborative code reviews, documentation written as systems are built, and hands-on training during the transition phase. By the end of an engagement, your team should be able to operate, maintain, and extend everything that was built. We measure success partly by how well your team performs after we leave.

Do embedded engineers transfer knowledge to our team?

The fundamental difference is where the work happens and how context flows. Outsourced teams work offsite, receive requirements through documents and meetings, and deliver finished products. Embedded teams work inside your organization, learn your domain through daily immersion, and build alongside your people. The result: embedded teams produce AI that fits your organization because they understand your organization. Outsourced teams often produce technically sound AI that doesn't quite fit because they never had the full picture.

Can we start with one engineer and scale up?

Absolutely. Starting with a single embedded engineer is the lowest-risk way to test the model. They onboard, learn your domain, and start delivering value. If the engagement is working and the scope expands, we add engineers who can ramp quickly because they have a teammate already embedded who knows the context. Many of our engagements start as solo embeds and grow into small teams.