A good model but poor retrieval.
AI Architecture Audit
Find what's actually broken before you build more.
You have an AI system. Or you’re about to build one. Something isn’t working—or you’re not confident the architecture you’re about to commit to is right. We examine the system, identify technical risks and bottlenecks, and give you a concrete path forward. No pitch deck. No forced rebuild. Just technical judgment.
Why an audit?
The visible failure isn't always the actual failure.
AI systems can fail at layers that aren’t obvious from the interface. We look underneath it.
Good retrieval but bad context construction.
Good prompts but weak evaluation.
Good models but excessive infrastructure cost.
Good components but poor orchestration.
A working prototype but an architecture that won’t survive production.
What we audit
Seven layers of technical inspection.
System Architecture
How the pieces fit togetherComponent boundaries · Service architecture · Data flow · Model placement · Agent orchestration · Dependencies · Scalability · Failure boundaries
Is the architecture solving the problem—or just connecting components together?Model Strategy
Why these models?Model selection · Model routing · Prompt architecture · Context windows · Fine-tuning · Training strategy · Model redundancy · Latency · Cost · Performance
Is the model actually the right layer to solve this problem?Data & Knowledge
What does the system know, and how does it access it?Data sources · Data quality · Transformation · Chunking · Embeddings · Retrieval · Knowledge structures · Freshness · Metadata · Memory
Does the system have access to the right information at the right time?Infrastructure
What does it actually cost to run?Compute · Storage · Databases · Model hosting · APIs · Networking · Scaling · Deployment · Observability
Can this architecture operate reliably at the expected scale and cost?Evaluation
How do you know it works?Evaluation datasets · Metrics · Retrieval evaluation · Model evaluation · Agent evaluation · Regression testing · Human evaluation · Production feedback
What evidence supports the claim that the system works?Failure Modes
What happens when it doesn't?Hallucination · Retrieval failure · Model failure · Tool failure · Agent loops · Data failure · API failure · Timeout behavior · Recovery · Fallbacks
What happens when the system is wrong?Cost, Risk & Efficiency
Where is the system leaking value?Token usage · Model spend · Infrastructure cost · Latency · Redundant processing · Unnecessary model calls · Scaling assumptions · Vendor dependencies · Security concerns · Operational risk
What will become expensive, fragile, or risky as the system grows?Audit framework
The system before the recommendation.
AI SYSTEM
DATAMODELARCHITECTURE
INTELLIGENCEEVALUATIONINFRASTRUCTUREFAILURE MODES
COST + RISKRECOMMENDATIONSWhat we actually look for
Inspection at the layer where it matters.
Data Flow
Where does information enter? How is it transformed, stored and assembled into model context? Where can information be lost?
Models
Why this model for this problem? Is routing appropriate, fine-tuning justified and the context strategy sound?
Retrieval
What gets retrieved, why, how is relevance measured and what happens when retrieval fails?
Agents
What decisions are autonomous, what requires deterministic control and what happens when an agent is wrong?
Infrastructure
What happens under load, where are the bottlenecks and what infrastructure is unnecessary?
Evaluation
What does good mean? Is there a test set, failure tracking, regression detection and human judgment where needed?
What you receive
Evidence, maps and a path forward.
Audit Report
A structured technical assessment of the system.
Architecture Map
A visual representation of how the system currently works.
Risk Cards
Prioritized technical risks with severity and impact.
Model Assessment
What is working, what isn't, and what should change.
Data Strategy Assessment
Evaluation of data quality, structure, retrieval and knowledge architecture.
Infrastructure Assessment
Cost, scaling, deployment and operational concerns.
Cost Analysis
Where compute and model costs originate and where optimization may exist.
Prioritized Roadmap
Fix now · Fix next · Monitor · Don’t touch.
Five questions
From inspection to the next decision.
Audit decision path
01What is working?→02What is broken?→03Why is it broken?→04What should change?→05What should happen next?
What this is not
A sales demo with a diagnostic label.
Not a Sales Demo
You aren’t paying for a product pitch.
Not a Generic AI Consultation
The assessment is based on your actual system.
Not a Guaranteed Rebuild
An audit may conclude that your existing architecture is sufficient.
Not a Benchmark Report
We assess the system as a whole—not just model scores.
Not a Promise to Fix Everything
The purpose is to establish what actually needs fixing.
Who it's for
Technical confidence before the next commitment.
Founders
You’ve built an AI product and need technical confidence before scaling.
CTOs / Engineering Leads
You want an independent assessment of architectural decisions.
Product Teams
You’re unsure whether the next investment should be another feature or an architectural change.
Teams with a Stalled AI Initiative
You’ve tried several approaches and still don’t have a reliable system.
Teams Before a Major Build
You want technical judgment before committing significant engineering resources.
YOU PROBABLY DON'T NEED ONE IF
You have a tiny prototype, a well-understood problem, a simple API integration, or no meaningful architectural decisions yet.
YOU PROBABLY SHOULD CONSIDER ONE IF
Costs are growing unexpectedly, performance is inconsistent, components are difficult to manage, retrieval is unreliable, agents are unpredictable, or a major architecture investment is next.
How the audit works
An inspection, then a decision.
Architecture audit engagement flow
01Submit→02Scope→03Inspect→04Report→05Review→06Decide
The next step may be with us—or without us.
Architecture audit
Know something is wrong, but not where?
Don't spend another month changing models, rewriting prompts or adding infrastructure without knowing the actual bottleneck.
Request an Architecture Audit →Talk Through Your System →Start wherever you are.
Tell us what you are building, what you have tried, or what is not working. We will help identify the actual problem.
Or email us directly at genjecx@gmail.com