tc/decision infrastructure/v3.2 — Q2 '26
REV 1.4
v3.0 Framework Deployed

Stopexperimenting.Start operating.

Turtle Creek architects the enterprise-grade decision infrastructure that bridges theoretical AI models with automated, reliable business execution.

Output increase
2.6×
Final error rate
0%
Decisions / day
52,847
oadi ▸ control-plane ▸ live
TLS
tc ~/ops $ deploy --layer decision-infra --evaluate
[ok] ingestion bound · evaluation engine live · routing verified
# framework v3.2 · audit trail enabled
tc $ watch --metric decisions --per day
DECISIONS / 24H
52,847
tc $ status
main · git@oadi4 agents · 8 models · 1.2s p95
Ingestion
43.2 MB/s
Evaluation
8 models
Decisions
routing
From chaos to order
ASSESSMENTAUDITDEVFLOWCONSULTINGCLASSROOM
§ 01 / THE PROBLEM

What an operational AI deployment actually looks like.

Most teams run on fragmented signals, manual review, and brittle handoffs. We replace that with a governed stack that turns operational data into automated, observable execution.

Before — chaos

Fragmented, brittle, manual

Source A

STALE

Source B

ERROR

Routing

ERROR

Decision

STALE

Manual review

ERROR

60% of the workday lost to coordination overhead

After — order

Governed, automated, observable

Signal layer

OK

Context & memory

OK

Decision engine

OK

Execution

OK

Outcome

OK

2.6x output - 0% final error rate

§ 02 / THE SYSTEM
◆ Operational AI Framework

A four-layer model for operational intelligence.

Our proprietary methodology decomposes messy operations into four distinct layers of an automated, resilient AI stack. Each layer hardens the one below it.

LAYER 03 · Probabilistic → Deterministic

Decision Infrastructure

Context-aware routing translates probabilistic insights into concrete, auditable operational decisions across your stack.

03
Routes / day
50,412
Auto vs. human
82 / 18
Rollback SLO
<90s
Components
Policy router
Human-in-loop queue
Governance gate
Deterministic fallback
§ AX / PHILOSOPHY
◆ Operating Axioms

Four rules we never compromise on.

I

Systems over models.

A great model inside a broken process degrades faster than it improves. We build the system first, then deploy the intelligence.

II

Execution over theory.

Readiness assessments and strategy decks don't move the needle. Working software that routes real decisions does.

III

Governance before speed.

Automation without auditability is liability. Every decision we automate is observable, reversible, and owned by a human.

IV

Operators, not evangelists.

We measure adoption by whether frontline teams use the system, not by whether leadership has seen the demo.

§ 03 / SERVICES
◆ Engagements

Three ways to work with us.

From a structured diagnostic to an ongoing embedded practice. Start where you are.

S01Flagship
START HERE

Operational AI Audit

A structured diagnostic across all four layers of your operational AI stack. We identify where signal is lost, where decisions stall, and where automation would compound — not create — risk.

Deliverables
  • Four-layer readiness scorecard
  • Prioritized gap register
  • Automation opportunity map
  • 90-day implementation roadmap
  • Executive briefing deck
Duration
2–3 weeks
Pricing
Fixed scope
Book the Audit
S02Implementation

Decision Infrastructure Build

We design, wire, and deploy the ingestion, evaluation, and routing layers specific to your operational context. Delivered as production-grade software, not slide decks.

Deliverables
  • Data ingestion pipeline
  • ML/rules evaluation engine
  • Decision routing layer
  • Governance & audit trail
  • Operator training + handoff
Duration
8–16 weeks
Pricing
Time & materials
Discuss scope
S03Ongoing

Operational AI Retainer

Embedded operational AI expertise: model drift monitoring, evaluation loop tuning, governance reviews, and continuous improvement cycles across your live stack.

Deliverables
  • Monthly evaluation reviews
  • Drift detection & retraining
  • Governance audit cadence
  • Exec visibility reporting
  • On-call engineering support
Duration
Monthly
Pricing
Retainer
Talk to us

All engagements are fixed-scope or time-and-materials. No retainer required to start. Questions? Talk to us.

§ 04 / PROOF
◆ Operational Outcomes

The delta between manual operations and AI-routed decisions.

2.6×
Output increase
0%
Final error rate
50K+
Decisions automated / day
4mo
Avg time to full deployment
Before → After

From reactive operations to autonomous execution.

Before deployment: manual triage, spreadsheet routing, and delayed escalations. After: signals route to decisions in under a second, with full audit trail and rollback.

Ramp window
2 wks
Error rate (post-deploy)
1.9%
Manual overhead reduction
17%
Manual pipeline
AI pipeline
Day 1Day 14
AI pipeline outperforms manual at day 4

Insights

Foundational reading for teams building the decision layer

These articles explain the operating problem, show where current AI approaches fall short, and help you see where Operational AI fits inside your environment.

Article

AI Needs Signals, Not Just Data

Why real-time signals—not stored data—drive operational AI systems.

Article

The Next Layer of AI: Operational Decision Infrastructure

Why AI tools are not enough and how decision infrastructure transforms operations.

Article

The Operational AI Stack

The layers required to build real AI-driven decision systems.

Get started

Operational AI is not a tool.

It is a system. Score your readiness across all four layers in 15 minutes — or start a conversation about a custom engagement.