You're ready to scale your engineering team—but what’s the smartest way to do it?

Should you augment with a few contractors? Bring in an outsourced squad? Hire full-time? Or… something else?

Many AI startups wrestle with the choice between augmented teams and embedded teams. On the surface, both promise speed and flexibility—but they work very differently.

What’s the Difference?

Let’s define the two models clearly:

Augmented Teams

• Short-term, plug-in help (usually individual contractors)

• Task or ticket-based

• Operate adjacent to your core team

• You manage delivery, performance, and handoffs

Think: “Extra hands on deck,” not long-term product partners.

Embedded Teams

• Engineers are integrated into your workflow (Slack, GitHub, Jira)

• Aligned with your standups, retros, sprint goals

• Long-term accountability

• Delivery is shared—not just assigned

Think: “Feels like a full-time team member,” even if nearshore or external.

Why This Matters More for AI Startups

When building AI products, execution speed and cross-functional coordination are critical. The engineering work isn’t just code—it’s:

• Orchestrating models, APIs, and edge cases

• Responding to model behavior changes

• Handling compliance, data privacy, and output testing

• Designing fallback logic for hallucinations or failure modes

These tasks require collaboration, not just completion.

Contractors can write Python—but embedded teams ship resilient AI workflows with you.

When Augmented Teams Fall Short

You might be using an augmented model if:

• You’re delegating tasks to offshore contractors or agencies

• You’re handling sprint planning and velocity management yourself

• Your contractors only work async or off-cycle

And what happens next?

• Friction in handoffs

• Fragmented architecture

• Bugs reappear because no one owns the outcomes

• You, the CTO, become a project manager

Startups don’t just need more velocity—they need stable velocity with shared context.

Why Embedded Teams Scale AI Products Better

Augmented Teams:

• Time zone alignment: Often asynchronous, leading to communication lags.

• Sprint participation: Not involved in full agile cycles.

• Product ownership: Focused on completing tasks, not delivering outcomes.

• AI ecosystem familiarity: Skill levels vary and require vetting.

• Team rituals + culture fit: Typically siloed and disconnected from company culture.

Embedded Teams:

• Time zone alignment: Work in real-time with your team.

• Sprint participation: Fully integrated into agile processes.

• Product ownership: Aligned with outcomes, not just output.

• AI ecosystem familiarity: Pre-vetted for domain-specific expertise.

• Team rituals + culture fit: Aligned with your engineering culture and proactively involved.

With embedded nearshore teams, you benefit from:

• Faster onboarding

• Higher code consistency

• Aligned engineering momentum

• Lower management overhead

What Embedded Looks Like with Necodex

Our engineers don’t just “join the repo”—they join the team.

We ensure:

• Daily standups + async-ready communication

• Embedded sprint cadence

• Devs work in your stack (LangChain, Copilot, Notion, GitHub, etc.)

• Optional delivery manager to help you track throughput

Based in LATAM. Online when you are. Always engaged.

Tasks Don’t Ship Products, Teams Do

In 2025, AI startups that scale with fragmented teams will hit walls. Fast. You can’t afford rework, misalignment, or disconnected workflows.

An embedded nearshore team gives you the best of both worlds:

• Real-time collaboration

• Fast ramp-up

• Long-term team fit

• Lower cost than U.S. hiring

Need a nearshore team that works like it’s in-house? Book your strategy session with Necodex.

Let’s help you build one—fully embedded, AI-ready, and fast to onboard.