Enterprise AI
One architecture. Many useful workflows.
Enterprise AI at ColdAI means one shared foundation instead of a new stack for every pilot. Inference can run on-premise or in a private cloud, inside your own infrastructure, while identity, knowledge retrieval, model access and observability are built once as reusable layers. An operating model then decides which workflows earn production status, and each workflow keeps its own permissions, evaluations and accountable owner.
The opportunity
Start with a decision
worth improving.
Several departments are experimenting with AI independently. The opportunity is to share the right foundations while preserving each workflow's permissions, evaluation and accountable owner.
From possibility to a working system
Follow the flow.
Three connected decisions. One considered architecture.
Conceptual flow, not a live system or measured result.
Map the enterprise boundary
Identify systems of record, data classes and workflow owners. Assess where foundations can be shared and where organizational or security boundaries require isolation.
Build reusable foundations
Design access, retrieval, model integration and tracing as reusable capabilities. Keep task-level evaluations and release decisions separate so reuse does not become uncontrolled propagation.
Scale what earns its place
Expand workflows that meet their agreed criteria. Document support responsibilities and total operating costs, then improve the shared architecture using evidence from real deployments.
The work, made concrete
What we can build
with your team.
We agree scope, dependencies and acceptance criteria before delivery. Your project can start with an assessment, a pilot or an integration into an existing system.
An enterprise AI architecture
Clarify the system boundary, the responsible owners and the decisions the architecture needs to support.
A prioritized workflow portfolio
Turn the agreed design into reviewable work, evaluated against representative inputs and explicit success criteria.
Shared controls and an operating model
Make the next step operable: document responsibilities, known limitations and the path from pilot to ongoing use.
In depth
Go deeper into private AI architecture
Two working guides for the teams who build and approve private AI: one for the infrastructure, one for the legal and contractual decisions behind it.
- 01ArchitectureSelf-hosted LLM reference architectureA vendor-neutral reference architecture for running LLMs on-premise or in a private cloud: model choice, serving engines, GPU sizing, gateway and rollback.
- 02Regulation explainerData sovereignty for enterprise AI: GDPR, HIPAA, SOC 2How GDPR, HIPAA and SOC 2 translate into decisions on where AI inference runs, what prompts and logs are kept, which sub-processors are used and who sees them.
The reusable layers of a private enterprise AI platform
Each layer below the workflows is built once and shared. The workflows on top stay separate, so a change to one team's assistant cannot quietly alter another's.
- Workflows and agents
Assistants, document pipelines and agents, each with its own owner, instructions, evaluation set and release decision.
- AI gateway
One entry point that authenticates callers, applies quotas and policy, routes to approved models and records each call.
- Model serving and registry
Approved models, self-hosted or reached over private networking, each pinned to a registered version.
- Knowledge and retrieval
Indexes over approved sources that return only what the requesting user is already allowed to see.
- Identity and policy
The enterprise identity provider, data classifications and access rules that every layer above inherits.
- Observability and audit
Traces, cost metering and audit logs, kept under retention rules agreed with security and privacy owners.
- Systems of record
ERP, CRM, document stores and data platforms, which remain the source of truth for every workflow.
Sharing foundations without sharing mistakes
When several departments experiment with AI on their own, each tends to rebuild the same plumbing: a connection to the identity provider, a way to reach a model, a retrieval index and some form of logging. The duplication costs money, but inconsistency costs more. One team stores every prompt in full, another stores nothing, and nobody can say which models have seen customer data.
The opposite mistake is to centralize too much. If instructions, retrieval settings and release decisions are shared across workflows, a change made for one use case reaches others that were never re-tested. ColdAI's working rule is to share capabilities and keep decisions local: identity integration, the gateway, model access, retrieval infrastructure and tracing are common services, while each workflow's instructions, evaluation set, autonomy level and go-live decision belong to its owner.
The aim of standardizing is faster and safer delivery, not uniformity. A contract-review assistant and a field-service agent can share a gateway and an audit trail while running different models under different approval rules. Many organizations map these shared controls to the functions of the NIST AI Risk Management Framework1, which gives risk teams a vocabulary that does not depend on any one vendor.
Matching inference location to data classification
Most enterprises end up with a mixed estate. A routing policy written against your own data classes tells teams where a workflow's inference may run before anyone builds it.
| Criterion | Managed model API over private networking | Private cloud tenancy you operate | On-premise or air-gapped cluster |
|---|---|---|---|
| Where prompts are processed | In the provider's environment, reached without crossing the public internet | In cloud regions and tenancy you choose and administer | In your own data center, under your physical control |
| Model choice | Widest, including frontier models that are not released as open weights | Open-weight models plus any provider models offered inside that cloud | Open-weight models you can license, verify and run yourself |
| Operational access to data | Provider staff under contract terms, retention settings and audit reports | Your staff, plus the cloud operator's support processes | Your staff only, under your own access controls |
| Operating effort | Lowest: the provider runs capacity, patching and scaling | Moderate to high: you run serving, scaling and upgrades | Highest: hardware, drivers, model imports and spares are yours |
| Typical fit | Internal and confidential data where contract terms satisfy the data owner | Confidential or regulated data that may stay in a chosen region | Restricted data, classified sites or contracts that forbid external processing |
A workflow's column follows from its most sensitive input, including retrieved documents. The self-hosted reference architecture covers the build; the data sovereignty guide covers the legal analysis.
What to have ready before an enterprise AI architecture engagement
ColdAI's enterprise AI work produces an architecture, a prioritized workflow portfolio, and shared controls with an operating model2. It moves fastest when these inputs exist, even in rough form.
Frequently asked questions
What is an enterprise AI platform, as opposed to an AI tool license?
A tool license gives one team one product. An enterprise AI platform is the set of shared services that many workflows use: identity integration, an AI gateway, approved models, retrieval and tracing. It covers tools bought from vendors and systems built in-house, so security and finance can see every AI workflow in one place while teams still choose the interface that suits their task.
Does private AI deployment rule out frontier cloud models?
No. Most platforms route by data class. Workflows handling internal or confidential data may call managed models over private networking under suitable contract terms, while restricted data stays on self-hosted open-weight models. The gateway turns that into a routing rule rather than a separate integration for each team, and the data sovereignty guide covers the legal questions behind each choice.
Should we build the platform before the first use case?
Usually not. Build the shared layers alongside the first funded workflows, so their real requirements decide what the gateway, retrieval and logging must do. A platform designed in isolation tends to solve problems nobody has yet and to miss the ones the first production workflow hits immediately, such as permission-aware retrieval or per-team cost reporting.
Who should own an enterprise AI platform?
A platform team, usually within technology or data, owns the shared layers, their availability and their roadmap. Business owners stay accountable for each workflow's output, evaluations and release decisions, and security and privacy set the policies the gateway enforces. Writing these decision rights down early stops the platform team becoming the default owner of every AI outcome.
How does this relate to the Agentforce, Copilot, Bedrock and Claude practices?
Those are platform-specific practices that sit on this foundation: the Agentforce Center of Excellence, Oracle AI Agent Studio, Bedrock AgentCore, Microsoft Copilot development and Claude Enterprise. The enterprise layer settles identity, data rules, logging and cost reporting once, so each vendor platform plugs into the same controls instead of inventing its own.
What does ColdAI deliver in an enterprise AI engagement?
Three things: an enterprise AI architecture covering inference locations, shared layers and integration points; a prioritized portfolio of workflows with owners and acceptance criteria; and shared controls with an operating model for onboarding, evaluating, supporting and retiring workflows. Where inference must stay inside your infrastructure, the design includes model versioning, A/B testing and audit logging for that environment.
Sources
- AI Risk Management Framework — National Institute of Standards and Technology
- Enterprise AI: deliverables, readiness and approach — ColdAI
Connect the architecture
Go deeper into the stack.
Explore the complementary capabilities that can turn an individual technology into a complete workflow.
Your next move
Bring us the
real problem.
A workflow that takes too long. A system that cannot connect. An idea that needs a technical path. Start there, and we can define what to investigate, build and measure.
- Executive and workflow ownership
- An inventory of systems and data
- Security, platform and delivery stakeholders