Talent, Enablement & AdvisoryAI Engineering Talent
AI Engineering Pod — Standard
A dedicated BigStep team of AI engineers, managed by us, shipping against your own backlog in two-week sprints with a demo at the end of each, for at least three months, at $35K–$60K a month.
AI Engineering Pod — Standard
$35K–$60K a month
- Duration: 3-month minimum
- Tier: Embed
- Format: Managed capacity
You leave withManaged delivery with fortnightly demos and a clear sprint cadence
Book a Free SessionSound familiar?
We’ve got a roadmap full of AI features and the two engineers who know how to build them are drowning. Hiring takes six months a head, if we find anyone. Last time we used contractors we spent more time managing them than they saved us.
What you leave with
Code
Format: Your repository
How we know it’s acceptableMerged through your own code review.
Sprint reports
Format: Document, fortnightly
How we know it’s acceptableWalked through and accepted at each demo.
Monthly review pack
Format: Slides or document
How we know it’s acceptableReviewed with the decision-maker at the account review.
Handover notes
Format: Document in your wiki
How we know it’s acceptableYour engineer can pick up any open item without asking us.
Also included
- A dedicated pod of AI engineers with a named delivery lead as the single point of contact.
- Sprint planning every two weeks, and a demo of working software at the end of every sprint.
- A documented escalation path, and replacement of any engineer who leaves.
How it runs
3-month minimum
Onboarding
Weeks 1–2
Onboarding
Access, codebase familiarisation, backlog and PRD walk-through.
OutCompleted onboarding checklist and sprint 1 plan.
Then, on a repeating cadence
Every two weeks
Plan, build, demo
OutA shipped increment and a sprint report.
Every month
Account review
OutReview pack and any capacity changes.
Month 3
Renewal review
OutA decision to continue, scale up or ramp down.
Is this right for you?
This is for you if
- Buyerthe person who owns the engineering roadmap and cannot hire fast enough to deliver it. Usually a VP Engineering or CTO.
- Also has to say yesprocurement or vendor management (master agreement, approval of an external supplier); security (external engineers getting code and data access); Finance, because three months at the minimum is over $100K.
When this isn't the right fit
- You have no product owner and no prioritised backlog. A pod without direction burns budget quietly. Start with the AI Opportunity Sprint.
- The work is one defined build with a clear end. A fixed-scope sprint fits better and puts delivery risk on us.
- You want one senior person inside your team, not a team. That is the Embedded AI Engineer (FDE).
- You have less than three months of work. Most of month one is onboarding, and you would pay for ramp-up without getting output.
- You need us to own the architecture and the non-AI engineering as well. That is the AI Engineering Pod - Full Stack.
What’s included, and what isn’t
In scope
- the agreed pod working against your backlog
- sprint ceremonies
- demos
- fortnightly reports
- monthly review
- a delivery lead
- replacement of departing engineers
- a handover sprint at the end
Out of scope
- product ownership and prioritisation (that stays with you)
- architecture ownership (Full Stack)
- infrastructure, DevOps, cloud provisioning and hosting
- production on-call or 24x7 support (Managed Services)
- cloud, licence and model API costs
- work outside the agreed backlog without replanning
- recruiting your permanent staff
Before we start
Before day one
- A signed SOW and master agreement.
- Repository, environment, ticketing and chat access for every pod member.
- A named product owner with authority to set priorities.
- A prioritised backlog for at least the first two sprints.
- Agreed overlap hours.
- NDA and security paperwork completed for each engineer.
What we’ll need from your team
- The product owner, three to four hours a week, for backlog grooming, sprint planning and demos.
- An engineer for code review, three to five hours a week.
- A decision-maker for one hour a month at the account review.
Pricing
What we need to give you a quote
- A clear PRD (or equivalent product brief) for the features in the first two sprints, and a sample of the backlog beyond that.
- The stack and the skills required.
- Start date, expected duration and overlap hours.
- Security and compliance requirements for external engineers’ access.
After we hand over
Who owns it after we leave
Your engineering lead owns the code from the first merge. Nothing sits in a BigStep-only repository. At ramp-down, a handover sprint transfers open work; what it displaces is the review time your engineers were already spending on the pod’s pull requests.
How success is measured
- Share of committed sprint work delivered
- Cycle time from ticket to production
- Features shipped per month
What comes next
- Extending or enlarging the pod
Questions buyers ask us
You should, for the core team. The pod covers the months hiring takes and the peaks after it, and you can ramp it down once the minimum is up.
That is why the pod comes with our own delivery lead and a fixed demo every two weeks. You manage priorities, not people.
Fair question. Every two weeks you see working software and a written report of what was committed versus what shipped.
Most of month one is onboarding. Under three months you pay for ramp-up and get little output; if the work is that short, a fixed-scope sprint suits you better and we will say so.
Freelancers can work well for one-off tasks. What you don’t get is continuity, a replacement when someone leaves, IP assignment, and someone other than you managing the work.