AppVerse is a DevOps services company that builds secure, observable infrastructure on AWS, Azure or Google Cloud, with automated delivery and controlled costs. We also deploy and run AI models in your own cloud and region.
Infrastructure and AI run in your accounts, in the region your data requires.
Terraform, CI/CD and runbooks, so nothing depends on one person's memory.
Model serving, tracing, evaluations and spend limits for LLM features.
We work on the cloud you already use or help you choose one. Our DevOps services in AWS, Azure and Google Cloud cover architecture, infrastructure as code with Terraform, containers with Docker and Kubernetes where they are justified, and CI/CD pipelines with GitHub Actions that test, build and deploy on every merge.
Then we make it observable: logs, metrics, traces and alerts in Grafana and Sentry, with dashboards that show what customers experience, not just CPU graphs. Backups and disaster recovery are tested rather than assumed, and every infrastructure change goes through code review. It's how we run our own products, including the Bluck WhatsApp marketing platform and M361 SEO intelligence.
Our cloud DevOps consulting services start with a review of what you run today: architecture, deployment process, security posture, spend and the incidents that keep repeating. You get a written plan ranked by risk and effort, which our team can carry out or your own team can follow.
For migrations, we move workloads in stages rather than in one risky weekend: from on-premises servers or a single VM to managed services, from one cloud to another, or into a region closer to your users. Each stage has a rollback plan, and old and new systems run in parallel until the results match.
AI features add new things to run: model endpoints, vector databases, prompt versions, evaluation suites and per-request costs that shift with every model update. Our LLMOps work puts these under the same discipline as the rest of your stack, with tracing for every model call, evaluations that run before each release, and alerts when quality, latency or spend drifts.
Where data can't leave your environment, we deploy through managed services such as Amazon Bedrock, Azure AI and Vertex AI, or serve open-weight models with vLLM or Ollama on your own GPUs. Model routing and semantic caching keep unit costs predictable as usage grows.
Many buyers need data to stay in-country or in-region. AWS, Microsoft Azure and Google Cloud operate regions in many countries, but service availability differs by provider and by region. Before we design around a region, we check that every service your product needs, including AI model endpoints, is actually available where your data must live.
We build to support your obligations under frameworks such as GDPR, India's DPDP Act and other national data-protection laws, with region-locked storage, encryption, access controls and audit trails. The same controls support your SOC 2 / ISO 27001 audits. Your legal and compliance team confirms the requirements; we engineer to them and document how.
Cloud bills grow quietly. We start cloud cost optimization with visibility: tagging, cost dashboards per service and per customer, and budgets with alerts. Then we work through savings in order of effort: idle and oversized resources, storage tiers, autoscaling, reserved or committed-use pricing, and architecture changes where a managed service costs less to run than what you built.
AI spend gets its own attention. We route simple requests to smaller models, cache repeated answers, trim prompts and set per-feature budgets, so a popular AI feature doesn't erase your margin. You see spend against usage on a dashboard, not on a surprise invoice.
Twenty minutes on your current setup, pain points, compliance needs and target regions.
A written review of architecture, security and spend, with fixes ranked by risk and effort.
Terraform, CI/CD and observability delivered in stages, each with a rollback plan.
Runbooks and walkthroughs for your team, or we stay on to operate it with you.
Templates, broadcast campaigns, inbox, chatbots and payments on the official WhatsApp Business API — sending at scale for real brands.
Daily Google and YouTube rank tracking with trends, streaks and achievements that keep marketing teams moving.
Prove value on one high-impact workflow before you commit further.
Design and ship a complete AI-native product, ready for launch.
A senior team embedded with yours, working in your time zone.
Usually the one your team, customers and contracts already point to. If you are choosing fresh, we compare the managed services you need, AI model availability, region coverage for your users and data rules, and cost at your expected usage. We write the comparison down and recommend one, but the infrastructure is code, so it stays portable.
Often, yes. AWS, Azure and Google Cloud run regions in many countries, but which services and AI models are available varies by provider and region. We check availability for your exact stack first. Where a hosted model isn't offered in-region, we can serve an open-weight model on infrastructure inside that region instead.
LLMOps is the operational side of running AI in production: versioning prompts and models, running evaluations before releases, tracing model calls, and watching quality, latency and cost. If AI features are customer-facing or touch real decisions, you need at least the basics, or a model update can quietly change answers without anyone noticing.
We start by making spend visible per service and per customer, then work through savings in order of effort: idle and oversized resources, storage tiers, autoscaling, committed-use pricing and architecture changes. For AI workloads we add model routing, caching and budgets. We won't promise a percentage before seeing your bill, but you get a ranked list of options.
Certification comes from an independent auditor, and no vendor can grant it. What we do is build to support your SOC 2 / ISO 27001 audits: infrastructure as code, change history through code review, access controls, encryption, logging, tested backups and documentation that shows auditors how it all works.
Yes. After the initial work, you can take over with our runbooks, or keep us on through a dedicated AI pod on a monthly retainer that covers infrastructure, deployments and incident response alongside product work. Every engagement includes a daily overlap with your team's working hours.
Book a free 20-minute scoping call. Tell us what you run and where your data must live, and we'll suggest where to start.
