Cloud

Cloud & Infrastructure Services: tuned for always-on AI agent workloads.

Autonomous systems run differently: bursty, always-on, and unforgiving of cold starts. We migrate and modernise the infrastructure underneath them. Landing zones, orchestration, and cost controls tuned for tens of thousands of agent runs a month. So the platform scales with the work instead of throttling it.

99.9% platform uptime under autonomous load

10K+ agent runs a month in production

Talk to an Expert Browse Case Studies

Pakistan

UAE

USA / Pakistan

USA

Pakistan

Qatar

Pakistan

UAE

Pakistan

UAE

Pakistan

UAE

Pakistan

Qatar

Pakistan

UAE

USA

Pakistan

UAE

USA / Pakistan

USA

Pakistan

Qatar

Pakistan

UAE

Pakistan

UAE

Pakistan

UAE

Pakistan

Qatar

Pakistan

UAE

USA

Pakistan

USA

Pakistan

UAE

Singapore

USA

USA / Pakistan

USA

Australia

USA

Denmark

USA

China

USA

Angola

USA

Pakistan

UAE

Pakistan

USA

Pakistan

UAE

Singapore

USA

USA / Pakistan

USA

Australia

USA

Denmark

USA

China

USA

Angola

USA

Pakistan

UAE

Pakistan

Outcomes

What you get, stated as results.

Infrastructure that scales with agent load, not against it
Per-workflow cost visibility and automated controls
Reliability engineered for systems that run unattended

Capabilities

What this practice covers.

Migration & modernisation

Move workloads to cloud without lift-and-shift regret. Re-architected for elasticity and observability.

Landing zones

Secure, compliant cloud foundations with identity, networking, and guardrails set from day one.

Agent-scale orchestration

Compute and queueing tuned for spiky, parallel agent workloads that a standard web tier would buckle under.

FinOps & cost control

Per-workflow cost visibility and automated controls, so agent throughput never becomes a surprise bill.

Reliability engineering

SLOs, autoscaling, and failover designed for systems expected to run unattended.

Observability

Traces and metrics that span application, agent, and infrastructure in one view.

The stack behind this service.

Real tools our team works in day to day, not a generic capability list.

AWS

Azure

Google Cloud

Terraform

Kubernetes

How it works

A sequenced path, not a big bang.

99.9%

Platform uptime under autonomous load

Assess the estate

Map current workloads, dependencies, and the cost of standing still.

Lay the foundation

Landing zone, identity, and guardrails before anything moves.

Migrate in waves

Re-architect and move workloads in sequenced, reversible waves.

Tune for autonomy

Scaling, FinOps, and reliability tuned for always-on agent load.

Proof, Not Promises

Real engagements, the results attached to each one.

Cut-over completed with 4 minutes of planned downtime

Workforce scheduling SaaS firm, Manchester

Out-of-hours pages reduced by 64% in 3 months

Telecoms cooperative, Upper Midwest

Environment approved by compliance in 3 weeks

Radiology group, Midwest

6 store apps consolidated into 1 landing zone

Convenience store chain, Southwest

Where it applies

Telecommunications Technology Public Sector

Ten industries. One audited standard.

Different regulations, different systems of record — the same discipline every time: agents that run inside guardrails and leave a record you can read.

View all industries →

BFSI

Retail & CPG

Telecom

Technology

Healthcare

Public Sector

PropTech

Education

FinTech

AgriTech

Startups

Cloud & Infrastructure case studies

Challenge, solution, results. What this practice has shipped.

Anonymised at the client's request. Each card states the pain point, what we built, and what changed. Plus the service type and country.

04 studies

Web UK

Zero-downtime Azure migration for a Manchester scheduling SaaS

Workforce scheduling SaaS firm, Manchester

Challenge: The platform ran on two rented servers in a colocation facility. A disk failure had caused a nine-hour outage, customers were asking about resilience in procurement questionnaires, and the engineering team of six had no capacity to plan a migration while shipping features.
Solution: AGS designed an Azure landing zone with infrastructure as code, containerised the application and moved the database using replication so cut-over happened in a maintenance window of minutes. We ran two rehearsals, handed over runbooks and stayed on for hypercare for the first month.
Results: Cut-over completed with 4 minutes of planned downtime
Monthly hosting cost down 23% against colocation
Migration delivered in 9 weeks

AI Agents USA

Incident remediation agents for a rural telecoms cooperative

Telecoms cooperative, Upper Midwest

Challenge: A three-person operations team covered a cloud estate supporting billing, provisioning and customer portals. Out-of-hours alerts woke someone most nights, usually for the same handful of issues: full disks, stuck queues and expired certificates. Fixes were documented but applied by hand.
Solution: We built remediation agents that handle the recurring alerts: each one follows the team's runbook, applies the fix in a controlled way and posts what it did and what it saw. Anything outside the runbook pages a human with the diagnostics already gathered. All actions are logged and reversible.
Results: Out-of-hours pages reduced by 64% in 3 months
Median time to fix recurring alerts under 4 minutes
Agents live for 11 alert types within 8 weeks

AI MVPs USA

GPU inference sandbox for a Midwest radiology group

Radiology group, Midwest

Challenge: The group wanted to evaluate report-drafting models on its own imaging data but had no environment that satisfied its compliance officer. A previous attempt using shared cloud notebooks was stopped over data handling concerns, and GPU costs from an earlier trial had surprised the finance team.
Solution: An eight-week engagement to build an isolated evaluation environment: private networking, a de-identification pipeline, GPU capacity that scales to zero when idle, and full access logging. Two candidate models were benchmarked against radiologist-written reports with the results presented to the clinical and compliance leads together.
Results: Environment approved by compliance in 3 weeks
GPU spend held to $2,100 across the evaluation
Two models benchmarked on 1,500 de-identified studies

Apps USA

Landing zone for a Southwest convenience chain store apps

Convenience store chain, Southwest

Challenge: Store-level apps for stock counts, price changes and shift handovers had been built by different teams on different cloud accounts with no shared identity, networking or monitoring. A breach in one account could have exposed the others, and audit preparation took weeks each year.
Solution: AGS designed a multi-account landing zone with central identity, guardrails and logging, then migrated the store apps into it one at a time with the store IT team. We packaged app deployment as a template so future apps inherit the controls without extra work.
Results: 6 store apps consolidated into 1 landing zone
Audit evidence gathering cut from 3 weeks to 2 days
New app onboarding reduced to under 1 day

FAQ

Cloud & Infrastructure: questions, answered.

Why do autonomous agents need different infrastructure?

Autonomous systems run bursty, always-on, and unforgiving of cold starts. Compute and queueing are tuned for spiky, parallel agent workloads that a standard web tier would buckle under, so the platform scales with the work. Tens of thousands of agent runs a month, without throttling.

What uptime can we expect under autonomous load?

We engineer for 99.9% platform uptime under autonomous load. SLOs, autoscaling, and failover are designed for systems expected to run unattended, and observability spans application, agent, and infrastructure in one view so reliability is measured rather than assumed.

How do you keep agent workloads from becoming a surprise bill?

FinOps is built in, not bolted on. You get per-workflow cost visibility and automated controls, so agent throughput never becomes a surprise bill. Cost management is tuned alongside scaling and reliability for always-on load, tied to the SLOs you agree.

How do you approach cloud migration?

In sequenced, reversible waves. No lift-and-shift regret. We assess the estate, lay a secure landing zone with identity and guardrails before anything moves, then re-architect and migrate workloads for elasticity and observability, tuning for autonomous agent load once they land.

What is a landing zone and why do we need one?

A landing zone is a secure, compliant cloud foundation with identity, networking, and guardrails set from day one. We stand it up before any workload moves, so migration builds on solid ground rather than accumulating risk that has to be unpicked later.

Put Cloud & Infrastructure to work.

Tell us the workflow. We'll show you the shortest path to a running agent.

Book a Call