AI is only as reliable as the platform under it. We migrate, automate and run AWS environments that hold up under real load, audits and attacks.
Cloud and AI infrastructure is our service for migrating to AWS, automating delivery with DevOps, sizing compute for AI workloads and keeping cost and recovery under control. It is for CTOs and operations leaders who run production systems and want AI on top of them without adding downtime, risk or unplanned spend.
Most AI programs do not fail on the model. They fail on the platform. Foundry's 2026 research puts integration cost (36%) and missing in-house deployment expertise (33%) at the top of the barrier list, ahead of anything to do with model quality. Enterprise AI RFPs now put 20% of the score on deployment and infrastructure and another 15% on observability and cost control.
An inference workload does not behave like a web application. GPU capacity is scarce and expensive, retrieval needs low-latency storage, and traffic arrives in bursts that autoscaling tuned for page views handles badly. If the environment was sized for last year's application, the pilot works and production does not.
Then there is the bill. Cloud spend grows with usage and with neglect: idle instances, over-provisioned databases, storage in the wrong class, no budget alerts. Without FinOps discipline, the cost of running AI erases the savings it produced.
Which platform, which services, what it will cost and what moves first.
On-premises to AWS, or cloud to cloud, without a maintenance window your customers notice.
Compute, storage and networking sized for training, inference and retrieval, on the AWS services you already pay for.
Infrastructure as code and automated pipelines, so every change is repeatable and reversible.
The bill is an engineering artifact. We treat it that way.
Backups an attacker with admin rights cannot delete, and a restore you have rehearsed.
Ongoing management so the environment stays inside the controls your auditors expect.
A migration is a project. Running the platform is a discipline. This is what stays in place after cutover.
Every service exports metrics, logs and traces. Dashboards show latency, error rate, saturation and cost per workload. Alerts route to a named on-call engineer, not a mailbox.
Infrastructure changes go through code review and a plan step before apply. Production deploys are gated, canaried and reversible within minutes.
Backups are restored on a schedule, not just taken. RPO and RTO are measured in each drill and reported, so the numbers in the runbook are real.
Budgets and anomaly alerts per account. A monthly FinOps review turns idle capacity, wrong storage classes and unreserved steady-state load into a savings backlog with owners.
6 monthsto complete the migration to AWS
A marketing SaaS serving 3,500+ brands in 70+ countries moved from IBM to AWS in six months, with weekend cut-over and one-minute replication.
0data loss: clean recovery points even after admin-level compromise
Immutable AWS Backup vaults, S3 Object Lock, cross-account replication and DRS restore templates keep a SaaS provider recoverable after admin compromise.
40+US carriers aggregated in the platform
A San Francisco logistics SaaS re-architected to microservices on AWS in two years, with carrier rate comparison and portals used by Fortune 500 shippers.
We inventory infrastructure, applications and data, measure current cost and performance, and define what the target environment must do for the workloads you run and the AI you plan to run.
Target architecture on AWS, migration waves, tooling and a cost model. Risks and their mitigations are listed before anything moves. You receive the plan within days of the discovery call.
Data is cleaned and converted, environments are built from Terraform, pipelines are stood up, and cutover happens in a tested window with rollback ready. First deliverables within 2 weeks.
Monitoring, DR drills, FinOps reviews and security management continue for as long as you want us on the platform. 89% of clients stay longer than 12 months.
Cloud migration services move applications, data and infrastructure from on-premises hardware or another provider to a cloud platform, most often AWS in our case. The work covers assessment, target architecture, data cleanup and schema conversion, replication, cutover, validation and post-migration support. Done well, the business does not notice the move; the SaaS provider we migrated from IBM to AWS kept serving 3,500+ brands with zero downtime.
It depends on the number of applications, the volume and shape of the data, and how much has to change on the way. A single application with a standard database can move in weeks. A multi-application estate with heterogeneous databases is a program of several months, delivered in waves so that each wave is validated before the next starts. You get a dated plan after the assessment, and first deliverables within 2 weeks.
AWS is our primary platform: we are an AWS Partner at Select Tier, and the team holds AWS certifications in architecture, security, development and operations. We also work on Microsoft Azure, Google Cloud and Oracle, and in hybrid setups where some data must stay in your data center. For data and AI platforms we are a Databricks Consulting Partner. The choice follows your workloads and existing contracts, not our preference.
Three ways. Hardware and data-center costs disappear, and capacity is paid for as it is used. Automation removes manual operations work: provisioning, patching, scaling and backups run from code. And a FinOps practice keeps the bill honest with reserved and spot capacity, right-sizing, storage tiering and budget alerts. The savings are not automatic; a migrated estate with no cost governance can cost more than the data center did.
We clean and profile data first, convert schemas with tooling rather than by hand, and set up replication between the old and new environments so that the new one is continuously up to date. Cutover happens off-peak, in a rehearsed window, with a rollback path that has been tested. After cutover we validate against production metrics before the old environment is retired.
Inference and retrieval have different profiles from web traffic: GPU or accelerated compute, low-latency access to vector and feature stores, bursty demand, and strict data-residency requirements because prompts and documents are sensitive. We size and isolate that capacity on SageMaker, Bedrock and EKS inside your AWS account, version models and prompts in the deployment pipeline, and put cost controls in place, because AI spend grows faster than most teams expect.
A 45-minute discovery call. We will tell you whether your current environment is ready for the AI you plan to run, and what it would take if it is not.
AWS partner · Databricks partner · ISO 27001 · You own the code from day one
