Full-stack FDE pod
Product engineers who build features end to end in your codebase, from API and data model to interface. Suited to AI-enabled products where the model is one part of a larger application.
FDE pods
Small engineering pods that own an outcome
Small engineering pods that own an outcome
inside your environment
Each pod is three to five engineers who join your tools and ceremonies, ship one agreed result to production, and transfer ownership to your team before they leave.
Book discovery call
Our clients
KnowlesSterne KesslerRepleniumABBClient fiveClient sixClient sevenClient eight
Pods built around
the work you need shipped
Agentic FDE pod
Engineers who design, evaluate, and release multi-agent systems wired to your real systems. Every agent ships with evaluation sets, guardrails, and human review where it is needed.
Data engineering FDE pod
Pipelines, warehouse models, and data quality checks that give AI systems trustworthy inputs. The pod documents lineage and ownership so your data team can run it afterwards.
Integration and legacy modernization pod
Connectors to ERP, CRM, and older systems, plus incremental modernization that replaces fragile pieces one at a time instead of a multi-year rewrite.
FDE for SaaS and ISVs
Engineers who deliver implementations for your customers under your brand. Software companies use this to clear onboarding backlogs without hiring a services team.
Embedded FDE engineers
Individual senior engineers placed inside an existing team for a defined outcome, when a full pod is more than the work requires.
Not sure which pod fits your backlog?
Describe the outcome you need and we will suggest a pod shape on the call.
Book discovery callWhy do embedded teams
so often stall?
Extra hands without an owner
Augmented staff wait for direction, so the outcome still depends on managers who are already at capacity.
Pilots built outside your stack
Work done in a separate environment needs rebuilding before it can pass security review.
Specialist skills you need briefly
Agent evaluation or data modelling expertise is needed for months, not as a permanent hire.
No plan for the exit
When the outside team leaves, nobody inside knows how the system works or how to change it.
How a pod engagement
runs from scope to handover
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We agree on one measurable outcome, the systems involved, and who on your side will own the result.
Deliverables: Outcome statement, success metrics, pod shape, access plan
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Engineers join your repositories, tickets, and ceremonies under your access controls, paired with your future owners.
Deliverables: Team charter, working agreement, environment access
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The pod releases working increments to staging and then production, reviewed against the agreed metrics every sprint.
Deliverables: Production releases, sprint reports, architecture records
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Your engineers take the lead on changes while the pod reviews, then the pod steps out or moves to the next outcome.
Deliverables: Runbooks, ownership checklist, enablement sessions
FDE pod, staff augmentation, or consulting:
the honest comparison
All three can work. The difference is who owns the outcome and what stays with you afterwards.
| Criterion | Staff augmentation | Strategy consulting | tkxel FDE pod |
|---|---|---|---|
| Who owns the outcome | You | Shared, ends at recommendations | The pod, until handover |
| What you receive | Hours of capacity | Reports and roadmaps | Working systems in production |
| Where work happens | Your stack | Workshops and documents | Your stack, from day one |
| Knowledge after exit | Leaves with the people | Lives in slide decks | Runbooks and trained owners |
| Best for | Known backlog, strong leads | Undecided direction | Defined outcome, thin capacity |
| Fails when | Nobody has time to direct | Nobody builds the plan | No internal owner is named |
Featured success stories
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Engineers who ship inside your systems,
not isolated AI demos
Talk to an FDE lead
Production from the first sprint
We build in your repositories and environments from day one, so there is no separate rebuild between proof of concept and release.
Senior pods, not rotating benches
Each pod is led by an engineer who has shipped AI systems before, drawn from tkxel’s team of around 150 AI professionals. The same people stay through delivery.
Governance built in, not bolted on
Autonomy matrices, audit trails, and human review are designed with your risk and security teams before release, backed by ISO 27001 controls.
Measured against your metrics
Every engagement starts with agreed business metrics and evaluation sets. We report against them throughout, and the pod exits when your team can run the system.
We've been recognized by the best, year after year
- Inc. 5000
- Top 15 Inspiring Workplaces
- Titan Business Platinum (AI & Automation)
- Financial Times
- Mogul People Leader
- Forbes Coaches Council
- ISO 27001
- ISO 20000
- ISO 9001
- CMMI DEV 3
Tell us the outcome you want a pod to deliver
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Frequently asked questions
What is an FDE pod?
An FDE pod is a small forward deployed engineering team that works inside your environment to deliver one agreed outcome. It typically includes a lead engineer, two or three specialists, and a shared delivery lead. Unlike staff augmentation, the pod owns the result and plans its own exit from the start.
How long does a pod usually stay?
Most pods stay between two and four months for a first outcome. Larger programmes keep a pod longer and move it from one outcome to the next, while ownership of finished systems transfers to your team in stages.
Can a pod work in our time zone?
Yes. tkxel has offices in the USA, Saudi Arabia, Portugal, and Pakistan, and pods align their core hours with your team. Pods can work remotely, on-site, or in a hybrid arrangement.
Who owns the code and IP?
You do. The pod works in your repositories and cloud accounts from the first commit, under your contracts and access controls. There is nothing to transfer at the end except knowledge, which the handover phase covers.
When is a pod the wrong choice?
If your team already has clear technical leadership and only needs more capacity on a known backlog, staff augmentation is usually cheaper. If you have not yet decided what to build, start with the AI strategy and readiness assessment instead. A pod works best when the outcome is clear and internal capacity is not.
How is pricing structured?
Pods are priced per engagement phase, based on pod size and duration agreed during scoping. Discovery is fixed-price. We share the full estimate before the pod starts, and changes in scope are agreed in writing rather than billed silently.