AI-Native Services through FDX
Forward Deployed. AI-Native. Every layer of your stack.
Forward Deployed FDX: the X is any forward deployed role, Engineer, Strategist, Data Scientist or subject matter expert. Cloud engineering is one offer in the model. The same embedded squad that models your customers' experiences builds your AWS foundation, takes your applications into production, and runs your data engineering and analytics as a service, with Client Specific Analytics Models connected to the Brain.
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3 offers
cloud, applications, and data engineering and analytics as a service
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One ontology
under the cloud, the applications and the analytics
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Your AWS
built in your organization, in Terraform you own
The model
What AI-Native Services means, and why the engineers are forward deployed.
serviceMob is an AI-Native Services (AINS) company: the model, the data ontology and the people who operate them arrive as one service, on a fixed cadence, inside your delivery organization. Four commitments hold every engagement together. Context: we understand the business before we write code. Ontology: we model your domain as structured data so it is machine-actionable. Signal: we extract the measures that predict outcomes. Taste: we apply judgment so what ships gets adopted.
FDX is the delivery model that carries it. The X stands for any forward deployed role: Forward Deployed Engineers (FDE) build and validate the data ontology and the pipelines; Forward Deployed Strategists (FDS) own the roadmap and the operating model; Forward Deployed Data Scientists (FDDS) build the predictive models and the statistical validation; subject matter experts (SME) bring contact center, CRM, workforce and industry depth. They work inside your operation, and they stay. The Brain for Customer Service & Support is the first thing they build. The offers on this page are what the same squad builds next, on the same ontology.
Three engineering offers
One squad. One ontology. Three things it builds.
AWS foundation and run
Forward Deployed Cloud Engineering
A secure AWS landing zone with identity, network, a Terraform module library, a reference pipeline and an observability baseline. Then your applications taken into production on it, with the runbooks, telemetry and on-call to run them. Scope and pace follow what your business needs, settled in the working session.
From prototype to product
Application engineering
Where the ontology calls for a new surface, the squad builds it: internal tools, customer-facing workflows, integrations between the systems that produce your service data. Prototyped with your team, hardened for production, handed over with a runbook.
Client Specific Analytics Models
Data engineering and analytics as a service
Pipelines, models and the metrics themselves, run for you. Each engagement produces CSAMs, Client Specific Analytics Models built on your data and connected ontologically to the Brain, so a forecast, a churn signal or a margin model reads the same experience record as everything else.
CSAMs
What a Client Specific Analytics Model is.
A CSAM is an analytics model built for one client on that client's data, expressed in the client's ontology and connected to the Brain. It is specific because the drivers of contact demand, margin and churn differ by business: booked nights for a hotel group, subscriber cohorts for a subscription business, patch cycles for a game. It is connected because every CSAM reads the same Experience Object the Brain maintains, so its outputs land next to AMPRx, CPRx and 1CX Rate instead of in a separate dashboard.
Forward Deployed Data Engineers build the pipelines, Forward Deployed Data Scientists build and validate the models, and the service keeps running them as your systems change. You buy the analytics program, delivered; the platform meters it.
A landing zone engagement
What a single-account estate becomes.
Growth-stage SaaS companies often run development, staging and production in one AWS account with long-lived access keys, because that account carried the product a long way. The foundation motion redesigns it:
- AWS Organizations under AWS Control Tower, with organizational units for security, infrastructure, production workloads, non-production workloads and a sandbox.
- Service control policies attached at the root and organizational units, inherited by every account.
- AWS IAM Identity Center federated to your identity provider, permission sets in place of standing access keys, short-lived roles for workloads.
- One AWS CloudTrail organization trail, AWS Config conformance packs in every account, Amazon GuardDuty enabled for new accounts, AWS Security Hub CSPM aggregating findings to one home Region.
- An AWS Transit Gateway hub with one spoke VPC per workload account and private endpoints for the services your code calls.
- GitHub Actions assuming an AWS role over OpenID Connect with one-hour sessions, Amazon ECR scan on push, Amazon ECS blue/green deploys with rollback.
Config conformance results and Security Hub findings give continuous control evidence mapped to SOC 2 criteria that you can hand an auditor.
Where your workloads run
The hosting sentence, as we say it everywhere.
Client workloads run in serviceMob's managed AWS environment, with controls aligned to the SOC 2 Trust Services Criteria, on AWS infrastructure covered by AWS's own SOC 2 Type 2 report.
Forward statements are targets. Every figure on this page is either part of the engagement record or the method itself.
Questions
Before the first engagement.
What does the squad look like?
Forward Deployed Engineers, a Forward Deployed Strategist, Forward Deployed Data Engineers and Data Scientists where the work calls for them, and subject matter experts. Our engineers pick up tickets from your board, join your standups and work with your security lead. One team, supported by serviceMob.
What is a CSAM, and who owns it?
A Client Specific Analytics Model: built on your data, in your ontology, connected to the Brain. You own the data and the outputs. serviceMob runs the pipelines and models as a service and keeps them current as your systems change.
How are the engineering offers priced?
Cloud engineering in three parts: a foundation fee, a fee per application taken into production sized by integration count and data sensitivity, and a monthly retainer for monitoring, runbooks and L2 and L3 support, with AWS consumption billed at cost. Application engineering per application. Data engineering and analytics as a monthly service sized by data sources and business units. Ranges follow in the written proposal.
Do you take over our AWS account?
The foundation is built in your organization, defined in Terraform you own, with runbooks handed to your team. Where you prefer serviceMob to host, the hosting sentence above is the arrangement.
Bring the prototype. Leave with a production service.
A working session covers your current estate, the first application worth hardening, the analytics model your business is missing, and what the foundation would need to look like underneath them.