We place experienced engineers—not contractors—into product companies building or scaling AI-powered systems. If you are looking for a cheap dev shop or a quick MVP, we are not the right fit.
See If We Are a FitTypical engagement: 6-12 months | US timezone overlap | Architecture-led delivery
We are selective about who we work with. Not because we are difficult—but because long-term embedded work only succeeds when there is a genuine fit on both sides.
We are not a body shop. We do not have a bench of junior developers waiting to be staffed.
Our engineers have built and deployed RAG pipelines, LLM orchestration systems, vector search, and intelligent automation—in production, at scale. Not demos. Not POCs. Real systems handling real traffic.
If you are adding AI capabilities to your product, we speak the language.
Every engagement starts with architecture review. We do not just write code—we make sure the code fits into a system that will scale, remain maintainable, and not become technical debt in 18 months.
Our engineers think in systems, not just tickets.
We do not staff juniors supervised by a single senior. Every engineer we place has 7+ years of experience, has led projects, and can operate autonomously within your team context.
You get engineers who solve problems, not create new ones.
Our team overlaps with US business hours (EST/CST/PST). This is not about location—it is about being present for standups, design discussions, and real-time problem-solving.
Async-first sounds good in theory. In practice, embedded engineering requires synchronous collaboration.
We do not treat your codebase as someone else problem. Our engineers take ownership of the systems they work on—documentation, testing, monitoring, and handoff included.
When an engagement ends, you are not left with mystery code and missing context.
We do not do packages. We structure engagements based on what your team actually needs.
Who it is for: Product teams that need experienced full-stack or backend engineers to increase velocity on core product work.
Typical duration
6-12 months
Who it is for: Teams building AI-powered features who need engineers with production LLM and RAG experience—not just ML researchers.
Typical duration
4-12 months
Who it is for: Companies building new products or platforms who need a complete, self-sufficient engineering team—but want to maintain product ownership in-house.
Typical duration
6-18 months
Who it is for: Teams with existing products that need dedicated focus on infrastructure, performance, and operational stability.
Typical duration
3-9 months
We start with a technical conversation—not a sales call. We want to understand your architecture, your team structure, and what you are actually trying to accomplish. This is also where we determine if there is a fit. We will be honest if we do not think we are the right partner.
Based on the technical requirements, we match engineers from our team who have relevant experience. You will meet them directly and have the opportunity to assess fit. We do not send you whoever is available. We send you the right people.
Our engineers invest significant time upfront understanding your codebase, architecture, and team workflows. We do not expect to be productive on day one—we expect to be productive for the next 12 months. This includes documentation review, architecture walkthrough, and initial pairing with your team.
We operate as part of your team—same standups, same sprint cadence, same communication channels. Our Technical Lead maintains regular check-ins to ensure alignment and address any friction early. We adapt to your process, not the other way around.
As your needs evolve, we can scale the team up, adjust composition, or begin a structured transition to hand off ownership to your internal team. We plan for endings from the beginning. Knowledge transfer is built into how we work, not an afterthought.
We do not publish client logos without permission, and most of our clients prefer confidentiality. Here is what we can share.
Embedded a 4-engineer pod for 14 months to rebuild their transaction processing system.
Joined a 3-person founding team to build their core AI features from scratch.
Provided a platform reliability pod to stabilize a system processing millions of records daily.
Embedded AI engineering pod for 8 months to productionize ML models stuck in notebooks.
All engagements above: 6+ months, production systems, measurable outcomes.
We are selective about the teams we work with—and we expect you to be selective too. If you are building something serious and need senior engineers who can operate at your level, let's talk.
No sales pitch. No pressure. Just a technical conversation to see if there is alignment.
See If We Are a FitTypical response time: 1-2 business days | We will be honest if we are not the right partner