Question 1

What is AI-native SRE and how is it different from traditional SRE?

Accepted Answer

Traditional SRE was designed for conventional web applications. AI-native SRE starts from a different premise. AI workloads behave differently. Models drift in ways traditional monitoring cannot detect. GPU costs are variable. LLM pipelines have latency characteristics standard observability tools were not built to surface. The practice, tooling, runbooks, and SLO definitions all need to be designed around how AI products actually behave in production. That is the only kind Coneixedor builds.

Question 2

Our startup is early-stage. Is SRE consulting relevant to us?

Accepted Answer

It depends on one thing: do you have users who depend on your product working? If yes, SRE thinking is relevant. Not enterprise-scale SRE with a 20-person team. The foundational decisions: how you define reliability, how you handle incidents. These matter from the moment real users depend on you. The first call is free. A senior partner will tell you honestly whether you need this now or whether you can wait six months.

Question 3

What does a typical engagement look like?

Accepted Answer

Three phases. Phase 1: The Reality Check. A focused 30-minute call with a senior partner. You leave with a clear read on your top reliability risks and what fixing them properly looks like. Free. Phase 2: The roadmap. Tailored infrastructure plan. Every decision explained before anything is built. Phase 3: The build and run. Fixed-scope project, ongoing managed service, or combination. The engagement ends when your team fully owns what was built. Documentation, runbooks, and handover are part of every engagement.

Question 4

Who takes the Reality Check call?

Accepted Answer

A senior partner. The partner who takes your first call is the partner who stays accountable for your infrastructure through the entire engagement. You are never handed to a junior engineer or a sales rep.

Question 5

Do you charge by the hour or by team size?

Accepted Answer

Our SRE consulting pricing is outcome-based, not billed by the hour or by team size. During your free Infrastructure Reality Check and the audit that follows, we surface the real gaps in your infrastructure, walk through them with you, and agree on what's actually worth fixing. From there, we typically price to closing those specific gaps, not to hours logged or engineers assigned. Every AI startup's infrastructure needs are different, so the engagement can flex too: some teams want a fixed-scope build, others prefer ongoing reliability support that scales as they grow. What never changes is the principle behind it: you pay for the outcome, not for headcount.

Question 6

We already have engineers. Why outside SRE help?

Accepted Answer

Having great engineers is not the same as having an SRE practice. SRE is a specific discipline. Most startup engineers are hired to build product. SRE requires a different skill set: designing for failure, defining reliability standards, building runbooks, managing on-call sustainably. Coneixedor does not replace your team. We work alongside them to build the practice they do not have time to build themselves.

Question 7

Do you only work with startups or larger companies too?

Accepted Answer

Coneixedor is built specifically for funded startups and growth-stage companies. Typically seed to Series B, 5–80 person engineering teams, products live and growing. We do not work with enterprises. If you are a startup that has outgrown the infrastructure you started with, you are exactly who Coneixedor was built for.

Question 8

Which platforms do you support?

Accepted Answer

AWS, Azure, GCP for cloud. Kubernetes and ECS for containers. Terraform and Ansible for IaC. GitHub Actions, GitLab CI, CircleCI for pipelines. Datadog, Prometheus, Grafana for observability. PagerDuty for incidents. Vertex AI, MLflow, Feast for MLOps. Evidently AI for drift detection. If your stack includes something not on this list, tell us on the first call.

Question 9

How do you handle MLOps and AI infrastructure reliability specifically?

Accepted Answer

Coneixedor builds end-to-end ML pipelines including automated training, model versioning, drift detection, and scalable serving. We also build LLM-specific observability and GPU cost management. If your models work in development but underperform, drift, or cost too much in production, that is specifically the problem this practice is built to solve.

Question 10

What does it cost?

Accepted Answer

We do not publish fixed pricing because the right engagement varies significantly. Every engagement is designed to cost less than the alternative: hiring a senior SRE full-time (USD 200k–300k/year in the US, 6-month hiring process), or waiting until something breaks and paying the cost of the incident, lost contract, or failed fundraise. The first step is always the free Infrastructure Reality Check.

Question 11

Do you work with India-based startups or only US and UK?

Accepted Answer

Both. Coneixedor works with funded startups across India, the US, the UK, and Europe. For India-based startups the engagement model is exactly the same. For US and UK startups the work happens remotely with overlap hours around your timezone.

Question 12

How is Coneixedor different from hiring a freelancer?

Accepted Answer

Three differences. First: outcome ownership. A freelancer completes tasks. Coneixedor owns outcomes. Second: senior depth. Every engagement pairs a senior partner's accountability with engineers who specialise in exactly your infrastructure challenge. Third: direct accountability. A senior partner owns your outcome from first call to last.

Question 13

What happens if something goes wrong during an engagement?

Accepted Answer

Your senior partner is the first call. Not a support email. Not a junior escalation path. Your senior partner, directly. This is the accountability promise on every engagement. Large consultancies have layers between you and the person who can fix the problem. Coneixedor has one layer: a senior partner owns your outcome.

Question 14

We are looking at AI SRE tools like autonomous incident management platforms. Do we need a consulting engagement as well?

Accepted Answer

If you already have an SRE practice, an AI SRE tool can make it faster. But every one requires something to already exist: SLOs defined. Observability instrumented. Runbooks written. Alerting configured. If those foundations do not exist, an AI tool has nothing to connect to. Coneixedor builds the foundation. AI SRE tools make that foundation faster to operate. Build the practice first, then automate it.

Questions founders ask before the first call.