Enterprise AI Systems Diagnostic Lab

Before spending on AI builds.
Inspect the structural foundation.

We conduct multi-layer diagnostic stress tests across your vector embeddings, API token economics, security perimeters, and business ROI—giving you an unbiased pre-flight report before you invest.

Fixed 10-Day Sprint
Zero Vendor Kickbacks
Air-Gapped NDA
SYSTEMS HEALTH OBSERVATORY
DIAGNOSTIC MATRIX
68
/ 100
OVERALL READINESS GRADE: TIER 2
Operational with high-risk blindspots in token economics and RAG chunking.
SCAN TARGET:Internal Documentation, Wikis, PDF Archives & Vector Stores
Detected Blindspot: High Hallucination Risk

Unstructured PDF tables and fragmented chunking causing 26% retrieval inaccuracies and hallucinated responses.

Remediation Blueprint

Implement hybrid BM25 + dense vector indexing with dynamic semantic chunking and automated metadata tags.

Expected Yield: 99.4% Retrieval Accuracy • Hallucinations Eliminated
01 / Financial Diagnostic

How much operational waste is hiding in your AI stack?

Unoptimized model routing, redundant token calls, and manual triage errors drain six figures annually. Adjust the sliders below to estimate your potential audit savings.

$15,000 / mo
$2k$25k$50k$100k+
120 hrs / mo
20 hrs150 hrs300 hrs500 hrs
PROJECTED ANNUAL AUDIT RECOVERY
Est. Annual API Spend Waste$99,000From unoptimized frontier calls & missing caching
Total Projected 12-Mo ROI$174,600API optimization + labor hours automated
Book Free 20-Min Feasibility Audit Call
02 / Sprint Roadmap

The 10-Day Diagnostic Sprint Lifecycle

A rigorous, structured engineering sprint designed to uncover architectural vulnerabilities with minimal internal team disruption.

Phase 01Days 01–03

Confidential Architecture & Data Ingestion

Under strict NDA, we inspect codebases, database schemas, API latency, vector chunking strategies, and token cost accounts.

Key Deliverable:Data Flow Topology & Security Boundary Mapping
Phase 02Days 04–06

Diagnostic Stress-Testing & Vulnerability Probing

We stress-test prompt injection resilience, benchmark RAG recall accuracy, evaluate token over-expenditure, and isolate edge-case failure modes.

Key Deliverable:Vulnerability Matrix & Benchmark Stress Report
Phase 03Days 07–09

Model Routing & Financial ROI Modeling

We construct a quantified cost-benefit model, benchmark open-source vs. frontier models, and engineer a prioritized 12-month roadmap.

Key Deliverable:Model Selection Matrix & Quantified ROI Forecast
Phase 04Day 10

Executive C-Suite Briefing & Engineering Spec

We deliver a vendor-agnostic technical specification and executive briefing deck with immediate 30/60/90-day execution milestones.

Key Deliverable:Board-Ready Presentation & Full Architecture Spec
03 / Vulnerability Radar

The 4 blindspots that sink enterprise AI initiatives.

Most failures aren’t caused by the underlying foundation models—they are caused by brittle chunking, missing guardrails, and uncontrolled compute spend.

FINANCIAL EFFICIENCY

The Silent Token Bleed

Failure Mode:

Calling expensive frontier LLMs ($0.03/1k tokens) for routine classification and deterministic data routing.

Audit Remediation:

Specialized Small Language Model (SLM) routing + semantic caching layer.

60%–75% API Cost Cut
ACCURACY & TRUST

The Unindexed Vector RAG Trap

Failure Mode:

Fragmented chunking and missing metadata causing 25%+ hallucination rates on complex PDF contracts.

Audit Remediation:

Hybrid BM25 + dense embeddings with automated document restructuring.

99.4% Retrieval Precision
GOVERNANCE & PII

The Shadow PII Exposure

Failure Mode:

Transmitting unmasked customer identifiers and proprietary data across external public APIs.

Audit Remediation:

Local air-gapped scrubbing proxy with deterministic schema guardrails.

100% Zero-Data Retention
EXECUTION VELOCITY

The Stalled Staging Experiment

Failure Mode:

80% of internal AI proof-of-concepts stall in staging due to unbounded edge cases and missing human handoffs.

Audit Remediation:

Deterministic exception routing and human-in-the-loop operational portals.

3–5 Wk Production Path
Enterprise Data Sovereignty

Zero Data Retention. Zero Vendor Bias. 100% Client Ownership.

We do not resell vendor licenses, take platform commissions, or push pre-selected proprietary frameworks. Our audit findings and engineering specifications are 100% vendor-agnostic and owned by your organization.

All technical evaluations are conducted under strict bilateral NDAs with air-gapped zero-retention inspection protocols. Your customer records and codebases are never stored, logged, or used for model training.

Bilateral NDA Protected
Zero Data Retention Sandbox
100% Vendor-Agnostic Advice
Full Deliverable Ownership
04 / Engagement Tiers

Select the diagnostic depth tailored to your stack.

From rapid 3-day feasibility checks to full-scale enterprise systems audits, choose the engagement that delivers actionable clarity.

MOST POPULAR DIAGNOSTIC

Comprehensive AI Systems Audit

10-Day Diagnostic Sprint

Complete end-to-end evaluation spanning infrastructure, data hygiene, security boundaries, model economics, and prioritized ROI roadmaps.

Data Infrastructure & Vector RAG Assessment
API & Token Cost Optimization Matrix (30%–60% Savings)
Security, Prompt Injection & PII Vulnerability Audit
Ranked 12-Month Automation Roadmap with ROI Models
Executive C-Suite Briefing Deck & Technical Architecture Spec
100% Vendor-Agnostic Blueprint & Full Code Ownership
Schedule 10-Day Audit
RAPID FEASIBILITY

Targeted Workflow Feasibility Scan

3-Day Rapid Deep Dive

A focused evaluation of a single mission-critical manual workflow or dataset before committing capital to full-scale development.

Single Workflow Data Hygiene & Edge-Case Dissection
Technical Feasibility & Accuracy Risk Scoring
Estimated Build Timeline & Budget Forecast
Proof-of-Concept Integration Architecture Spec
Clear Go / No-Go Engineering Recommendation
Request Workflow Scan
CONTINUOUS GOVERNANCE

Quarterly Security & Cost Governance

Ongoing Advisory Retainer

Continuous oversight of live AI systems to eliminate prompt injection risks, enforce PII masking, and prevent token spend creep.

Live LLM API Cost & Token Waste Monitoring
Quarterly Model Re-evaluation (SLM vs Frontier vs Open-Source)
Continuous Security Guardrail & Schema Verification
Automated Telemetry & Anomaly Alerting
Monthly Advisory Briefing with Principal AI Architects
Inquire About Governance
Frequently Asked Questions

Audit Details & Confidentiality

Key details regarding timeline requirements, security NDAs, stakeholder time commitments, and deliverable ownership.

Our standard comprehensive AI systems audit is completed in exactly 10 business days. It is engineered to require minimal time commitment (2–3 hours total across key stakeholder interviews) from your internal engineering and executive leadership.

Ready to validate your enterprise AI roadmap with zero risk?

Schedule a 20-minute executive briefing with our principal AI architects. We will discuss your technology stack, identify immediate diagnostic vectors, and outline a fixed-scope audit sprint.