Engineering systems diagnostic

Identify structural risks before they become scaling constraints.

We analyse system architecture across AI platforms, SaaS backends, and distributed services to surface failure modes under real-world load.

Not a code review, performance audit, or implementation inspection.

Request a diagnostic → → See pricing
24–72 hr structured delivery Architecture-level analysis
Audit in progress
0 / 5 checked

Example visualization — actual pattern recognition across systems is a structured written analysis of your system description.

Mapping typical service boundary issues

Who is it for?

Built for founders and engineers building production AI systems, SaaS backends, and distributed services.

Diagnose your system

What does your system look like?

Select the closest system archetype. We’ll surface the structural failure patterns that typically emerge at scale.

AI / agent systems — common observations
P0Prompt logic embedded in orchestration layer — changes require full redeploy and break non-obvious downstream flows
P0Tool definitions duplicated across agents — diverging behaviour that looks like a model problem but is structural
P1No clear boundary between reasoning and execution layers — makes debugging and testing nearly impossible
P1State passed through prompt context instead of a proper store — context window pressure degrades agent reliability at scale
P2Missing fallback and retry logic in tool calling flows — silent failures accumulate unnoticed in production
SaaS backends — common observations
P0Business logic scattered across API handlers, webhooks, and background jobs — impossible to reason about or test holistically
P0Synchronous calls to external services in the critical path — one slow third-party kills your p99 latency
P1Auth logic duplicated across services — inconsistent enforcement that creates security gaps at module boundaries
P1Database schema tightly coupled to API contract — any schema migration requires coordinated frontend and backend deploys
P2No clear ownership of cross-cutting concerns (logging, errors, rate limiting) — each service reinvents them differently
Data pipelines — common observations
P0No idempotency in ingestion — duplicate events cause silent data corruption that only surfaces in analytics months later
P0Transformation logic embedded in ETL scripts — untestable, unreviewable, and breaks on schema changes with no warning
P1No backfill or replay capability — any upstream fix requires manual data reconstruction
P1Shared mutable state between pipeline stages — one slow stage blocks everything downstream
P2Schema validation happens at the end of the pipeline, not the start — bad data travels far before being caught
Microservices — common observations
P0Synchronous service mesh with no circuit breaking — a single slow service cascades into full system degradation
P0Distributed transactions without saga pattern — partial failures leave data inconsistent with no recovery path
P1Services sharing a database — defeats service isolation and makes independent deploys impossible
P1API contracts versioned informally — breaking changes deployed without consumer awareness
P2Observability bolted on per-service rather than standardised — debugging cross-service flows requires stitching logs manually

Typical observations

30–60%
logic duplication found across services, modules, or agents in most reviews
≈ 70%
of AI "bad behaviour" is caused by architecture, not prompts or models
Most
scaling failures are structural — infra upgrades won't fix them
Not sure if you need a full review?
Start with a £99 snapshot. Most people upgrade after seeing the observations.
Start with a snapshot →

Scope

What Is Included

01 / System
Architecture
  • Service boundaries and coupling
  • Hidden inter-module dependencies
  • Scaling bottlenecks in design
  • Unnecessary complexity layers
02 / AI
Agents & orchestration
  • Multi-agent design issues
  • Tool calling and duplication
  • Unreliable reasoning flows
  • Prompt + system coupling
03 / Code
Critical paths
  • Core business logic flows
  • Integration failure points
  • Failure propagation paths
  • Maintainability under growth

Report structure

What you get

01
Risk breakdown
P0Critical — scalability and reliability risks demanding immediate action
P1High — maintainability and growth risks within 3–6 months
P2Medium — technical debt and design weaknesses to address next cycle
02
Root cause analysis
Why the system ended up this way — structural reasons, not just symptoms.
03
Prioritised fix plan
What to address first to reduce risk fastest, sequenced by real-world impact.
04
Target architecture direction
A simplified view of what your system should evolve into — not a rewrite, a direction.

Pricing

Simple, fixed-price tiers

Entry
£99
Quick Architecture Scan
  • Entry diagnostic (limited scope)
  • Used for rapid system screening before deeper engagement is recommended
  • Written summary report
  • Delivered in 24 hrs
Core
Full review
£199
System Architecture Diagnostic
  • All three review areas
  • P0 / P1 / P2 risk breakdown
  • Root cause + fix plan
  • Architecture direction
  • Delivered in 48–72 hrs
Deep
£500+
Architecture Strategy Engagement
  • AI agent deep analysis
  • Multi-session engagement
  • Roadmap-level guidance
  • Async Q&A included
This is not
  • Code-level audit, linting, or performance profiling
  • Vague "best practices" consulting
Price promise

If the review doesn’t surface meaningful structural risks or feels misaligned with your system complexity, You’ll get refunded — no questions asked.

  • Only applies to initial snapshot or full review
  • Based on honest assessment of system scope
  • No partial or vague “feedback only” outcomes
Get a second engineering
opinion on your system.
Email us with a brief description of your system.
We will confirm scope and timeline within 24 hours.
Request a review →