Merino Advisory / Singapore

Operational clarity for systems that cannot fail quietly.

Strategy, resilience, and applied AI—turned into working decisions, controls, and habits.

Singapore-based boutique advisory helping technology and operations leaders turn reliability, risk, regulatory obligations, and AI adoption into concrete operating models, practical playbooks, and measurable control systems.

We work at the intersection of governance, engineering execution, and operational readiness so teams can scale without overbuilding.

Discuss an engagement Explore the labs LinkedIn
  • Operating model design
  • Reliability and resilience
  • AI R&D and AI-assisted operations
  • Organisation and capability uplift

Practice / 01—03

Where the work concentrates

Decision-ready operating models for high-consequence platforms.

  • Governance that holds up in audits
  • Reliability systems that scale with risk
  • AI R&D with evaluation and guardrails

How we engage

Advisory sprints, operating model builds, workshops, training, and ongoing support.

Advisory services

Merino Advisory provides management consulting and advisory services for organisations running mission-critical or high-consequence platforms where outages, incidents, security events, or compliance gaps create material operational and reputational risk.

Strategy and Operating Model

Design operating models that match risk appetite and business objectives without creating unnecessary process or organisational overhead.

Typical outcomes

  • Current-state assessment (operating model, governance, reliability posture, delivery constraints)
  • Target Operating Model (TOM): decision rights, accountability, forums, escalation paths
  • Metrics and management system: SLOs/SLIs, reliability KPIs, risk indicators, operating cadence
  • Practical governance: lightweight controls that are auditable and actually used

Typical deliverables

  • Operating model blueprint (roles, interfaces, decision rights, RACI)
  • Service ownership model and service catalog structure
  • Reliability and risk KPI framework (with definitions and reporting cadence)

Reliability and Resilience Advisory

Build the core reliability system that prevents incidents where possible and reduces impact when they occur.

Typical outcomes

  • Incident management playbooks (severity model, triage, comms, post-incident learning)
  • SRE and platform reliability model aligned to your org size and regulatory posture
  • Readiness for audits and obligations through evidence-friendly processes
  • Resilience planning (BCP/DR alignment, testing approach, failure-mode thinking)

Typical deliverables

  • Incident Response Playbook and templates (timeline, roles, comms, PIR structure)
  • SLO program blueprint (how to define, operationalise, and govern SLOs)
  • Reliability review cadence (weekly/monthly ops reviews with standard agenda)

AI R&D + AI-Assisted Operations

Run applied AI R&D to reduce operational toil and improve decision quality, then harden it for real production constraints: governance, traceability, and on-call.

Typical outcomes

  • R&D framing: hypotheses, constraints, and success metrics
  • AI-ready operational knowledge base (runbooks, controls, definitions, escalation logic)
  • AI-assisted triage patterns (human-in-the-loop, deterministic outputs where required)
  • Evaluation and quality gates (pass/fail criteria, regression checks, rollout safety)
  • Governance for AI usage in ops (quality, traceability, and change management)

Typical deliverables

  • AI-assisted incident triage operating pattern (roles, workflow, guardrails)
  • Evaluation plan + acceptance criteria checklist (measurable, repeatable)
  • Knowledge architecture (taxonomy, runbook structure, ownership model)
  • AI usage policy for operations teams (what is allowed, reviewed, and logged)

Organisation, Talent and Training

Build capability, not dependency. Define the roles, hiring sequence, and training paths required to operate high-stakes systems.

Typical outcomes

  • Role archetypes (platform, SRE, security, ops, product) mapped to responsibilities
  • Hiring sequence and capability roadmap (what to hire first, and why)
  • Training pathways tied to operational standards (incident practice, reliability design)
  • Practical onboarding and continuous learning program

Typical deliverables

  • Talent map: role taxonomy, gaps, sequencing plan
  • Training curriculum template (90-day onboarding and continuous training)
  • Interview scorecards and role definitions (optional)

Consulting approach

We structure engagements to produce decision-ready outputs quickly, then convert them into operating habits teams can sustain.

Phase 1

Align on risk, outcomes, and constraints

  • Stakeholder interviews and objectives
  • Risk appetite and operational constraints (regulatory, uptime, security expectations)
  • Define what good looks like (measurable outcomes)
Phase 2

Diagnose the operating system

  • How work moves: ownership, prioritisation, escalation, incident handling
  • Where ambiguity and toil live (handoffs, unclear controls, missing runbooks)
  • Baseline for reliability metrics and operational cadence
Phase 3

Design the target model and playbooks

  • Operating model (decision rights, forums, responsibilities)
  • Core playbooks (incident response, change management, reliability reviews)
  • Metrics and evidence patterns suitable for internal governance and audits
Phase 4

Implementation support and capability transfer

  • Pilot the model with real workflows (incidents, releases, reviews)
  • Train managers and on-call teams
  • Hand-over: templates, training material, and governance cadence
We can deliver as advisory sprints (2-6 weeks), operating model builds (6-12 weeks), workshops and training, or ongoing advisory support, subject to availability.

AI R&D + AI-assisted operations, built for auditability and real operations

We run applied AI R&D like engineering work: define a baseline, prototype thin slices, measure outcomes, then ship with the guardrails that keep it reliable under real operational pressure.

AI R&D themes (generic across industries)

  • Operational knowledge systems: turn runbooks, controls, definitions, and escalation logic into answerable, owned knowledge.
  • Incident intelligence: triage assistance, summarisation, and comms drafting with human approval gates.
  • Workflow automation: reduce operational toil through repeatable, logged workflows and clear quality gates.
  • Evaluation and regression: test sets, pass/fail checks, and monitoring so changes are measurable.
  • Governance and evidence: traceability, access control, and audit-friendly decision records.

How we run AI R&D

  1. Define the problem, success metrics, and constraints (risk, privacy, audit needs).
  2. Map data and knowledge sources (what is authoritative, who owns it, how it changes).
  3. Prototype thin slices to validate feasibility and workflow fit with real operators.
  4. Build an evaluation harness (golden set, pass/fail gates, regression checks).
  5. Roll out safely (shadow mode, human-in-the-loop, staged promotion).
  6. Operationalise (runbooks, monitoring, incident playbooks, governance cadence).

AI R&D is only valuable if it survives on-call and governance: measurable quality, clear ownership, and audit-ready workflows.

Open research notebook

Experiments

Interactive labs and presentation notes that make technical risks visible, testable, and easier to discuss.

LAB / 01
Security engineering · Interactive lab + presentation

When 128 bits are only 41.3

A deliberately vulnerable Bitcoin testnet wallet shows how operational knowledge can collapse a random-looking output into a small, searchable family of states.

Read the field note

View all experiments →

Resources and R&D notes

We are publishing short, practical notes and templates for technology and operations leaders across organisations of all sizes. These are designed to be usable without hiring a consultant, and will be iterated and expanded over time.

AI evaluation checklist (operational use-cases)

A practical checklist to make operational AI measurable, safe to iterate, and ready for production workflows.

  • Define success metrics and failure modes
  • Golden test set and regression checks
  • Human override, escalation triggers, and audit trail
  • Monitoring signals for drift and degraded quality

Reference design: AI-ready operational knowledge base

A reference structure for turning runbooks and operational definitions into a governed, usable knowledge system.

  • Runbook structure, taxonomy, and ownership model
  • Freshness and change control (what updates when, and who approves)
  • Retrieval patterns and source attribution
  • Quality gates for edits and generated outputs

Playbook skeleton: incident response with AI-assisted triage

A practical playbook template that integrates AI responsibly into incident triage without replacing human accountability.

  • Roles and responsibilities (incident commander, comms, technical lead)
  • AI usage guardrails (what it can draft vs. what must be verified)
  • Triage workflow and escalation triggers
  • Post-incident learning template

Template: operational AI risk register

A lightweight template to capture risks, mitigations, and controls for operational AI workflows.

  • Wrong or misleading outputs and mitigations
  • Access control, data leakage risk, and confidentiality
  • Change management and approval gates
  • Evidence and logging expectations

Talent map for reliability and platform teams

A sequencing guide for building a platform and SRE capability over time.

  • Role archetypes (platform, SRE, ops, security, product reliability)
  • Hiring sequence based on maturity and risk profile
  • Common anti-patterns (over-hiring specialists too early, unclear ownership)

Template: training path for reliability and incident management

A training path template for engineers and operators to build reliability capability.

  • 30/60/90 day onboarding for on-call readiness
  • Incident simulation structure and cadence
  • Runbook standards and knowledge ownership

Why AI prototypes fail without requirements and architecture

A delivery-focused note on why AI-driven development often stalls and what to do instead.

  • Common failure modes (undefined requirements, missing constraints, unclear ownership)
  • A requirements-first workflow that still leverages AI effectively
  • Template: minimum viable architecture note and acceptance criteria checklist

Reliability-first operating model checklist

A governance and accountability checklist for platforms where incidents or control failures are material.

  • Service ownership and escalation clarity
  • SLO and error budget program readiness
  • Incident lifecycle standards (triage to communications to learning)
  • Evidence patterns for internal governance and audits

Governance and Professional Standards

We operate like a professional services firm without inventing client claims or references.

What you can expect

  • We maintain clear documentation standards (versioning, change logs for playbooks).
  • We use structured templates so decisions are traceable.
  • We treat confidentiality and client information handling as a baseline expectation.
  • We aim for practical, auditable operating routines, not slideware.

Connect on LinkedIn

Follow Daniel Merino on LinkedIn for reference materials, R&D notes, and advisory updates.

View LinkedIn profile

This link opens Daniel Merino's LinkedIn profile in a new tab.

About Merino Advisory

Merino Advisory Pte. Ltd. is a Singapore-based professional services firm providing management consulting focused on operating models, resilience, and AI R&D + AI-assisted operations for high-stakes digital platforms.

Company

We publish open frameworks, templates, and R&D notes alongside advisory work for organisations improving operating models, resilience, and AI-assisted operations.

Founder

Daniel Merino - Management Consultant

Daniel has spent the last decade working with leadership teams on operating models and execution for high-consequence platforms where reliability, risk, and operational discipline are business-critical.

His work typically sits at the intersection of platform operations, governance, and capability building: making complex systems manageable through clear accountability, measurable standards, and repeatable playbooks.

Contact

For advisory discussions, partnership conversations, or feedback on the resource library, please reach out.

Email: dan@merinoadvisory.com