Independent Tech Review

Discuss your case

Independent technical review

Your AI system already works — or almost works.

I help you understand what is actually happening in the system: where risks and bottlenecks are, and what should be fixed before scaling or before incidents become expensive.

Discuss your case
  • AI
  • RAG
  • Agents
  • Infrastructure
  • Security

When an independent expert is useful

Before production

The architecture looks usable, but open questions remain:

  • where the real failure points are;
  • what happens under higher load;
  • whether observability is sufficient;
  • how access and trust boundaries are organized;
  • which dependencies can become a single point of failure.

System works unstable with unknown causes

Symptoms exist, causes are not clear:

  • periodic errors;
  • unstable latency;
  • unpredictable AI-agent behavior;
  • non-reproducible RAG results;
  • sudden increase in operating cost.

Conflicting explanations

Integrator says one thing, developers say another, infrastructure says a third. You need proof of what is true.

  • what exactly happened in the system;
  • which statements are evidence-based;
  • which findings remain hypotheses;
  • what should be fixed first.

High-stakes rollouts and budget decisions

Before large spending on AI platforms, RAG, agent systems, or enterprise integration:

  • check missing architecture elements;
  • map risks that can inflate failure costs;
  • identify limits that make the project vendor-dependent;
  • list checks required before scaling.

What I review

Architecture

End-to-end review of system structure: model layer, retrieval pipeline, agent orchestration, integrations, data flow, and infrastructure dependencies.

Security

Review of trust boundaries, authn/authz, component access to data/tools, isolation, and potential abuse or attack paths.

Production Readiness

Assessment of observability, logging, failure handling, retry/timeout behavior, and ability to investigate incidents in operation.

Incident Investigation / Reliability

Build a traceable chain: symptom → evidence → cause → remediation with prioritized outcomes.

Investigation approach

  1. Initial review: capture system context and define what is observable.
  2. Technical investigation: review available architecture docs, logs, telemetry, and integration traces.
  3. Findings: list confirmed evidence and keep hypotheses explicit.
  4. Recommendations: produce prioritized remediation and follow-up checks.

Selected cases

Intermittent MySQL failures in a hotel booking platform

Symptom: periodic database outages with unclear trigger.

Finding: expanded MySQL logging and timeline correlation exposed suspicious external queries outside expected post-auth flow.

Remediation: isolated the affected perimeter, enabled stricter telemetry, and introduced allowlist access restrictions.

Outcome: access controls were restricted through allowlist; the failures stopped.

Incident investigation · Root cause analysis · Database security

Network saturation from security monitoring

Symptom: slowdowns and delays when using corporate storage.

Finding: security tooling traffic was competing with user traffic and saturating interfaces.

Remediation: traffic shaping / bandwidth limiting for security tooling on the routing layer.

Outcome: bandwidth limits were introduced on the routing layer; storage usability was restored.

Performance investigation · Network · Security tooling

Application-driven SMB session accumulation

Symptom: persistent degradation while users worked with shared storage.

Finding: client app opened SMB sessions and did not close them correctly, causing resource accumulation.

Remediation: temporary workaround on clients and recommendation to replace the application.

Outcome: temporary and long-term remediation was separated.

Root cause analysis · App + Infrastructure interaction · Storage performance

Making “random” production failures observable

Symptom: periodic incidents appearing non-reproducible and “random.”

Finding: lack of independent telemetry prevented reliable reconstruction of event sequence.

Remediation: additional external logging and diagnostic telemetry were introduced.

Outcome: external diagnostics increased clarity and enabled incident timeline reconstruction.

Observability · Incident investigation · Diagnostics

What you get

  • a technical picture of the system and concrete priorities;
  • confirmed findings with clearly marked hypotheses;
  • risk list and constraints;
  • root-cause analysis where evidence permits it;
  • a prioritized remediation plan;
  • questions and checks that still need completion.

About independence

The review is based on production systems work, incident investigation, and infrastructure debugging. I do not promote a chosen stack, vendor, or integrator. The objective is to establish technical reality, even if it contradicts the first project assumption.

Cases show the pattern repeatedly: symptoms are the beginning of investigation, not the final result.

Describe your system or problem

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