LegalAI

Enterprise-grade ambition on a startup budget

What to do when you need a DevOps pro, but cannot afford a full-time hire, and don't have enough workload? And even if you could, they'd be forced to wear too many hats, because your business requires a compliant setup now, while your team is focused on shipping.

team work

With a small engineering team and a mission to land enterprise clients, LegalAI faced a classic startup dilemma.

Building a secure, scalable infrastructure was non-negotiable, but hiring a full-time DevOps expert was over budget and would divert focus from the core product.

Quick facts

LegalAI

AI-Powered LegalTech Platform

Our client operates an AI-driven platform that streamlines legal case preparation for users in the USA. As a startup, they began their journey 8 years ago, focusing 100% on product development to quickly win over enterprise clients.

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-28% Expenses

Right‑sizing and autoscaling done right

By tuning requests/limits, enabling VPA where safe, and tightening node‑pool autoscaling, we cut idle capacity and bin‑pack workloads. We achieved a 28% reduction in cluster spend while maintaining SLOs and deployment velocity.

k8s cost optimization strategies

The best couple Terraform + GCP

The initial system was manual and not hardened. We built their foundational infrastructure on GCP by utilizing an infrastructure-as-code approach — a strategic recommendation that saved the client time on disaster recovery.

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"I don't necessarily need new features. I want reliable, dependable systems. It's that predictability factor. The main thing you just want is how deeply you can sleep at night."

James Jo

CTO, LegalAI

What we did for LegalAI

Effort Distribution

“How do you manage an end-to-end infrastructure lifecycle?”

For LegalAI, the journey from a manual process to an enterprise-grade platform required a structured, end-to-end approach. We managed the entire lifecycle, from initial architectural design and strategic tech selection to long-term operational management and strategic guidance.

  1. Strategic Architectural Design. The engagement began with architectural design. We consulted the client and helped them choose the right hosting provider, technologies, and processes. The results of our work include diagrams of the future project, a roadmap, a step-by-step implementation plan, and guidance.
  2. Infrastructure Provisioning Automation. According to the plan, we configured the environments and automated the development processes. Execution involved: setting up a secured landing zone in GCP with the heart of the infrastructure — Kubernetes; decoding of the infrastructure with Terraform; and configuring CI/CD pipeline using GitLab self-hosted runners.
  3. Comprehensive Monitoring. With the platform operational, full observability was established as well. We utilized Prometheus, Grafana, and Loki to create the deep visibility required not only to manage system health and performance, but to predict failures.
  4. Ongoing Partnership and Support. The platform's reliability is ensured by our 24/7 SRE support, giving the CTO peace of mind. This extends to a strategic partnership, where we provide what the client calls "a consulting value that doesn't allow us to make errors" by pushing back on risky ideas and proactively suggesting architectural improvements.
Grafana dashboard

Compliance readiness and business continuity

It means your security controls are not just theoretical but are automated, auditable, and continuously monitored. That's why we treat compliance as an engineering problem, not a paperwork exercise.

Our process involves mapping your infrastructure and CI/CD controls to the SOC 2 Common Criteria and automating the collection of evidence—like configurations and runbooks—from version control. This results in a continuously updated evidence catalog ready for auditors, with no last-minute manual effort.

We eliminate standing admin privileges entirely and replace them with a Just-in-Time (JIT) access model. This ensures no one has persistent, high-level access to production environments.

Access is granted for a time-bound window, requires approval, and is programmatically tied to a specific change or incident ID. All elevation events are logged for review, resulting in zero persistent production admins and a fully auditable access trail.

By building automated quality gates directly into the CI/CD pipeline. No deployment can be promoted to the next environment without passing a series of automated, non-negotiable checks.

Our GitLab pipelines integrate unit and integration tests, container vulnerability scanning, and post-deployment smoke tests. A "green check" is required at each stage before code can move from development to staging, and finally to production, ensuring a change failure rate of less than 10%.

No. A backup is a component; a DR plan is a documented, tested, and proven process for restoring service within a specific timeframe.

Our process includes defining clear Recovery Point Objectives (RPO) and Recovery Time Objectives (RTO). We use Terraform to define the infrastructure re-provisioning process as code and conduct semi-annual DR drills to validate the entire plan. This ensures we can meet our RTO of 2 hours and provides a clear, actionable playbook for any critical failure event.

Through a combination of proactive governance and continuous optimization. We establish a FinOps practice from day one to ensure costs remain predictable and aligned with business value.

This involves setting budgets with automated alerts, implementing resource labeling for chargeback, and tuning GKE node pools and autoscaling configurations. A monthly FinOps review allows us to rightsize resources and plan for reservations where applicable, resulting in 12-20% cost avoidance versus forecast and a stable spending profile.

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