Most AI failures aren't
model failures.

[ ARCHITECTURE // EVALUATION // KNOWLEDGE // TRUST ]

They're architecture failures — systems that work in a demo, then degrade with real users, real data, real edge cases. Retrieval returns the wrong thing. Knowledge has no structure. Nobody can tell a good output from a bad one.

I design, build, and evaluate production AI systems that organizations can trust. I don't just tell teams what's wrong — I build the systems that make it right.

Start with a Readiness Review Discuss an architecture engagement
01. Diagnostic

What Usually Breaks

Most teams can get an AI system to a working demo. Fewer can tell you why it behaves differently in production — or what to change when it does.

01

Does the system behave consistently under real inputs — messy data, edge cases, and users who do not follow the happy path?

02

Can you change a model or a prompt without silently breaking something downstream?

03

When output quality drops, can you tell whether it was retrieval, context, tool selection, or the model itself?

04

Is cost per query rising faster than usage, because the architecture asks the model to do work the architecture should be doing?

05

Can you tell whether last week's change made the system better, or only that it shipped?

06

Can every material AI-generated claim be traced back to supporting evidence?

07

Can risk, compliance, or business stakeholders understand why the system produced a given result?

02. Services

Engagement Ladder

Architecture and delivery are the transaction. Trust is the outcome. Engagements progress from diagnosis to build to regulated trust work.

[ START HERE ]

Production Readiness Review

Fixed fee from $12,000. Two weeks. For AI systems already built that degrade with real users.

  • Root-cause diagnosis of where and why the system fails
  • Retrieval and knowledge-structure review
  • Evaluation gap analysis
  • Prioritised fix list with effort estimates
  • Deployment readiness recommendation
[ PRIMARY ENGAGEMENT ]

AI Architecture & Delivery

Fractional or project-scoped. Hands-on design and build alongside an existing team.

  • Architecture decisions that are expensive to reverse later — agent boundaries, retrieval strategy, knowledge structure
  • Multi-agent orchestration and agent architecture
  • Retrieval and RAG infrastructure
  • Knowledge architecture, ontology and knowledge graph design
  • Evaluation and observability infrastructure
  • Architecture ownership while the client team owns delivery
[ REGULATED INDUSTRIES ]

Enterprise AI Trust

For banks, insurers, and enterprises operating AI in business-critical or regulated workflows.

  • Hallucination and unsupported-claim detection
  • Evidence attribution and citation review
  • Confidence calibration assessment
  • Decision-risk and escalation review
  • Governance and auditability frameworks
  • Executive-ready trust assessment report
  • Remediation of critical findings — architecture and evaluation fixes, not a document that ends the engagement

Most engagements start with a Readiness Review and continue into architecture work.

03. Knowledge

Knowledge Architecture

Most AI systems retrieve documents. The better ones retrieve knowledge. The difference is structure.

Returning five chunks of text isn't intelligence. Understanding how those pieces relate to each other is. As enterprise AI matures, the constraint shifts from model capability to how well the underlying domain knowledge has been modelled — entities, relationships, hierarchies, and the rules that connect them.

This work is uncommon. At Credit Suisse I helped build a global client data layer on Palantir Foundry, mapping disparate source systems worldwide into a unified ontology under regulatory scrutiny. That layer underpinned surveillance models that passed Swiss regulatory review.

Well-structured knowledge reduces the amount of reasoning the model has to do. That means smaller models, lower cost, better latency, and outputs you can explain.

04. Process

How a Readiness Review Works

A Readiness Review is the entry engagement. I convert system behavior into a fix list the team can start on Monday — not a score that ends the conversation.

[ 01 // USE CASE SCOPING ]

I define the workflow, business decision, source material, system boundaries, and where failure actually hurts.

  • AI use case and intended output
  • Business-critical decision points
  • Source material and attribution requirements
  • Where failure is expensive and who notices first

[ 02 // EVALUATION DESIGN ]

I define the metrics that matter for this use case, rather than relying on generic AI benchmarks.

  • Faithfulness to source material
  • Evidence attribution and citation support
  • Confidence calibration
  • Decision-risk and escalation behavior

[ 03 // TESTING & SCORING ]

I run representative and adversarial cases to find where the system fails under ambiguity, weak sources, or real-world inputs.

  • Controlled test cases
  • Hallucination and unsupported-claim detection
  • Automated evaluator scoring
  • Trace-level evidence for each finding

[ 04 // FINDINGS & ROADMAP ]

I deliver a prioritised fix list: what broke, why, how to fix it, how long it takes, and what to do first.

  • Root cause per failure
  • Recommended fix
  • Effort estimate
  • Sequencing for the next sprint of work
readiness-review.json 
{
  "system": "Customer Support Routing Agent",
  "metrics": ["routing_accuracy", "retrieval_precision", "regression_on_prompt_change"],
  "finding": "quality drop after model swap; root cause was retrieval, not the model",
  "recommendation": "fix retrieval chunking and add a regression suite before the next release"
}

How an Architecture Engagement Works

Readiness Reviews end with a plan. Architecture engagements execute it. Typically one to two days per week alongside an existing team.

[ 01 // ARCHITECTURE DECISION ]

I establish the decisions that are expensive to reverse before any code is written: agent boundaries, retrieval strategy, knowledge structure, and where humans stay in the loop.

  • Agent topology and orchestration model
  • Retrieval and knowledge architecture
  • Failure modes and escalation paths
  • What the system must never do autonomously

[ 02 // EVALUATION FIRST ]

Evaluation infrastructure ships before or alongside the feature, not after. Without it, every release is a guess.

  • Rubric and metric design for the use case
  • Evaluator architecturally separate from generation
  • Regression suites for prompt and model changes
  • Trace-level observability

[ 03 // BUILD ALONGSIDE THE TEAM ]

I own architecture. The client team owns delivery. Decisions are documented so the team can extend the system without me.

  • Hands-on implementation of core components
  • Design review and technical guidance
  • Architecture documentation the team can act on

[ 04 // HANDOVER ]

The engagement succeeds when the team can maintain and extend the system independently.

  • Documented architecture and decision rationale
  • Evaluation suite the team runs themselves
  • Knowledge transfer, not dependency

Your AI system isn't performing the way it did in testing?

Start with a Readiness Review
05. Build

Build Record

Selected systems. Client names withheld under confidentiality; technical detail is accurate.

BUILD RECORD
Multi-Agent Legal Drafting Platform
PROBLEM
Federal criminal defense drafting requires every argument to be grounded in statute and precedent. A fabricated or mis-supporting citation isn't a quality issue — it's a filing that damages a case. Attorney-client privilege ruled out sending anything to external services.
WHAT I BUILT
Specialized agents for fact extraction, argument construction, and citation verification over a private retrieval layer with zero data egress. Each generation agent scoped to a specific body of law rather than the whole corpus, so retrieval stayed precise. A separate evaluation node scores every draft against an explicit rubric — is the proposition supported, does the citation say what the argument claims — and returns structured feedback that routes back into generation for revision.
RESULT
Briefs that took attorneys 10–40 hours generate in under two minutes, with the evidence trail intact.
STACK
[ Multi-Agent Orchestration // Private RAG // Rubric Evaluation // Qdrant // Elasticsearch // AWS ]
BUILD RECORD
Multi-Agent Marketing Pipeline
PROBLEM
Generating campaign assets from a product description is easy to demo and hard to ship. Small deviations at the first stage compound into unusable output by the third, and nothing in a naive pipeline catches it.
WHAT I BUILT
A three-stage agentic pipeline — persona derivation, positioning, asset generation — with multi-model orchestration routing each stage to the model best suited to it. Every handoff enforces a structured schema and validates before the next agent runs, so errors surface at the boundary where they occur rather than at the end.
RESULT
Boundary validation is what made it production-viable rather than a demo. The pipeline runs unattended without compounding failure across stages.
STACK
[ Multi-Agent Orchestration // Multi-Model Routing // Schema Validation // GPT-4 // Claude ]
BUILD RECORD
Contract Intelligence Platform
PROBLEM
Large contract repositories were unstructured PDFs with inconsistent terminology. Manual extraction could not keep up, and cross-document analysis for risk and reporting was effectively impossible.
WHAT I BUILT
An extraction and normalization pipeline over the contract corpus: structured fields via typed schemas, hierarchical clustering to collapse synonymous legal terms into a shared vocabulary, and a queryable dataset for trend and risk analysis across agreements.
RESULT
Manual review that did not scale became an automated pipeline over the full corpus, with consistent terminology across documents and cross-contract analysis that was previously impossible.
STACK
[ Document Extraction // Schema Normalization // Hierarchical Clustering // Python ]
BUILD RECORD
Global Client Data Layer — Tier-1 Bank
PROBLEM
Surveillance models can only be as good as the data underneath them. Client records sat across disparate systems worldwide with no shared identifiers, inconsistent schemas, and conflicting records — and every transformation had to be defensible to a regulator.
WHAT I BUILT
Contributed to the data modelling and pipeline work behind a global client ontology on Palantir Foundry, mapping worldwide source systems into a unified structure. Earlier, architected the bank's first ML model on that client view for APAC — results presented to the Group CEO, Chairman, and Chief Compliance Officer.
RESULT
The layer underpinned an AML surveillance program whose models achieved Swiss regulatory sign-off with an 80% reduction in false positives.
STACK
[ Ontology Design // Palantir Foundry // PySpark // Regulated Data Pipelines ]
BUILD RECORD
Federated Learning for Cross-Institutional AML — open source
PROBLEM
The most dangerous laundering patterns are invisible to any single bank, because each sees only its own slice — and privacy regulation prevents pooling raw transaction data to find them.
WHAT I BUILT
Led modelling for the first open-source federated learning demonstrator for cross-institutional AML. Banks train a shared model by exchanging weights rather than data. Graph Neural Networks model the transaction networks; differential privacy guarantees no client information can be reconstructed from shared updates.
RESULT
In pilots, the collaborative model surfaced typologies that isolated single-institution models missed. Presented at Nexgen Fraud & Risk Summit, Frankfurt.
STACK
[ Federated Learning // Graph Neural Networks // Differential Privacy // Python ]
06. Evidence

What I Look For

These are the failure modes that show up once a system meets real users — and the reason a working demo is not evidence that a system is production-ready.

TRUST FAILURE
Unsupported Claims
EXAMPLE
An AI-generated risk report claims possible adverse media, ownership opacity, or regulatory concern even though the supplied sources contain no such evidence.
WHY IT MATTERS
Unsupported claims can turn a clean screening result into a false escalation, creating operational, compliance, and reputational risk.
EVALUATED BY
[ Faithfulness // Evidence Attribution // Confidence Calibration ]
TRUST FAILURE
Weak Attribution
EXAMPLE
The system cites a source ID as if it supports a claim, but the source actually says the opposite or does not address the claim at all.
WHY IT MATTERS
In regulated or decision-critical workflows, a citation is not enough. The citation must support the claim being made.
EVALUATED BY
[ Attribution Review // Source Grounding // Audit Evidence ]
TRUST FAILURE
Overconfidence
EXAMPLE
The AI assigns high confidence to conclusions based on weak, missing, ambiguous, or conflicting evidence.
WHY IT MATTERS
AI systems often sound authoritative even when the underlying evidence does not justify certainty. That gap must be measured.
EVALUATED BY
[ Confidence Calibration // Decision Risk // Escalation Logic ]
Engagements are confidential by default. Examples are sanitized. Verified recommendations from senior engineers, including former colleagues now at Meta, are available on the LinkedIn Profile.
07. Principal

Principal Architect

Charles Camp, Principal Architect

Charles Camp

Principal Architect
I combine tier-1 banking rigour with hands-on AI system design. Eleven years in engineering, the last five building production AI across financial services, legal technology, and healthcare — multi-agent systems, retrieval infrastructure, knowledge architecture, and the evaluation layers that keep them reliable.

At Credit Suisse I worked on the global client data layer that underpinned AML surveillance models passing Swiss regulatory review, with an 80% reduction in false positives.

Evaluating non-deterministic systems in regulated environments isn't a new problem. The models have changed; the discipline hasn't.

Vector Systems exists because most organizations can demonstrate what their AI does. Far fewer can demonstrate when it fails, how often, or whether it can be trusted in production.

The Vector Standard

  • [ 01 // EVIDENCE BEFORE CONFIDENCE ]
    AI systems should not sound more certain than their evidence allows. Confidence must be measured, not assumed.
  • [ 02 // ATTRIBUTION IS NON-NEGOTIABLE ]
    Every material claim must be traceable to source material, especially in regulated or decision-critical workflows.
  • [ 03 // FAILURES MUST BE VISIBLE ]
    The purpose of evaluation is not to prove AI is perfect. It is to reveal where it breaks before those failures reach production.
  • [ 04 // BUILDING IS THE POINT ]
    Identifying failures is the easy half. The work is designing the retrieval, knowledge structure, and evaluation that prevent them.
08. Pedigree

Institutional History

Credit Suisse
Carnegie Mellon
Soteria Initiative
Glovo