Signal 01
RAG works in demo, but not reliably
Ingestion, permissions, freshness, evaluation, and quality drift are fragile enough to slow product and customer teams.
AI Infrastructure Audit
AI Infrastructure Audit for LLM Products Moving Beyond MVP
Find the reliability, cost, security, and platform gaps blocking your RAG, agent tools, LLM deployment, Kubernetes, observability, and enterprise AI roadmap.
- 10d
- business-day AI readiness audit
- 7
- infrastructure dimensions reviewed
- 90d
- prioritized roadmap horizon
Who this is for
For AI teams whose prototype is becoming a platform risk
This AI readiness assessment fits teams that already have a working LLM product, a RAG or agent prototype, or enterprise pressure that makes infrastructure decisions urgent.
Signal 02
Agents need controlled tools
Tools now need secrets, approvals, audit logs, limits, retries, network boundaries, and tenant-aware isolation.
Signal 03
LLM spend is hard to explain
Provider bills are growing without request-level attribution, routing policy, fallback strategy, or margin dashboards.
Signal 04
Enterprise buyers ask harder questions
Data residency, private LLM deployment, RBAC, auditability, and security evidence are becoming part of the sales process.
Signal 05
Operations are scattered
Kubernetes, CI runners, queues, workers, observability, and deployment paths have grown without one platform owner model.
Audit scope
What we audit across your AI infrastructure
ToolLeap reviews the AI infrastructure architecture, operating model, and risk surface behind production LLM infrastructure.
ARC
Product and platform architecture
Service boundaries, tenancy, API flows, deployment environments, ownership boundaries, and the AI infrastructure stack behind the product.
RAG
RAG and data pipelines
Ingestion, refresh logic, vector database operations, permissions, evaluation, data freshness, and RAG infrastructure failure modes.
AGT
Agent tool runtime
Tool registry, secrets, approvals, sandboxing, audit logs, retries, rate limits, and isolation for AI agent infrastructure.
LLM
LLM inference and routing
Provider strategy, private or hybrid deployment, fallbacks, latency, quality, cost tradeoffs, and private LLM deployment readiness.
OPS
Kubernetes, CI, and platform operations
Infrastructure as code, environments, runners, worker queues, release paths, runbooks, and Kubernetes cost optimization opportunities.
OBS
Observability and cost control
Traces, evals, token costs, request attribution, SLOs, dashboards, LLM observability, and tenant-level margin signals.
SEC
Security and enterprise controls
RBAC, data residency, network boundaries, secrets, compliance evidence, auditability, and AI security assessment gaps.
Engagement process
A 10-business-day audit that turns architecture risk into a roadmap
The engagement connects AI readiness audit findings to business impact, delivery effort, dependencies, and a 30/60/90-day AI implementation roadmap.
01 Intake
Define goals and constraints
We review ICP, product stage, current architecture, enterprise requirements, production risks, and the business reason for the audit.
02 Review
Inspect architecture and operations
We review repositories, diagrams, cloud or Kubernetes setup, observability, deployment flow, data paths, and platform ownership.
03 Analysis
Map risk and readiness gaps
We score reliability, cost, security, RAG, agents, inference, data, and operations against production AI requirements.
04 Workshop
Prioritize the roadmap
We rank fixes by business impact, risk, delivery effort, dependency order, and the shortest path to measurable improvement.
05 Report
Deliver the audit package
You receive the maturity scorecard, architecture map, risk register, cost drivers, and 30/60/90-day roadmap.
Deliverables
What you get at the end
AI infrastructure maturity scorecard
A clear maturity assessment across reliability, cost, security, data, operations, and enterprise readiness.
Current-state architecture map
A practical view of systems, workloads, tenancy, data flows, model calls, environments, and operational ownership.
Production risk register
Prioritized risks across RAG, agents, LLM deployment, Kubernetes, observability, security, and platform operations.
Cost and reliability driver analysis
The LLM, queue, compute, routing, and operational patterns most likely to affect margin or uptime.
Enterprise readiness gap list
Data residency, audit logs, secrets, RBAC, isolation, private deployment, and compliance evidence gaps.
30/60/90-day implementation roadmap
A sequenced AI readiness roadmap with fixes, dependencies, owners, and optional build paths.
Why ToolLeap
AI platform engineering, not generic AI consulting
ToolLeap works at the infrastructure layer behind LLM products: RAG pipelines, agent tools, controlled execution, Kubernetes operations, observability, private deployment, and enterprise controls.
Related service paths
Turn the audit into targeted platform work
Use the audit to choose the smallest useful build path instead of funding another broad AI consulting workstream.
Foundation
AI platform engineering
Build the platform layer behind reliable RAG, agents, runners, Kubernetes, inference, and enterprise controls.
Follow-up path
RAG productionization
Turn ingestion, permissions, freshness, evaluation, and vector operations into a production-ready capability.
Follow-up path
AI agent infrastructure
Design tool runtimes, approvals, limits, secrets, audit trails, and isolated execution for product agents.
Follow-up path
Private LLM deployment
Assess hybrid inference, local models, GPU operations, data residency, and enterprise deployment paths.
Proof point
Browser terminal development
Use controlled browser-based execution patterns for developer workflows, customer tools, and agent actions.
FAQ
AI infrastructure audit questions
What is the difference between an AI readiness assessment and an AI infrastructure audit?
A generic AI readiness assessment often checks organization, use cases, and adoption plans. ToolLeap focuses on the technical infrastructure behind LLM products: RAG, agents, inference, Kubernetes, observability, cost, security, and enterprise controls.
How long does the audit take?
The standard engagement is designed for ten business days after intake materials are available. Larger platforms or regulated environments can add scope, but the default outcome is still a focused roadmap.
Who should be involved from our team?
The best group is usually a product engineering lead, platform or DevOps owner, security contact, and someone who understands customer or enterprise requirements.
Do we need Kubernetes or self-hosted LLMs already?
No. The audit can review managed LLM APIs, serverless workloads, early Kubernetes usage, or hybrid designs. We only recommend private or self-hosted deployment when the product and buyer requirements justify it.
Can you audit a RAG or agent prototype before it reaches production?
Yes. That is often the right time to review ingestion, permissions, tool execution, evaluation, cost attribution, and operational guardrails before fragile decisions become harder to change.
What artifacts do you need to review?
Useful artifacts include architecture diagrams, repository structure, deployment notes, cloud or Kubernetes setup, observability dashboards, LLM usage reports, data flow notes, and known production or sales risks.
Does the audit include security and governance?
Yes. The audit covers practical AI security assessment areas such as RBAC, secrets, tenant isolation, data residency, audit logs, network boundaries, and evidence needed for enterprise conversations.
What happens after the audit?
You can use the report with your internal team, ask ToolLeap to help design a specific architecture, or turn one roadmap item into targeted platform work such as RAG productionization, agent infrastructure, private LLM deployment, or observability.
Next step
Get a clear roadmap for your AI infrastructure.
In ten business days, ToolLeap maps maturity, architecture gaps, cost drivers, security risks, and the next platform build sequence.