# Ales Analytics

> Ales Analytics deploys private AI: open-weight and frontier LLMs running in a client's own cloud account or data centre, behind the client's own gateway.

Ales Analytics builds private AI environments: your data, your infrastructure, open-weight and frontier models and your workflows, assembled into one system you own and can change. Frontier capability inside a boundary you set, rather than an account on somebody else's platform.

We set up AI that runs on your own systems and your own data, built around the way your organisation works.

## Key facts

- Name: Ales Analytics (also written Ales). Website: https://www.alesanalytics.com/
- What it does: designs, deploys and runs private AI systems for organisations, and publishes free, fully cited research on enterprise AI.
- Services: Private AI setup; Enterprise Knowledge Synthesis AI; Autonomous Workflow Optimization engine; Algorithmic Arbitrage and Model Optimization; Managed AI and continuous improvement.
- Where the AI runs: in the client's own cloud account or on its own hardware, including fully disconnected (air-gapped) environments. Prompts and documents stay inside that boundary unless the client decides otherwise.
- Models: open-weight models the client hosts, and frontier models called through the client's own gateway, with each task routed to the model that suits it.
- Ownership: the client keeps the environment, the models, the measurement method, the logs and the documentation. Nothing can only be operated by Ales, and no licence has to be renewed with Ales.
- Measurement: results are reported against a baseline and a test set the client's own team writes, so value is a number rather than an opinion.
- Founded: 2026. Serves organisations worldwide. Works in English.
- Contact: solutions@alesanalytics.com, or https://www.alesanalytics.com/contact. There is no form to get past first.
- Pricing: scoped per engagement and discussed directly. The site publishes no prices.

## Who it is a good fit for

- Organisations that want to use advanced AI on confidential, regulated or commercially sensitive data, and cannot send that data to a service somebody else operates.
- Teams whose metered AI costs are growing with use, deciding whether to host open-weight models themselves, rent capacity or keep paying per call.
- Companies preparing for the EU AI Act, ISO/IEC 42001 or the NIST AI Risk Management Framework, who need an inventory, controls and evidence rather than a slide.
- Leaders who have run AI pilots that stalled before production and want to know why, measured properly, before spending more.
- Anyone who wants AI built around their own documents and systems, with the source printed on every answer.

## How Ales works

- Your data stays yours: Whatever we build runs inside your environment and reads data you already hold. What leaves, if anything, is your decision.
- Measured, not demonstrated: Results are reported against a baseline and a fixed set of questions your own team wrote, so progress is a number rather than an opinion.
- Small before large: One workflow, measured properly, before anything expands. It is the quickest way to learn and the easiest result to trust.
- You keep everything: Open models, your data, your environment and documentation written for your team. You can run and change all of it without us.

## Services

We build private AI systems and then keep them running: open-weight models and frontier models as the requirement demands, your own knowledge and documents underneath, and infrastructure you control around all of it. We tune it for cost, security, speed.

- [Services - Private AI, built and run](https://www.alesanalytics.com/services): A private AI environment you control, and four things we build in it: company knowledge, workflow automation, cost optimisation and ongoing improvement.
- [Private AI Setup - Cloud or on-premises](https://www.alesanalytics.com/services/private-ai-setup): Private AI in your own cloud account or building: open-weight and frontier models behind your own gateway, with identity, logging and cost controls.

## Documentation

One page lists every guide, report and reference on the site, grouped by area, so it is the best place to start.

- [Documentation - Architecture, research and explainers](https://www.alesanalytics.com/docs): Every Ales Analytics guide in one place: the Enterprise Architecture Atlas, fully cited research on enterprise AI, and plain-language explainers.

## Research

Free to read online, no form. Every figure links to the study it came from, and a forecast is labelled as a forecast.

- [Research - Enterprise AI, fully cited](https://www.alesanalytics.com/research): Free, fully cited research on why enterprise AI pilots fail, what your data needs first, and what happens to the costs between pilot and production.
- [Why Enterprise AI Adoption Is Failing](https://www.alesanalytics.com/research/why-enterprise-ai-fails): Why most enterprise AI pilots stall before production: AI not built for the workflow it was placed into. The diagnosis, the causes, and what follows.
- [The Enterprise Data Foundation Layer for AI](https://www.alesanalytics.com/research/data-foundation-layer): Why naive document retrieval is the wrong default for the enterprise, what replaces it, and the connector, atlas and governance specifications underneath.
- [The Scaling Cliff](https://www.alesanalytics.com/research/cost-cliff): Why the sums that held at ten users break at ten thousand, where the hidden seventy per cent of the work sits, and the honest case against self-hosting.

## Architecture Atlas

A vendor-neutral technical reference to enterprise architecture: primitives, the control plane and data plane, Azure, AWS, Google Cloud, Microsoft 365, Kubernetes, AI serving, retrieval and agents, and how real outages propagated. Every page links to its primary sources.

Every page has a Markdown version at the same address with `.md` added, linked below. Each one states its place in the atlas and links to the pages before and after it. Start with the overview: [Enterprise Architecture Atlas](https://www.alesanalytics.com/architecture.md).

### Foundations

- [From Primitives to Cloud Constructs](https://www.alesanalytics.com/architecture/primitives.md): Why every cloud construct exists: the primitives underneath, the control plane and data plane split, static stability, and failure domains as vectors.
- [Distributed Systems and Time](https://www.alesanalytics.com/architecture/distributed-systems.md): Timeouts, retries, idempotency keys, quorum, leases, fencing tokens and clock skew: the distributed-systems mechanics behind every cloud failure.
- [The Numbers Architects Reason With](https://www.alesanalytics.com/architecture/numbers.md): Thirteen calculations behind architecture decisions: availability in series and parallel, queueing, Little's law, KV-cache sizing and cost per token.

### Cloud platforms

- [Azure, AWS and Google Cloud Compared](https://www.alesanalytics.com/architecture/cloud-providers.md): How Azure, AWS and Google Cloud differ where it matters: resource hierarchy, how IAM evaluates a request, policy engines, networking and equivalence.
- [Cloud Networking, DNS and Private Endpoints](https://www.alesanalytics.com/architecture/networking.md): How cloud networking really behaves: DNS caching, private endpoints as DNS overrides, NAT and MTU traps, and the VNet, VPC and global VPC differences.
- [Identity, Tokens and Authorization](https://www.alesanalytics.com/architecture/identity.md): How tokens are validated and revoked, what continuous access evaluation changes, why tokens are never forwarded, and how to state a revocation window.
- [Data, Storage and Databases](https://www.alesanalytics.com/architecture/data-and-storage.md): Sources of truth and derived copies, why replication is not backup, object storage across S3, Blob and GCS, choosing a database class, and CDC.
- [Compute and Kubernetes Architecture](https://www.alesanalytics.com/architecture/compute-and-kubernetes.md): VMs, containers and serverless compared, what keeps running when the Kubernetes control plane stops, DRA for GPUs, and the version skew policy.
- [Security, Secrets and PKI](https://www.alesanalytics.com/architecture/security.md): Envelope encryption and KMS control, certificate rotation, secrets sprawl, supply-chain signing, and what the CrowdStrike incident teaches about EDR.
- [Observability, Operations and Governance](https://www.alesanalytics.com/architecture/observability-and-operations.md): Why observability must be independent of what it observes, error budgets, progressive delivery for configuration, governance, and FOCUS cost data.

### The enterprise estate

- [Microsoft 365 as an Authorization Graph](https://www.alesanalytics.com/architecture/microsoft-365.md): Why Copilot inherits SharePoint's permission state, the two-decision model with Purview DLP, Sites.Selected, RCD, and Entra Agent ID blueprints.
- [Hybrid Cloud, Private Cloud and On-Premises](https://www.alesanalytics.com/architecture/hybrid-and-on-premises.md): What pulls workloads on-premises, licensing as an architectural force, the hard parts of hybrid identity, DNS and AI, and tenant-to-tenant migration.

### AI systems

- [AI Inference and the Model Gateway](https://www.alesanalytics.com/architecture/ai-serving-and-gateways.md): LLM inference explained for architects: prefill and decode, the KV cache, PagedAttention, GPU cost, and what a model gateway must do on every request.
- [Permission-Aware Retrieval, MCP and AI Agents](https://www.alesanalytics.com/architecture/ai-retrieval-and-agents.md): How to build permission-aware RAG, what the MCP 2026-07-28 revision changes, how identity travels through agents, and the OWASP agentic top 10.

### Failure and design

- [Failure Architecture: How Outages Propagate](https://www.alesanalytics.com/architecture/failure.md): How the AWS, Google, Azure Front Door, Cloudflare and CrowdStrike outages propagated, the five fail modes, a failure matrix and a blast-radius method.
- [Twenty-Two Architecture Principles](https://www.alesanalytics.com/architecture/principles.md): Twenty-two enterprise architecture principles, from a declared source of truth to time as a dependency, each traced to its second-order consequences.
- [Architecture Trade-Offs and Second-Order Effects](https://www.alesanalytics.com/architecture/trade-offs.md): The common architecture decisions compared, the second- and third-order effects each brings, and the joins where one domain's failure becomes another's.
- [28 Reference Architecture Scenarios](https://www.alesanalytics.com/architecture/scenarios.md): Twenty-eight reference architectures for AI, data, resilience and platforms, each with flows, trust boundaries, failure modes and the alternative.
- [Worked Architecture Examples](https://www.alesanalytics.com/architecture/worked-examples.md): Three designs worked through from requirements to an injected failure: an enterprise AI assistant, an order API that survives a region, a paying agent.

### Practice

- [Architecture Misconceptions and False Abstractions](https://www.alesanalytics.com/architecture/misconceptions.md): Twenty-four architecture misconceptions, from replication is backup to stateless MCP, and eleven abstractions that work until they quietly don't.
- [Architecture Exercises and Design Reviews](https://www.alesanalytics.com/architecture/exercises.md): Practise architecture: one question per domain, ten derivation exercises and eight design reviews from beginner to principal, with answers.
- [Technology Lifecycle and the Architecture Knowledge Graph](https://www.alesanalytics.com/architecture/lifecycle.md): How to design for protocol and product churn: the six lifecycle states, versioning and skew, replacement strategies, and an architecture knowledge graph.

### Reference

- [Evidence, Verification and Open Questions](https://www.alesanalytics.com/architecture/evidence.md): How every claim in the atlas was checked: the evidence scale, the verification ledger, the corrections made, the open questions and when facts expire.
- [Claim Verification Standard](https://www.alesanalytics.com/architecture/verification-standard.md): The rule every security and cloud provider claim in the atlas follows: an exact source, the date it was checked, the conditions it holds under and its tier.
- [Enterprise Architecture Glossary](https://www.alesanalytics.com/architecture/glossary.md): Plain definitions of the terms in the atlas: control plane, static stability, fail-static, revocation window, Tier-0, KV cache, MCP and more.


## Explainers

Plain-language explanations of the rules, standards and technical terms behind AI in organisations, each linking to its official source.

- [Explainers - AI rules and terms](https://www.alesanalytics.com/explainers): Plain-language explanations of the rules, standards and technical terms behind AI in organisations, each linking to its official source.
- [The EU AI Act](https://www.alesanalytics.com/explainers/eu-ai-act): The EU's AI Act in plain language: what it bans, what it calls high-risk, what it asks of general-purpose models, and the deadlines as amended in 2026.
- [ISO/IEC 42001](https://www.alesanalytics.com/explainers/iso-42001): What ISO/IEC 42001 is, what an AI management system covers, what certification does and does not mean, and how it relates to ISO 27001 and ISO 23894.
- [The NIST AI Risk Management Framework](https://www.alesanalytics.com/explainers/nist-ai-rmf): What the NIST AI RMF is, how its four functions work, what the generative-AI profile adds, and why organisations outside the US use it.
- [Data protection law and AI](https://www.alesanalytics.com/explainers/data-protection-and-ai): How data protection law applies to AI wherever you operate: lawful basis, minimisation, automated decisions, deletion, and residency by architecture.
- [Model risk management](https://www.alesanalytics.com/explainers/model-risk-management): What model risk management means, where it comes from, what an inventory and independent validation are, and why the same structure suits AI systems.
- [AI rules around the world](https://www.alesanalytics.com/explainers/ai-rules-around-the-world): US state laws, the UK approach, Singapore's framework, Japan's law, the OECD principles and EU product rules that touch AI, each linked to the original.
- [Baseline](https://www.alesanalytics.com/explainers/baseline): What a baseline is, why value cannot be shown without one, what to record, and how to capture one when the AI is already partly in use.
- [Control group](https://www.alesanalytics.com/explainers/control-group): How a control group works, when a randomised comparison is possible, what to do when it is not, and why a small permanent comparison is worth keeping.
- [The AI profit and loss view](https://www.alesanalytics.com/explainers/ai-profit-and-loss): A one-page way to report what an AI system earned, saved and helped avoid, net of everything it cost, in the terms a finance team already uses.
- [Total cost of ownership](https://www.alesanalytics.com/explainers/total-cost-of-ownership): The cost layers around an AI system: model, data work, integration, checking, governance, change and infrastructure, and why the licence is a small part.
- [AI inventory](https://www.alesanalytics.com/explainers/ai-inventory): What an AI inventory records, why every framework asks for one first, how to find AI hidden in software you already use, and how to keep it current.
- [Language models and open weights](https://www.alesanalytics.com/explainers/language-models-and-open-weights): What a language model is, the difference between renting one as a service and running one you can download, and why open weights matter for control.
- [Private deployment](https://www.alesanalytics.com/explainers/private-deployment): What running AI on your own servers or cloud account means, what stays inside, what disconnected means, and what it gives you under data protection law.
- [Model gateways](https://www.alesanalytics.com/explainers/model-gateway): What a model gateway is, why routing, policy and cost control end up in one place, and how it makes changing model a setting rather than a project.
- [Owning or renting the hardware](https://www.alesanalytics.com/explainers/own-or-rent-infrastructure): What decides between buying accelerators and renting capacity: how steady the load is, residency, what you already own, and costs the hardware quote omits.
- [Tokens and inference](https://www.alesanalytics.com/explainers/tokens-and-inference): What a token is, what inference means, why longer documents and more people at once change the hardware you need, and how that feeds into sizing.
- [Grounded answers](https://www.alesanalytics.com/explainers/grounded-answers): How an assistant finds the right passage in your documents before answering, why the citation matters, and what makes a grounded answer trustworthy.
- [AI agents](https://www.alesanalytics.com/explainers/ai-agents): The difference between an assistant and an agent, what an agent is allowed to touch, and why the boundary and the audit trail are the product.
- [Human sign-off](https://www.alesanalytics.com/explainers/human-in-the-loop): What human oversight means in practice for AI systems: the checkpoint, who approves, what they see, and how it is recorded.
- [Access and identities for AI](https://www.alesanalytics.com/explainers/machine-identities): Why an AI assistant or agent needs its own identity, how its permissions are set and checked, and why most AI security work is access control.
- [Tuning and routing](https://www.alesanalytics.com/explainers/fine-tuning-and-routing): Tuning a model on your own examples, what routing does, and why sending each question to the right model makes systems faster, cheaper and easier to audit.
- [Evaluation sets](https://www.alesanalytics.com/explainers/evaluation-sets): How a test set of real questions with reviewed answers is built, why your own people write it, and how it turns quality into a number tracked over time.
- [System of record](https://www.alesanalytics.com/explainers/system-of-record): What a system of record is, why the same fact often lives in several systems, and how naming one authority per fact is what makes an AI answer trustworthy.

## Company

- [Ales Analytics - AI Infrastructure You Own](https://www.alesanalytics.com/): Private AI environments built around your own data: frontier and open-weight models on infrastructure you control, joined to the systems you already run.
- [About Ales Analytics - Our mission](https://www.alesanalytics.com/about): Our mission: to put the full power of AI to work for every organisation, built to its exact specifications and run inside its own environment.
- [Contact - Talk to us about private AI](https://www.alesanalytics.com/contact): Tell us what you are trying to do, in as much or as little detail as you have. You will be talking to the people who build these environments.

## Markdown versions

Every page above as plain Markdown, at the page's own address with `.md` added (the home page is `/index.md`). Each one opens with the title and a one-line summary and ends with its sources.

- [Ales Analytics - AI Infrastructure You Own](https://www.alesanalytics.com/index.md)
- [Services - Private AI, built and run](https://www.alesanalytics.com/services.md)
- [Private AI Setup - Cloud or on-premises](https://www.alesanalytics.com/services/private-ai-setup.md)
- [Documentation - Architecture, research and explainers](https://www.alesanalytics.com/docs.md)
- [Research - Enterprise AI, fully cited](https://www.alesanalytics.com/research.md)
- [Explainers - AI rules and terms](https://www.alesanalytics.com/explainers.md)
- [Why Enterprise AI Adoption Is Failing](https://www.alesanalytics.com/research/why-enterprise-ai-fails.md)
- [The Enterprise Data Foundation Layer for AI](https://www.alesanalytics.com/research/data-foundation-layer.md)
- [The Scaling Cliff](https://www.alesanalytics.com/research/cost-cliff.md)
- [The EU AI Act](https://www.alesanalytics.com/explainers/eu-ai-act.md)
- [ISO/IEC 42001](https://www.alesanalytics.com/explainers/iso-42001.md)
- [The NIST AI Risk Management Framework](https://www.alesanalytics.com/explainers/nist-ai-rmf.md)
- [Data protection law and AI](https://www.alesanalytics.com/explainers/data-protection-and-ai.md)
- [Model risk management](https://www.alesanalytics.com/explainers/model-risk-management.md)
- [AI rules around the world](https://www.alesanalytics.com/explainers/ai-rules-around-the-world.md)
- [Baseline](https://www.alesanalytics.com/explainers/baseline.md)
- [Control group](https://www.alesanalytics.com/explainers/control-group.md)
- [The AI profit and loss view](https://www.alesanalytics.com/explainers/ai-profit-and-loss.md)
- [Total cost of ownership](https://www.alesanalytics.com/explainers/total-cost-of-ownership.md)
- [AI inventory](https://www.alesanalytics.com/explainers/ai-inventory.md)
- [Language models and open weights](https://www.alesanalytics.com/explainers/language-models-and-open-weights.md)
- [Private deployment](https://www.alesanalytics.com/explainers/private-deployment.md)
- [Model gateways](https://www.alesanalytics.com/explainers/model-gateway.md)
- [Owning or renting the hardware](https://www.alesanalytics.com/explainers/own-or-rent-infrastructure.md)
- [Tokens and inference](https://www.alesanalytics.com/explainers/tokens-and-inference.md)
- [Grounded answers](https://www.alesanalytics.com/explainers/grounded-answers.md)
- [AI agents](https://www.alesanalytics.com/explainers/ai-agents.md)
- [Human sign-off](https://www.alesanalytics.com/explainers/human-in-the-loop.md)
- [Access and identities for AI](https://www.alesanalytics.com/explainers/machine-identities.md)
- [Tuning and routing](https://www.alesanalytics.com/explainers/fine-tuning-and-routing.md)
- [Evaluation sets](https://www.alesanalytics.com/explainers/evaluation-sets.md)
- [System of record](https://www.alesanalytics.com/explainers/system-of-record.md)
- [Architecture Atlas - Enterprise systems and how they fail](https://www.alesanalytics.com/architecture.md)
- [From Primitives to Cloud Constructs](https://www.alesanalytics.com/architecture/primitives.md)
- [Distributed Systems and Time](https://www.alesanalytics.com/architecture/distributed-systems.md)
- [The Numbers Architects Reason With](https://www.alesanalytics.com/architecture/numbers.md)
- [Azure, AWS and Google Cloud Compared](https://www.alesanalytics.com/architecture/cloud-providers.md)
- [Cloud Networking, DNS and Private Endpoints](https://www.alesanalytics.com/architecture/networking.md)
- [Identity, Tokens and Authorization](https://www.alesanalytics.com/architecture/identity.md)
- [Data, Storage and Databases](https://www.alesanalytics.com/architecture/data-and-storage.md)
- [Compute and Kubernetes Architecture](https://www.alesanalytics.com/architecture/compute-and-kubernetes.md)
- [Security, Secrets and PKI](https://www.alesanalytics.com/architecture/security.md)
- [Observability, Operations and Governance](https://www.alesanalytics.com/architecture/observability-and-operations.md)
- [Microsoft 365 as an Authorization Graph](https://www.alesanalytics.com/architecture/microsoft-365.md)
- [Hybrid Cloud, Private Cloud and On-Premises](https://www.alesanalytics.com/architecture/hybrid-and-on-premises.md)
- [AI Inference and the Model Gateway](https://www.alesanalytics.com/architecture/ai-serving-and-gateways.md)
- [Permission-Aware Retrieval, MCP and AI Agents](https://www.alesanalytics.com/architecture/ai-retrieval-and-agents.md)
- [Failure Architecture: How Outages Propagate](https://www.alesanalytics.com/architecture/failure.md)
- [Twenty-Two Architecture Principles](https://www.alesanalytics.com/architecture/principles.md)
- [Architecture Trade-Offs and Second-Order Effects](https://www.alesanalytics.com/architecture/trade-offs.md)
- [28 Reference Architecture Scenarios](https://www.alesanalytics.com/architecture/scenarios.md)
- [Worked Architecture Examples](https://www.alesanalytics.com/architecture/worked-examples.md)
- [Architecture Misconceptions and False Abstractions](https://www.alesanalytics.com/architecture/misconceptions.md)
- [Architecture Exercises and Design Reviews](https://www.alesanalytics.com/architecture/exercises.md)
- [Technology Lifecycle and the Architecture Knowledge Graph](https://www.alesanalytics.com/architecture/lifecycle.md)
- [Evidence, Verification and Open Questions](https://www.alesanalytics.com/architecture/evidence.md)
- [Claim Verification Standard](https://www.alesanalytics.com/architecture/verification-standard.md)
- [Enterprise Architecture Glossary](https://www.alesanalytics.com/architecture/glossary.md)

- [Changes, as an Atom feed](https://www.alesanalytics.com/feed.xml): new research, atlas changes and benchmark runs, newest first.

## Optional

- [The whole site as one text file](https://www.alesanalytics.com/llms-full.txt): every page above with its full text, for reading in one request.
- [Privacy statement](https://www.alesanalytics.com/privacy): What this site collects, why, how long it keeps it, the cookies it sets, including Google Analytics, how to change your decision, and how to reach us about any of it.