Talent, Enablement & AdvisoryAI Engineering Talent
FDE Sprint
In eight weeks, BigStep forward-deployed engineers work inside your team to put one AI system into production, and leave your own engineers able to change and redeploy it without us.
FDE Sprint
$40K–$100K
- Duration: 8 weeks
- Tier: Embed
- Format: Managed capacity
You leave withA deployed AI system and structured knowledge transfer to the client’s team
Book a Free SessionSound familiar?
We need this live this quarter, and we don’t want a black box when the agency leaves. Our engineers are good, but none of them has put an LLM system into production before.
What you leave with
System in production
Format: Your environment
How we know it’s acceptableMeets the production-ready definition agreed in week 1.
Evaluation set
Format: Your repository
How we know it’s acceptableRe-runnable by your team without us.
Architecture document
Format: Document
How we know it’s acceptableReviewed and accepted by your tech lead.
Runbook
Format: Document
How we know it’s acceptableYour named engineer has followed it to redeploy the system.
Knowledge-transfer checklist
Format: Signed document
How we know it’s acceptableEach named engineer has made and deployed a change unaided.
Also included
- Weekly pairing between our engineers and your named engineers.
- A final handover session.
How it runs
8 weeks
Week 1
Scope, architecture, success criteria, knowledge-transfer plan.
OutA one-page spec and the agreed definition of production-ready.
Weeks 2–4
Build, paired.
OutA working version in staging by the end of week 4.
Weeks 5–6
Hardening, evaluation, monitoring; your engineers lead the changes.
OutEvaluation baseline and monitoring in place.
Week 7
Production deployment.
OutThe system live.
Week 8
Handover.
OutRunbook, signed knowledge-transfer checklist, handover session.
Is this right for you?
This is for you if
- Buyerthe engineering leader who has a specific AI system to ship and wants your team to learn by building it.
- Also has to say yesthe team lead whose engineers will pair with ours, because their time is committed for eight weeks; security; Finance above the threshold.
When this isn't the right fit
- You have no engineers to receive the knowledge. The AI Engineering Pod - Full Stack or the AI Feature Sprint fits better.
- Your engineers cannot commit pairing time. Knowledge transfer is half of what you are buying. Without it, the AI Feature Sprint is the honest answer.
- Your system is not defined, or the data is not accessible. Start with the AI Opportunity Sprint.
- The work clearly needs more than eight weeks. Split it into two sprints, or use an Embedded AI Engineer (FDE).
What’s included, and what isn’t
In scope
- one system
- one production environment
- knowledge transfer to the named engineers
- documentation, evaluation set and monitoring
Out of scope
- a second system
- data cleanup
- changes to unrelated systems
- support after handover (Managed Services)
- training anyone beyond the named engineers
Before we start
Before day one
- Your named engineers with committed pairing time.
- Repository, environment and data access.
- Representative sample data, not a cleaned subset.
- A production environment and a known path to get deployment approved.
- Agreed success criteria.
What we’ll need from your team
- Two named engineers, eight to ten hours a week each, pairing with ours.
- A domain expert, about two hours a week, to say whether the system’s answers are right.
- A platform or DevOps engineer, about two hours a week, for access and deployment.
- An approver available within 24 hours.
Pricing
What we need to give you a quote
- A description of the system.
- Data access.
- How many of your engineers will take part, and their available hours.
- The target environment and its deployment approval process.
- What production-ready means for them.
- The integrations involved.
After we hand over
Who owns it after we leave
Your named engineers who went through the knowledge transfer. What it displaces: maintaining the system and the evaluation set. An estimate of the weekly time needs measuring during weeks 5–8, not guessing now.
How success is measured
- One system metric agreed in week 1 (for example accuracy or cost per call)
- The number of your engineers who shipped a change on their own
What comes next
- A second FDE Sprint for the next system.
Questions buyers ask us
Yes, that is the AI Feature Sprint. This is for teams who need to own the system after we leave.
Fair. Then this is not the right programme, and we would rather tell you now than discover it in week four.
It ends in production, not in a demo. There is a working version in staging by week four.
We agree what production-ready means in week one. If something has to give, it is scope, not quality, and we say so at the time.
Mostly it is one engineer versus two, which depends on the system’s complexity and how many of your engineers we are bringing up to speed.