Staff Augmentation & Outsourced Teams

Bring the right expertise into the work.

Embed vetted full-stack, cloud-native, SRE, and security engineers directly into your workflows.

Discuss this practice Hire a dedicated team

Cloud-native engineers
SRE
DevSecOps
Full stack
Search
AI / ML

4 capability areas · 6 services

What we deliver in Staff Augmentation.

Add an individual specialist under your direction, or engage a coordinated team with an agreed delivery scope. Choose the responsibility model deliberately.

Outsourced Cloud-Native & SRE Teams

Outsourced Cloud-Native & SRE TeamsDedicated engineers and SRE teams integrated into your workflows for continuous operations.

Typical deliverablesAgreed capacity · delivery ownership · team integration plan

Read the capability guide

Managed Engineering Operations

Typical deliverablesService scope · coverage & escalation terms · operating runbooks

Read the capability guide

Full Stack & Search Engineering

Typical deliverablesRole scope · technical fit · delivery integration

Read the capability guide

All-Practice Staff Augmentation

On-Demand Specialists, Every PracticeFlexible capacity across Cloud Native, DevSecOps, AI, TechOps, and FinOps.

Typical deliverablesExpertise requirements · responsibility model · working arrangement

Read the capability guide

Deliverables

What you receive.

We agree acceptance criteria, evidence and handover as part of the scope, before implementation begins.

A role or team scope mapped to your technical context
An agreed direction, acceptance and escalation model
Onboarding, access boundaries and a knowledge-transfer plan

In practice

Choose capacity and accountability together.

An example product team needs cloud and SRE expertise while maintaining control of its product priorities.

Step 1

Define the work, skill mix, collaboration hours and acceptance criteria.

Step 2

Choose a customer-directed specialist or DotConn-coordinated managed pod.

Step 3

Agree onboarding, reporting and handover before the engagement starts.

The useful result

A delivery model both teams can explain, with no assumed team size or hidden ownership.

Getting started

Start with the role or outcome you need.

Helpful starting context

Expertise, technical environment, expected work, preferred direction model, overlap hours and start window.

Set the boundaries early

Managed pods have no fixed size. Specialist availability, coverage and terms are confirmed during scoping. AI assistance does not imply a guaranteed productivity uplift.

Engagement models

Choose how we engage.

The practice defines the expertise. The engagement defines who owns what.

Consulting

Make a better technical decision.

Best when: You need clarity before you commit budget.
DotConn: Leads the assessment and recommendations.
You receive: Assessment, options, architecture decisions and a prioritized roadmap.

Discuss this model

Implementation

Deliver an agreed technical change.

Best when: The target is clear and needs building.
DotConn: Coordinates delivery and validates acceptance criteria.
You receive: A working system, tested configuration, documentation and handover.

Discuss this model

Managed service

Keep agreed work running.

Best when: You want a platform or IT area run for you.
DotConn: Owns recurring service activities and reporting.
You receive: Runbooks, service reports, review cadence and escalation records.

Discuss this model

Managed pod

One team around a delivery scope.

Best when: You have a backlog, not a headcount gap.
DotConn: Coordinates specialists and delivery reporting.
You receive: An agreed backlog, delivery ownership, working increments and knowledge transfer.

Discuss this model