Production AI reliability

Your AI product is working.
Now it needs to survive real customers.

AlanOps finds the production risks, resolves the difficult technical decisions, and helps your team ship an AI system customers can trust.

Request a focused review See the outcomes

Reviewed and led personally by Alan Son.

20+years in production systems
AMPDco-founder and CTO
CarBuddy AICTO
Portableyour code and data stay yours

The scaling signal

The demo succeeded. Production is exposing the real product.

Once customers depend on the system, model quality is only one part of reliability. Data paths, integrations, fallbacks, observability, security, and release discipline all become customer experience.

01

Load reveals hidden failure modes

Queues back up, services time out, and small assumptions become incidents when real usage arrives.

02

AI behaviour is hard to diagnose

Teams can see that an answer failed, but cannot trace the model, prompt, retrieval, data, or integration that caused it.

03

Shipping starts to feel dangerous

Every release carries unknown risk because tests, evaluation, rollback, and operational ownership are incomplete.

What changes

A production system your team can explain, operate, and improve.

The goal is not more infrastructure. It is a clear reliability model around the customer journeys that matter most.

  1. 01 Critical journey and failure-mode map
  2. 02 Evaluation, testing, and release controls
  3. 03 Observability across models, data, and services
  4. 04 Prioritised reliability plan with accountable owners

How we work

Start with the risks that can damage trust.

I review the live system, customer-critical workflows, and delivery process. You get a direct view of what matters now, what can wait, and what AlanOps can resolve with your team.

01

Map

Identify the customer journeys, dependencies, and promises the product must keep.

02

Test

Stress the risky assumptions across models, data, infrastructure, and operations.

03

Prioritise

Turn findings into a sequenced plan tied to customer and commercial impact.

04

Harden

Implement the controls, fixes, and operating model with your team.

Operator proof

Advice from someone accountable for live AI products.

I do this work while building and operating commercial products. The recommendations have to survive real users, real constraints, and real production systems.

01 / AMPDCo-founder and CTO

AI citation intelligence across the discovery journey.

Product strategy, data systems, platform architecture, and production delivery.

Visit AMPD
02 / CarBuddy AICTO

Conversational AI connected to dealership operations.

AI workflows, vertical SaaS, customer communication, and reliable commercial systems.

Visit CarBuddy AI

Useful questions

Clear boundaries make better engagements.

Do you replace the existing engineering team?

No. I work with the team that knows the product, provide senior technical judgement, and add focused delivery capacity where it is useful.

Is this only for generative AI products?

No. The same approach works for agentic workflows, predictive systems, retrieval products, and AI features inside a wider SaaS platform.

Can AlanOps implement the recommendations?

Yes. The review can stand alone, or AlanOps can lead the hardening work and remain accountable through production.

Related situations

AI launch readiness Launch the product without gambling customer trust. Explore this engagement → Reliability after rapid growth Demand found the scaling wall. Turn it into an advantage. Explore this engagement → Managed hosting and operations Keep ownership of the product. Hand off the operational burden. Explore this engagement →

Start with the current truth

What would a useful first conversation need to resolve?

Share the situation, the pressure, and what has already been tried. I will review it personally and tell you plainly whether AlanOps is the right fit.

  • No generic sales sequence
  • No obligation to commission delivery
  • A direct response from Alan

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