SOLUTION · DATA & INFRASTRUCTURE ENGINEERING

Foundations for production-grade AI.

We design, build, and harden the data and infrastructure layer that AI systems need to work reliably in the real world.

Observability Retrieval Cost Optimization Data Pipelines Inference Infra

01 / THE INFRASTRUCTURE GAP

The model is the visible part. The infrastructure decides whether it delivers.

AI in production fails most often because the infrastructure underneath isn’t there. The model works in a controlled environment, then fails under real load, hallucinates without anyone noticing, integrates poorly with existing systems, and runs up infrastructure bills no one budgeted for.

Production AI Stack1 model · 5 infra layers

Model

What your users actually see

The demo-ready surface: the part everyone notices.

Visible

Held up by: five engineered layers

L-01

Inference serving

Scalable serving with predictable latency and cost.

Scale · latency

L-02

Retrieval / RAG

Vector stores and RAG pipelines tuned to your data.

Vector · rag

L-03

Data pipelines

Secure ingestion, transformation, and governance to inference.

Ingest · govern

L-04

Observability & eval

Continuous evaluation, drift alerting, and audit trails.

Eval · drift

L-05

Governance & cost

Caching, routing, and compliance: the operating economics.

Cost · comply

✓ 5 layers · production-gradeThe model is the visible part Instrumented end-to-end

02 / INSIDE THE STACK

Each layer, shown as the surface your team operates.

The infrastructure isn’t an abstraction. Once it’s built right, each layer becomes a concrete surface your team works from. Here is what each one looks like.

01 · Observability & evaluation

Drift, quality, and ROI: measured live, not asserted.

The dashboard your team opens every morning. Continuous evaluation, real-time scoring, and drift alerting mean a model failing quietly gets caught, with the before/after baseline that proves the win.

Eval · 24h Live

Eval pass rate96%

Drift alerts2

p95 latency410ms

Eval scoreBaseline

✓ Continuous evaluationDrift alerting Audit trails

02 · Retrieval pipeline

A RAG pipeline tuned to your data, not a generic template.

Source documents move through ingestion, transformation, and indexing into a vector store sized for your corpus. Each stage is governed and observable, so a stalled feed is a row you can see, not a silent gap in answers.

Stage status carries the state: indexed, syncing, held

Retrieval · pipeline stages Live

src/contractsPDF ingest · chunk + embedIndexed12.4k

src/ticketsStream · incremental syncSyncing+318

src/wikiGovernance review · PII scanHeldWhy?

indexVector store · 28.7k chunks–recall@5 0.91

✓ Governed ingestionPII-scanned recall@5 0.91

03 · Cost & governance

The operating economics of production AI, on one ledger.

Caching, batching, and model routing are infrastructure-layer wins, each one quantified against a measured baseline. This is the view that keeps the bill no one budgeted for from showing up.

Cost · optimization ledger4 levers · measured

Response caching1.00× → 0.62×−38%

Request batching1.00× → 0.79×−21%

Model routing1.00× → 0.55×−45%

Context trimming1.00× → 0.83×−17%

✓ Measured before/after−38% to −45% Tracked

03 / DELIVERABLES

What you walk away with.

Concrete observability, optimization, and infrastructure, not slides.

DELIVERABLES MANIFEST REF: DATA-INFRA-2026

05 ITEMS · CRYENX-LED

REF DELIVERABLE DESCRIPTION STATUS

D-01 Observability + evaluation infrastructure Continuous evaluation, real-time dashboards, drift alerting, audit trails for governance. INCLUDED

D-02 Cost + performance optimization roadmap Caching, batching, model routing: quantified ROI with before/after measurement. INCLUDED

D-03 Retrieval architecture Vector store and RAG pipeline tuned to your data, not generic templates. INCLUDED

D-04 Secure data pipelines Ingestion, transformation, governance from source systems to inference. INCLUDED

D-05 Operational runbooks Runbooks so your team can keep operating what we build. INCLUDED

04 / WHERE IT APPLIES

Cross-industry application.

Data & Infrastructure work spans Pre-AI engagements (building the foundations correctly from day one) and Post-AI engagements (fixing what’s already running). Industry-specific overlays (HIPAA observability for HealthTech, model risk management for FinTech, edge inference for Manufacturing Tech) are baked into the design.

INDUSTRY APPLICATIONS · 6 SECTORS OVERLAYS BAKED IN

I-01

HealthTech

HIPAA observability

I-02

FinTech

Model risk management

I-03

Manufacturing Tech

Edge inference · MES/SCADA

I-04

Consulting Firms

Knowledge ops scale

I-05

PE Firms

Portfolio operational scale

I-06

Logistics

Real-time data infrastructure

Pre-AI or Post-AI. Same engineering. Different starting point.

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EXPLORE · THE FOUR SOLUTIONS

01 AI Strategy & Enablement Clarity, governance, and execution at scale 02 Data & Infrastructure You’re here 03 Operational AI Systems Automate the work that runs the business 04 Autonomous Systems Multi-agent systems that reason and act