Embedded AI Team
Your embedded AI team, without the full-time hire.
An embedded AI team is a dedicated group of AI engineers who work inside your business on retainer — not a project vendor that leaves when the build is done. We own your roadmap, keep your models healthy, and ship new work every month. No recruiting, no onboarding, no 12-month lock-in.
- Day 1
- active on your stack
- Monthly
- retainer, cancel anytime
- 1 team
- for AI, data, and ops
No onboarding lag — we ramp in the first week
No 12-month lock-in. Stay because it works.
Engineers, data scientists, and an AI lead in one retainer
What is an embedded AI team?
An embedded AI team is a dedicated group of AI engineers and data scientists who work inside your business on a monthly retainer. Unlike a project agency, they stay month to month — owning your roadmap, monitoring models in production, and shipping new capabilities continuously. Also called a fractional AI team or AI team augmentation, this model gives ecommerce brands access to senior AI engineers without the cost of full-time headcount.
- Built by ex-Kohl's and Sears AI leads
- Ecommerce and retail focus — no generalists
- Owns your roadmap, not just tasks
- Cancel with 30 days notice
Scope of ownership
What an embedded AI team handles.
Task services do what they're told. We own the outcome. That means figuring out what to build, building it, and keeping it running — without you having to manage any of it.
Model monitoring and health
Every model in production has someone watching it. When accuracy drops or data quality goes sideways, we catch it before it becomes a business problem — not after a bad week of orders.
- Forecasting model accuracy tracking
- Recommendation engine CTR monitoring
- Automated alerting on data anomalies
AI roadmap ownership
We run your AI backlog. Each month we come to you with a prioritized plan based on what's actually happening in the business — not a generic roadmap copied from a kickoff deck.
- Monthly roadmap review with your team
- Prioritization by revenue impact
- Scope locked before any sprint begins
New AI development
New AI ships to production every quarter. Not experiments, not notebooks — working systems integrated into your stack. You own the code and the docs when each piece is done.
- Model development and integration
- Feature engineering on your data
- Production deployment and rollout
Stack and data maintenance
Data pipelines break. Platform APIs change. Schema shifts upstream. That maintenance work falls to someone — with us, it falls to us, not your product engineers.
- Shopify and Amazon connector maintenance
- Data pipeline repairs and upgrades
- Schema migration and backfill jobs
How it works
How we work each month.
Same structure every month. No status-update anxiety, no wondering what the team is actually doing.
Week 1
Review and prioritize
We check model health, look at data quality, and get caught up on anything that changed in the business. Then we agree on what this month is about — what to build, what to fix, what to leave alone.
Output: Signed-off monthly plan
Weeks 2–3
Build and ship
The actual work. New features, bug fixes, model retrains — we work through the plan from week one. Code ships to your production environment, not a staging sandbox that gets thrown away.
Output: Working code in production
Week 4
Review and hand off
We go through what shipped, what the numbers show, and what we learned. Docs get updated. We draft next month's plan and get your sign-off before we start again.
Output: Monthly delivery report
Compare your options
Embedded AI team vs hiring vs agency.
Most brands in the $1M to $20M range aren't ready for a full AI team on payroll, and a one-off project goes stale by month three. The embedded model sits in between.
| Factor | Full-time hire | Project agency | Embedded AI Team |
|---|---|---|---|
| Time to productive | 3–6 months | 4–8 weeks (project only) | 1 week |
| Annual cost | $200k–$280k loaded | $80k–$150k per project | Retainer, adjust monthly |
| Owns the roadmap | Yes (if senior enough) | No — scoped to project | Yes |
| Ecommerce domain depth | Depends on hire | Depends on firm | Yes — retail AI only |
| Monitors production models | Yes | No — contract ends | Yes, every month |
| Scales up or down | No — fixed headcount | New contract required | Adjust scope monthly |
Why this model works
Ecommerce AI, not general AI.
Most outside teams need months to understand how ecommerce data actually works — the seasonality, the return rates, the supplier lead times, the Shopify quirks. Our engineers have built this in production already. They walk in knowing the domain.
The team comes out of Kohl's and Sears, where they ran AI at real retail scale. That experience is expensive to hire for on its own. As an embedded team, you get it on a monthly retainer.
Ecommerce domain expertise
Shopify, Amazon, WooCommerce. We've shipped on all of them. You're not paying us to figure out how your stack works.
No rotating teams
The engineer who starts in month one is still here in month six. No handoffs, no context lost between agency rotations.
Outcome accountability
You get a delivery report each month, not a timesheet. What shipped, what the metrics show, what's next.
You own everything
Everything we build is yours. Code, model weights, docs. If we ever part ways you have a complete, documented AI system — not a dependency on a TwoDots platform.
Common questions
Questions about embedded AI teams
What is an embedded AI team?
An embedded AI team is a group of AI engineers and data scientists who work inside your business on a monthly retainer, owning your AI roadmap and monitoring production models continuously. Unlike a project agency, they stay month to month and keep shipping new work rather than handing off and leaving. We work exclusively in ecommerce and retail.
How is this different from hiring an AI team member full time?
Senior AI engineers run $180k to $260k fully loaded, and you typically need more than one for anything to actually function. The embedded model gives you a senior AI engineer plus supporting data scientists on a monthly retainer. No recruiting, no benefits, no six-month ramp. You can also reduce or cancel if priorities shift — try doing that with a full-time hire.
What does AI augmentation mean?
It means adding a dedicated AI capability to your existing engineering team without pulling your product engineers into ML work. Your developers keep shipping features. The embedded AI team owns the model layer — training, pipelines, monitoring, infrastructure. The two groups work together but don't compete for the same sprint capacity.
Do I need to have existing AI in place before starting?
No. Some clients come with models already running in production and no one watching them. Others start with an AI Fit Sprint, move to a scoped implementation, and then retain the team to own what was built. Either entry point works — we figure out where you are in week one.
What ecommerce platforms do you support?
Shopify, WooCommerce, Amazon Seller Central, Magento, and custom ecommerce stacks. On the data side we work with BigQuery, Snowflake, PostgreSQL, and Redshift. If it has a usable API or data export, we can work with it.
Can I scale the team up or down?
Yes, and that's intentional. Scope is reviewed monthly. Peak season coming up? Add capacity. AI running smoothly and you just need maintenance? Dial it back. There's no minimum term beyond the first 30 days.
How does billing work?
Monthly fixed retainer, invoiced at the start of each month. Scope is agreed in writing before the month begins. No hourly billing, no end-of-month surprises. You know what you're paying and exactly what the team is delivering.
What is the difference between the Embedded Team and AI Implementation?
AI Implementation is a project with a defined scope, a committed date, and one deliverable. It ends when the system ships. The Embedded Team stays — it owns your roadmap month to month, monitors what's in production, and keeps building. Most clients do Implementation first, then move to Embedded once they have something worth maintaining.
What is a dedicated AI team?
A dedicated AI team for hire is a group of AI engineers and data scientists engaged on a retainer basis — fully focused on your business, not split across multiple clients. Unlike a staffing agency, a dedicated AI team owns the strategy and the delivery: roadmap, model development, monitoring, and infrastructure.
How do I get AI capability without hiring full time?
The most practical option for most ecommerce brands is a fractional or embedded model. You engage a specialist AI team on a monthly retainer rather than building headcount. The team for AI work covers the full stack — data pipelines, model training, production deployment, and ongoing monitoring. It costs significantly less than hiring two senior AI engineers, and you can scale the scope up or down as your AI roadmap evolves.
How much does an embedded AI team cost?
Embedded AI team retainers typically run $8,000 to $25,000 per month depending on scope. A maintenance-only engagement covering model monitoring and minor fixes sits at the lower end. A full embedded model with active development, roadmap ownership, and multiple engineers running parallel work sits at the higher end. Compared to hiring two senior AI engineers at $180k to $260k each fully loaded, a retainer delivers comparable capability at a fraction of the annual cost and with no recruiting overhead.
When is an embedded team the right choice?
Hire an embedded AI team when you have AI running in production but no one dedicated to maintaining or extending it, when your engineering team is pulled into AI work at the expense of product, or when one-off AI projects keep going stale after delivery. If you don't yet know what AI to build, start with an AI Fit Sprint first. If you know what to build but need a single scoped delivery, start with AI Implementation. The embedded model is the right fit once you have something worth owning long term.
What is the difference between an AI team and a data science team?
A data science team focuses on analysis, reporting, and experimentation — answering business questions with data. An AI team (or ML engineering team) builds systems that run in production: models that make decisions, pipelines that move data, and infrastructure that keeps everything monitored and updated. An embedded AI team does the latter. They ship working software, not slide decks or notebooks.
Get started
Ready to add an AI team member without the hiring risk?
Tell us what's in production and what you want to build. We'll scope the first month and get a retainer agreement in front of you. No discovery workshops, no 30-day sales cycle.