A full-stack FDE pod is 3–5 tkxel product engineers who join your repo, sprints and tooling to build one AI-enabled feature end to end, from data access and model calls to UI, tests and release, then hand it to your team.
Book discovery callThe demo worked on clean sample data and a single happy path. Production needs auth, error states, latency budgets and a fallback when the model is wrong.
Your engineers own the core product and on-call. An AI feature that touches frontend, backend and data rarely gets a full sprint of their attention.
Model behavior changes with every prompt edit or provider update. Without evals, logging and token budgets, you find regressions through support tickets.
A separate app or sandbox repo looks fast until you try to merge it. Then your team inherits code that ignores your patterns and pipeline.
We turn one business outcome into a buildable spec: user flows, acceptance criteria, the data the feature needs, model options and a cost estimate per request. You get a one-page decision record before any code is written, so scope stays fixed.
We build the APIs that call your chosen models through a single gateway with retries, timeouts, caching and rate limits. Provider keys stay in your secrets manager, and swapping models later means changing configuration, not rewriting services.
When the feature needs your documents or records, we build chunking, embedding and retrieval against your vector store or database. Access rules from your source systems carry through, so users only see answers drawn from content they can already open.
We build the screens in your existing framework and design system: streaming responses, citations, edit and retry controls, and clear states for loading, empty and failed answers. Users see where an answer came from and can correct it.
We write an evaluation set from real examples and run it in CI on every prompt or model change. Input filters, output checks and human-in-the-loop review queues catch bad answers before they reach customers or downstream systems.
We log prompts, responses, latency and token spend per feature and per tenant, with dashboards and alerts in the tools you already run. You can see which requests cost the most and set budgets before a bill surprises you.
Code goes through your pull request reviews, test suites and deployment pipeline. We ship behind feature flags, roll out to a small cohort first and keep a documented rollback path for every release the pod makes.
Share the context and we’ll scope a production path in one call.
Book a scoping callWe review your codebase, architecture and data access, then agree on one measurable outcome with your product owner. We also confirm how the feature fails safely.
Deliverables: Outcome brief, architecture notes, acceptance criteria, risk log
We ship a working end-to-end slice behind a feature flag in your staging environment. Internal testers try it against an agreed eval set.
Deliverables: Working slice, eval set, feature flag, demo recording
We add guardrails, monitoring, load tests and cost limits, then roll out in stages. Your engineers review every pull request as it lands.
Deliverables: Production release, dashboards, runbooks, rollback plan
Your team takes the on-call rotation while we pair on fixes and the next small change. We close with a walkthrough of the code, prompts and evals.
Deliverables: Handover sessions, documentation, eval suite, next-step backlog
A single pod owns frontend, backend and data for the feature, so no handoff between separate teams slows the release.
We work in your repo, follow your conventions and pass your reviews, so nothing needs a rewrite to merge.
Eval suites run in CI on every prompt and model change and block merges that fall below the agreed threshold.
Per-feature token logging, caching and budget alerts show the cost of each user action from day one.
Pairing, runbooks and a handover sprint move on-call and future changes to your engineers.
Ready to see this in your own workflows?
Book discovery callEach route can ship an AI feature; they differ in who owns delivery and what stays behind afterwards.
| Criterion | In-house team only | Freelancers or staff augmentation | tkxel full-stack FDE pod |
|---|---|---|---|
| Who owns the outcome | Your team | You manage each person | Pod owns one outcome |
| Time to first slice | Depends on roadmap capacity | Depends on your onboarding | Thin slice within weeks |
| Works in your codebase | Yes | Yes, under your direction | Yes, through your reviews |
| Evals and guardrails | Built if time allows | Rarely in scope | Part of definition of done |
| Knowledge after delivery | Stays in house | Leaves with contractors | Handed over with runbooks |
| Fails when | Roadmap crowds out the feature | Nobody coordinates the specialists | Outcome stays vague after scoping |
“Placeholder quote — replace with a real client quote.”
“Placeholder quote — replace with a real client quote.”
“Placeholder quote — replace with a real client quote.”
We build in your repositories and environments from day one, so there is no separate rebuild between proof of concept and release.
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.
Autonomy matrices, audit trails, and human review are designed with your risk and security teams before release, backed by ISO 27001 controls.
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.
“Placeholder testimonial — replace with the global set from tkxel.com.”
“Placeholder testimonial — replace with the global set from tkxel.com.”
A full-stack AI development team is a small group of engineers who build an AI feature across every layer: data access, backend services, model calls, user interface, tests and deployment. At tkxel this is a pod of 3–5 engineers that works inside your repository and sprint cadence. Because one team owns every layer, retrieval, the model gateway and the interface are designed together instead of being stitched across separate teams.
A pod owns an outcome, while staff augmentation adds individuals you manage. With augmentation, you plan the work, coordinate the specialists and carry the architecture risk. A tkxel pod agrees on one measurable result with your product owner, plans its own work inside your process and is accountable for getting it to production. You still review the code and set priorities, but you do not manage day-to-day tasks.
The pod builds features where a model works on your data inside your product, such as copilots, semantic search, document summarization, classification, drafting assistants and agent workflows that call your internal APIs. It also builds the supporting pieces those features need: retrieval pipelines, review queues, logging and admin controls. If the feature depends on training models on large proprietary datasets, we pair the pod with tkxel’s machine learning engineers.
Yes, the pod works in your repository, ticketing system, CI/CD pipeline and cloud accounts under your access policies. Every change goes through your pull request reviews and your deployment process. tkxel is ISO 27001 certified, and we follow your security requirements for access, secrets and data handling, including working only in approved environments when production data is involved.
Most single-feature engagements run seven to thirteen weeks from scoping to handover, based on the four phases we plan for. A thin end-to-end slice usually reaches staging within the first month. The timeline stretches when data access approvals, security reviews or unclear acceptance criteria hold up the work, which is why we surface those blockers in the first two weeks.
A pod is the wrong choice when the work is a simple API call that your own senior engineer can add in a few days. It is also a poor fit if you cannot name one outcome or give the pod access to your codebase, because embedding is how the pod avoids building something you cannot merge. The trade-off is that a pod costs more per week than a single contractor, in exchange for owning delivery end to end.
After handover your team owns the feature, including on-call, prompts and the eval suite. During the final phase we pair with your engineers on real fixes, walk through the code and document runbooks for common failures. If you want help after that, you can keep one engineer on a light support arrangement or start a new pod on the next outcome.
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