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Forward deployed engineering
AI engineers who work inside your business
AI engineers who work inside your business
until the system runs without us
tkxel embeds small engineering pods in your teams to scope, build, and govern production AI. You get working agents, data systems, and the internal capability to run them.
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Our clients
KnowlesSterne KesslerRepleniumABBClient fiveClient sixClient sevenClient eight
Six ways we take AI
from plan to production
Forward deployed engineering
AI strategy and readiness
Maturity assessment, process mapping, and a ranked use-case portfolio with business cases. You leave with a sequenced roadmap and a clear view of whether your data can support it.
Forward deployed engineeringFDE pods
Small embedded teams for full-stack, agentic, data engineering, and integration work. Each pod owns an outcome, ships it in your environment, and hands it over to your engineers.
Forward deployed engineeringAgentic platforms and integrations
Governed connectors to your enterprise systems, knowledge assistants that cite their sources, and manual workflows rebuilt as reviewed, production-grade agents with cost controls.
Forward deployed engineeringAdoption and enablement
Role-based training, internal champion networks, and train-the-trainer programmes. Adoption is measured with usage and literacy metrics so you can see which teams need support.
Forward deployed engineeringAI security and governance
Agent permission reviews, adversarial testing of AI systems, and governance councils that decide which AI investments move forward and under what controls.
Forward deployed engineeringProduct and engineering transformation
AI product strategy, rapid prototype sprints, SDLC redesign for AI-assisted delivery, and incremental modernization of legacy SaaS without a multi-year rewrite.
Not sure which engagement fits your situation?
A 30-minute discovery call is enough to point you to the right starting point.
Book discovery call
Does your AI programme look
like this right now?
Pilots everywhere, production nowhere
Several teams have prototypes, but none has passed security review or reached real users.
Strategy decks with no one to build
The roadmap is approved, yet internal engineers are fully booked on the existing product.
Licences bought, usage flat
Teams have AI tools but few changed how they work, and nobody measures adoption.
Risk teams say no by default
Without audit trails and clear autonomy limits, security has no basis to approve agents.
How an FDE engagement
runs from first call to handover
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We map workflows, check data readiness, and rank use cases by value and feasibility with your business owners.
Deliverables: Readiness scorecard, ranked use-case portfolio, roadmap
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A pod of three to five engineers joins your tools, ceremonies, and access controls, paired with the engineers who will own the result.
Deliverables: Team charter, access plan, success metrics
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The pod ships the first use case in your environment, scored against evaluation sets drawn from real work.
Deliverables: Production-ready system, evaluation report, architecture records
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Guardrails, review queues, monitoring, and cost controls go live with a limited user group before wider rollout.
Deliverables: Release plan, dashboards, runbooks
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Your engineers take over in stages while the pod moves to the next use case or steps out entirely.
Deliverables: Ownership transfer plan, enablement sessions
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 about the workflow you want AI to handle
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Frequently asked questions
What does forward deployed engineering mean?
Forward deployed engineering puts engineers inside the client’s environment instead of delivering from a distance. They work in your repositories, with your data, and alongside the people who run the workflow. The approach suits AI work because most of the difficulty sits in integration, edge cases, and adoption rather than in the model itself.
Why a separate FDE site instead of tkxel.com?
tkxel.com covers the full range of tkxel services. This site focuses on one delivery model: embedded engineering teams that take AI from strategy to production. Both are run by the same company, teams, and certifications.
Where do I start if I have no AI use cases defined yet?
Start with the AI strategy and readiness assessment. In two to four weeks it maps your workflows, ranks use cases by value and feasibility, and checks whether your data can support them. Many clients move straight from that roadmap into an FDE pod for the top-ranked use case.
Do you work only with Claude, or with other models too?
We work with the models your organisation has approved, including Claude, GPT, and Gemini, through direct APIs or through Amazon Bedrock, Google Vertex AI, and Azure. Model choice is made per workflow based on accuracy, cost, and data residency requirements.
Is forward deployed engineering right for every company?
Not always. If your need is a standard capability that an off-the-shelf product already covers well, a licence is usually cheaper and faster. FDE pays back when workflows are specific to your business, span several internal systems, or carry risk that needs custom controls. We will say so on the discovery call if a product fits better.
Which regions do you cover?
tkxel has offices in the USA, Saudi Arabia, Portugal, and Pakistan. Pods work on-site, remote, or hybrid, and we align working hours with your core team’s time zone.