Solution Architecture Review

Overview

A Solution Architecture Review gives you an independent, honest verdict on whether a proposed solution architecture is ready for production. Our consultants assess it before you commit further investment, so issues surface while they are still cheap to fix. That includes whether your architecture can support the Data and AI workloads you are planning.

What we look at

Target-state architecture

The shape of the solution you are proposing to build.

Integration and failure handling

How systems connect, and what happens when something fails.

Scalability

Performance against current and projected volumes.

Foundations

Environments, CI/CD, observability, security and access.

Non-functional requirements

Performance, resilience, security and the qualities that make a build production-ready.

AI and machine learning readiness

Data pipelines, data quality, and how AI components are secured and monitored.

Supporting documentation

Whether a new engineer could rely on it from day one, including AI-generated docs or code.

Outcomes you can expect

A confident investment decision

You know whether to proceed, adjust or stop before committing more budget.

Less rework

Structural issues are caught at design stage rather than after go-live.

A platform that scales

Your architecture is tested against the volumes you expect, not just today.

AI that has solid foundations

Data, integration and security gaps are found before you build AI on top of them.

Documentation your team can rely on

New engineers can get productive without relying on tribal knowledge.

What is the proof?

A few examples of where an Advisory engagement has made a difference.

From Access database to cloud lakehouse

How Avocado migrated an Australian packaging manufacturer from Microsoft Access to a cloud lakehouse, with consistent QA records and automated reporting.

Read the case study

A governed, in-house alternative to Copilot

How Avocado built a private AI assistant for an Australian economics consultancy, with governance, access controls and built-in consulting workflows.

Read the case study

Laying the Foundation for Quality Assurance

Avocado designed an enterprise testing framework and QA strategy for a state-owned water utility, with reusable artefacts, governance and full capability handover.

Read the case study

Taking the risk out of certificate renewals for a major public sector agency

Avocado Consulting and 2Steps automated certificate retrieval, installation and verification for a large Australian public sector agency.

Read the case study

Agentic AI

Voice AI Case Study: Momento Hospitality

See how a voice AI agent we built cut staff-handled calls by 75% and drove US$236K in monthly bookings across eight Sydney venues.

Read the case study

A national sports website rebuilt in four months, ready for the season

Avocado helped a subsidiary of Australia's largest telecommunications company deliver the biggest upgrade in its sports website's history

Read the case study

47,000 users across five divisions moved to cloud collaboration, on schedule

Avocado helped a major bank move around 47,000 users across five divisions onto a cloud-based collaboration service, in waves of 1,500 users

Read the case study

Practice management platform rolled out to more than 700 financial planning practices

Avocado helped a leading wealth manager roll out a CRM and practice management platform to 700+ planning practices, integrated with core systems.

Read the case study

New credit card launched and card portfolio migrated, on an immovable deadline

One of Australia's big four banks engaged Avocado to deliver a critically important program over 18 months.

Read the case study

How it works

Four steps, around four to six weeks

Initial conversation

A short call to understand your platform, the decision ahead and your timeline.

Scoping and fixed quote

We agree scope, access to documentation and systems, and a single point of contact, and you receive a fixed price.

Review

Over four to six weeks, we work alongside your architecture and engineering team, reviewing design documents and assessing the areas listed above.

Findings and verdict

We present our fit-for-purpose opinion and severity-rated findings to your decision-makers, with a recommended action for each risk.

Frequently asked questions

What is a solution architecture review?

A solution architecture review, sometimes called a technical architecture review, is an independent assessment of whether a specific system or platform design is fit for purpose. It checks scalability, integration, security, resilience and documentation against your current and future needs.

When should you get an architecture review?

Before you commit significant investment to a new platform, replace a legacy system or add AI capability. Reviewing at design stage costs far less than fixing issues after go-live.

Can a Solution Architecture Review assess readiness for AI?

Yes. We assess whether your data pipelines, integration patterns, security controls and observability can support AI and machine learning workloads, and flag gaps before you build on them.

How is it different from an AWS Well-Architected Review?

A Well-Architected Review checks cloud workloads against one provider's framework. Our Solution Architecture Review is vendor-neutral and covers the whole solution, including integration, documentation and non-functional requirements.

Can a Solution Architecture Review support technical due diligence?

Yes. Investors, acquirers and boards use it as an independent technical due diligence check on a platform before funding, buying or relying on it. You get a clear verdict on whether the architecture is sound, plus the risks and remediation it would need.

What does fit with conditions mean?

Your architecture can proceed, provided specific findings are addressed first. Each condition comes with the risk it carries and a recommended action.

How long does a Solution Architecture Review take?

Most Solution Architecture Reviews take four to six weeks, depending on the size and complexity of the solution.

What we look at

Target-state architecture

Integration and failure handling

Scalability

Foundations

Non-functional requirements

AI and machine learning readiness

Supporting documentation

What you get

Outcomes you can expect

A confident investment decision

Less rework

A platform that scales

AI that has solid foundations

Documentation your team can rely on

What is the proof?

From Access database to cloud lakehouse

A governed, in-house alternative to Copilot

Laying the Foundation for Quality Assurance

Taking the risk out of certificate renewals for a major public sector agency

Voice AI Case Study: Momento Hospitality

A national sports website rebuilt in four months, ready for the season

47,000 users across five divisions moved to cloud collaboration, on schedule

Practice management platform rolled out to more than 700 financial planning practices

New credit card launched and card portfolio migrated, on an immovable deadline

Four steps, around four to six weeks

Initial conversation

Scoping and fixed quote

Review

Findings and verdict

Frequently asked questions

Where teams go next