Talent, Enablement & AdvisoryAI Engineering Talent
AI Engineering Pod — Full Stack
A BigStep team that takes an AI product from architecture to running in production — AI, backend, frontend and infrastructure — and is accountable for it shipping.
AI Engineering Pod — Full Stack
$55K–$90K a month
- Duration: 3-month minimum
- Tier: Embed
- Format: Managed capacity
You leave withEnd-to-end AI product delivery from architecture through deployment
Book a Free SessionSound familiar?
We know what we want to build and we have the budget, but we don’t have the team. Every agency we’ve spoken to either builds the app or does the AI, and we’d be the ones stitching the two together.
What you leave with
Architecture document and decisions log
Format: Document in client’s wiki
How we know it’s acceptableReviewed and accepted by the client’s technical approver.
Code, CI/CD pipeline
Format: Client’s repository
How we know it’s acceptableBuilds and deploys from the client’s pipeline without BigStep credentials.
Deployed product
Format: Client’s cloud
How we know it’s acceptablePasses the production-readiness checklist agreed in discovery.
Monitoring dashboards and alerts
Format: Client’s tooling
How we know it’s acceptableAlerts reach a client-side contact, not only us.
Runbook
Format: Document
How we know it’s acceptableThe named client owner can carry out the common operations alone.
Sprint reports
Format: Document, fortnightly
How we know it’s acceptableAccepted at each demo.
Also included
- A cross-functional pod with a named delivery lead. Proposed composition, to be confirmed in the role-definitions document: tech lead/architect, AI engineers, a full-stack engineer, part-time DevOps.
How it runs
3-month minimum
Onboarding
Weeks 1–2
Discovery and architecture
OutArchitecture document, initial backlog, milestone plan.
Then, on a repeating cadence
Weeks 3 onward, every two weeks
Build
OutIncrements in staging and a demo each sprint.
Before launch
Hardening, security review, load testing
OutCompleted production-readiness checklist.
Launch and handover
OutProduction deployment, runbook, handover to the named owner or to Managed Services.
Is this right for you?
This is for you if
- Buyerthe person who owns getting the product to market and has no engineering team to build it — a founder, a CPO or a business-unit head.
- Also has to say yesthe CTO or IT lead, if there is one (architecture and hosting standards); security (data access); legal (IP ownership); and Finance, since this is a recurring monthly commitment over at least three months.
This is the wrong answer if
- The client already has a capable engineering team and only lacks AI skills. The Standard pod is cheaper and avoids two teams owning one product.
- The product is not defined — no clear users or problem. Start with the Possibilities Session or the AI Opportunity & Roadmap Sprint.
- It is one AI feature in an existing product. That is the AI Feature Sprint.
- The client needs a fixed price for something undefined. This is ongoing capacity; if they need a fixed price, define the scope first and sell a Launch programme.
- The client will not give us authority over the architecture. We cannot be accountable for what we do not control.
What’s included, and what isn’t
In scope
- architecture, AI, backend, frontend and DevOps as agreed in the pod
- deployment to the client’s cloud
- monitoring set-up
- handover
Out of scope
- product strategy and UX research unless agreed separately
- cloud, licence and model API costs
- 24x7 production support after launch (Managed Services)
- data labelling or cleanup
- marketing or app-store work
Before we start
Before day one
- A signed SOW.
- A product owner with authority to decide scope.
- A cloud account we can deploy into.
- Access to the data sources and APIs the product needs.
- Designs, or an agreed approach to design.
- Security and compliance requirements written down.
What we’ll need from your team
- The product owner, four to six hours a week.
- A cloud or IT administrator, about two hours a week in the first month, less after.
- A domain expert, about two hours a week, to judge whether the AI’s outputs are right.
- A decision-maker available within 24 hours on scope questions.
Pricing
What we need to give you a quote
- A product brief, PRD or designs.
- Users and expected volumes.
- Hosting constraints.
- The list of systems it must integrate with.
- Compliance requirements.
- The target launch date.
After we hand over
Who owns it after we leave
A named client engineering lead, or — if the client has no engineering team — Managed Services. This must be decided before the launch milestone, not after it, because otherwise the product becomes nobody’s job.
What we measure
- Time to first production release
- Production quality (uptime, evaluation scores, cost per request)
- One business metric chosen with the client in week 1
What comes next
- Managed Services — Essential or Growthonce live
- Continuing the pod for version two
- The Agent Scale Launchwhere the product grows into a larger agent system.
Questions buyers ask us
Because the seam between them is where AI products fail: latency, cost per call, evaluation, the data contract. With one team, one party is accountable for the whole thing working.
Your repository, your cloud account, a documented runbook, and a handover built into the plan from the start.
Compare it with hiring an architect and four engineers, the months that takes, and the fact that you can stop after three months.
Fair. For an undefined product a fixed price is a padded price. If you can define the scope, we will quote a fixed-scope Launch programme instead.
Working software in your environment every two weeks, and a milestone review every month where scope and date are traded explicitly.