Cloud Engineering

Every Aightifier on your engagement has 6+ years of production experience. We don't use staff augmentation, junior developers, or offshore teams. Each engagement is staffed by 2-6 senior engineers with a dedicated engagement lead, and we add specialists as the work demands.

We charge a flat monthly fee tied to the team size and engagement length. Typical engagements start at $45k/month for a two-engineer pod and scale based on scope. Pricing is benchmarked against top-tier consultancies and reviewed annually. We share OKRs and back them with skin in the game.

Our average engagement is 28 months. The shortest engagements we accept are two-week paid pilots, and the longest client relationships have spanned seven years. We optimise for continuity, not headcount, which is why retention is our primary metric.

Yes. We sign mutual NDAs before discovery calls and execute enterprise DPAs as part of contracting. For regulated clients (HIPAA, GDPR, CCPA), we will work with your legal team to align on data residency, sub-processor lists, and breach notification timelines.

We are SOC 2 Type II certified, with annual audits by an independent firm. Our engineers are trained on secure-by-default patterns, and our delivery process embeds security reviews at every stage. For FedRAMP-bound workloads, we operate in GovCloud and maintain continuous monitoring controls.

Yes — and we prefer it. The most successful engagements pair our engineers with your engineers on shared goals. We use your tooling where it makes sense, document our decisions in your wikis, and aim to leave your team stronger than we found them.

Every shipped system comes with a two-year defect warranty at no additional cost. After that, you can keep us on for managed services, hand off to your internal team with a knowledge-transfer plan, or a mix of both. We never hold systems hostage.

We do, with one caveat: we are not the cheapest option, and we are not the right fit for pre-product-market-fit teams. We typically engage with startups that have found traction and need to scale production-grade systems without growing an internal team too quickly.

Our AI practice is built around evaluation, observability, and guardrails. Every AI system we ship has an evaluation harness in CI, observability dashboards in production, and explicit guardrails for safety and compliance. We treat AI as a system component, not magic.