# Scayl

> Scayl (scayl.ai) is an AI implementation hub: sourced data on how companies get value from AI, a searchable map of the 369 firms that implement AI inside client organisations, and free tools such as the Agentic Commerce Grader.

AI assistants and search engines are welcome here. Everything below is public: read it, quote it and cite it with a link to the page. Every figure on scayl.ai names its source on the page; Scayl's own data (the Landscape and the benchmarks) is a dated snapshot and says so.

## Main pages

- [Home](https://scayl.ai/): Sourced charts on the AI value gap, a searchable map of the firms that implement AI inside companies, and free tools like the Agentic Commerce Grader.
- [AI Implementation Landscape 2026](https://scayl.ai/ai-implementation-landscape-2026/): Who puts AI to work inside companies: 369 firms across 11 layers of the market, each with headquarters, website, description, funding, deals and sources. v3 snapshot of 2026-10-20, work in progress.
- [AI Implementation Landscape 2026: the report](https://scayl.ai/ai-implementation-landscape-2026/report/): Six findings from the Landscape data, with every figure computed from it: how the market splits, where firms are based, who funds and buys them, and what to ask each kind of firm. PDF: https://scayl.ai/assets/landscape/report/ai-implementation-landscape-2026-report.pdf
- [Free tools](https://scayl.ai/tools): Free tools that take the guesswork out of AI: the Agentic Commerce Grader is live today, with readiness, ROI, cost and governance tools on the way.
- [Agentic Commerce Grader](https://scayl.ai/agentic-commerce-grader): Free test: an AI shopping agent tries to find, price and buy a product on your store. Get a grade, a step-by-step replay and the fixes that matter most.
- [Agentic Commerce Grader for Chrome](https://chromewebstore.google.com/detail/ijngngagnblbbkodkgnhlglkgebpcphd): the free Chrome extension that opens the grader for the store in the current tab, so any store can be tested in one click.
- [Agentic Commerce Benchmarks](https://scayl.ai/agentic-commerce-grader-benchmarks/): 96 of 182 stores scanned let an AI agent reach checkout; the page shows where the others are lost, step by step.
- [How ready are independent stores for AI shoppers?](https://scayl.ai/agentic-commerce-grader-benchmarks/report/): The Agentic Commerce Grader tested 414 independent online stores in 20 niches once each (26–27 Oct 2026): grades, where AI shopping agents get stuck, by platform and niche, and what to fix. PDF: https://scayl.ai/assets/benchmarks/report/store-benchmark-report-2026-10.pdf
- [AI implementation glossary](https://scayl.ai/ai-implementation-glossary/): 60 AI implementation and agentic AI terms in plain English, each with a full definition, practical notes and sources.
- [Blog](https://scayl.ai/blog): Sourced articles on how companies get real value from AI: where the gap is, what the leaders do differently, and how to redesign work around AI.

## Landscape layers

Grouped by how a buyer would hire them, in recommended order. Each layer keeps its workbook name in brackets.

- [AI-native implementation firms](https://scayl.ai/ai-implementation-landscape-2026/layer/4) (Hire a specialist team; workbook layer: AI-native implementation firms): 90 firms. Firms built from the start to implement AI for clients. Best if you want a team that builds AI for you, end to end.
- [Regional & industry specialists](https://scayl.ai/ai-implementation-landscape-2026/layer/5) (Hire a specialist team; workbook layer: Regional & vertical specialists): 76 firms. Local and industry-focused firms implementing AI. Best if you want a partner who knows your market or your industry.
- [Big consultancies & integrators](https://scayl.ai/ai-implementation-landscape-2026/layer/3) (Hire a specialist team; workbook layer: Incumbent consultancies & integrators): 86 firms. Established consultancies and system integrators adding AI. Best if you're a large enterprise that needs scale and change across many functions.
- [Managed IT providers adding AI](https://scayl.ai/ai-implementation-landscape-2026/layer/9) (Use who you already work with; workbook layer: MSP & IT services roll-ups): 11 firms. Managed-service and IT-services providers being consolidated. Best if you're a smaller company and an outside IT provider already runs your systems.
- [Cloud & software platform programmes](https://scayl.ai/ai-implementation-landscape-2026/layer/2) (Use who you already work with; workbook layer: Platform & hyperscaler programmes): 10 firms. Cloud and software platforms that embed engineers at customers. Best if you're on AWS, Google Cloud, Microsoft, Salesforce or similar and want their certified partners.
- [Software that comes with engineers](https://scayl.ai/ai-implementation-landscape-2026/layer/6) (Use who you already work with; workbook layer: Platforms with embedded engineers): 7 firms. Software platforms that ship with their own engineers. Best if you'd rather buy a platform and have its engineers set it up with you.
- [Data & integration partners](https://scayl.ai/ai-implementation-landscape-2026/layer/10) (Buy it as a product or fix the foundations; workbook layer: Data, integration & semantic layer): 51 firms. Data and integration partners that make AI usable. Best if your AI projects stall on messy, scattered or inaccessible data.
- [Training & change management](https://scayl.ai/ai-implementation-landscape-2026/layer/11) (Buy it as a product or fix the foundations; workbook layer: Training & change management): 12 firms. Partners that train staff and manage the change to AI. Best if the AI tools are in place but people aren't using them well.
- [Ready-made implementation software](https://scayl.ai/ai-implementation-landscape-2026/layer/8) (Buy it as a product or fix the foundations; workbook layer: Implementation as software): 8 firms. Software that does the implementation work itself. Best if you want a cheaper, productised route instead of hiring a team.
- [Custom software built with AI](https://scayl.ai/ai-implementation-landscape-2026/layer/7) (Buy it as a product or fix the foundations; workbook layer: AI software factories): 10 firms. Firms that build custom software with AI at the core. Best if you need bespoke software built fast, with AI doing much of the work.
- [AI labs' own deployment teams](https://scayl.ai/ai-implementation-landscape-2026/layer/1) (Go to the source; workbook layer: Lab deployment arms): 8 firms. The AI labs' own deployment teams, placed inside customers. Best if you're a very large customer of OpenAI, Anthropic, Google or a similar lab.

## Articles

- [Everyone Uses AI. Almost No One Has Rebuilt Around It.](https://scayl.ai/everyone-uses-ai-almost-no-one-has-rebuilt-around-it/): Nearly nine in ten organisations use AI, but only about 6% see significant value. The difference is redesigning how work gets done, not using more tools.

## Glossary

- [Agent washing](https://scayl.ai/ai-implementation-glossary/agent-washing): Marketing existing chatbots, assistants or automation tools as 'AI agents' without real agentic capabilities.
- [Agent-to-agent protocol (A2A)](https://scayl.ai/ai-implementation-glossary/agent-to-agent-protocol): An open protocol that lets AI agents built by different vendors discover each other, exchange tasks and share results securely.
- [Agentic AI](https://scayl.ai/ai-implementation-glossary/agentic-ai): AI systems that pursue a goal by planning and taking multi-step actions, such as calling tools and checking results, rather than only answering a question.
- [Agentic commerce](https://scayl.ai/ai-implementation-glossary/agentic-commerce): AI agents that research, compare and buy products on a shopper's behalf, and the standards and storefront changes that let them do it.
- [Agentic workflow](https://scayl.ai/ai-implementation-glossary/agentic-workflow): A business process in which AI models carry out several of the steps, following a path defined in code, with people handling direction and exceptions.
- [AgentOps](https://scayl.ai/ai-implementation-glossary/agentops): The practices and tools for running AI agents in production: deploying, monitoring, evaluating, securing and improving them over time.
- [AI agent](https://scayl.ai/ai-implementation-glossary/ai-agent): A piece of software in which a language model works in a loop: it chooses actions, uses tools, observes the results and continues until a task is complete.
- [AI centre of excellence](https://scayl.ai/ai-implementation-glossary/ai-centre-of-excellence): A central team that sets AI standards, shares expertise and tools, and helps business units find, build and scale AI use cases.
- [AI governance](https://scayl.ai/ai-implementation-glossary/ai-governance): The policies, roles, processes and controls an organisation uses to decide which AI to build or buy and keep its use lawful, safe and aligned with its goals.
- [AI literacy](https://scayl.ai/ai-implementation-glossary/ai-literacy): The skills, knowledge and understanding people need to use AI effectively and safely, and to judge its outputs, risks and limits.
- [Chain of thought](https://scayl.ai/ai-implementation-glossary/chain-of-thought): A model working through intermediate reasoning steps before giving its answer, which improves accuracy on multi-step problems.
- [Change management](https://scayl.ai/ai-implementation-glossary/change-management): Helping people move from the old way of working to the new one, so that an AI system is actually adopted and its benefits realised.
- [Composite AI](https://scayl.ai/ai-implementation-glossary/composite-ai): Combining different AI techniques (machine learning, language models, rules, knowledge graphs, optimisation) in one solution, each used where it works best.
- [Computer use](https://scayl.ai/ai-implementation-glossary/computer-use): AI agents that operate a computer the way a person does, by reading the screen and moving the mouse and keyboard, to work in software that has no API.
- [Context engineering](https://scayl.ai/ai-implementation-glossary/context-engineering): Deciding what information goes into a model's limited context window at each step (instructions, documents, tool results, memory) so it can do the task well.
- [Context window](https://scayl.ai/ai-implementation-glossary/context-window): The maximum amount of text, measured in tokens, that a model can consider at once, including the instructions, documents, conversation and its own answer.
- [Copilots](https://scayl.ai/ai-implementation-glossary/copilots): AI assistants built into everyday software that help a person work faster (drafting, summarising, answering, coding) while the person stays in control.
- [Data readiness](https://scayl.ai/ai-implementation-glossary/data-readiness): Whether an organisation's data is accessible, accurate, well described, governed and fit for a specific AI use case.
- [Embeddings](https://scayl.ai/ai-implementation-glossary/embeddings): Lists of numbers that represent the meaning of a piece of text, an image or another item, so that similar meanings sit close together.
- [EU AI Act](https://scayl.ai/ai-implementation-glossary/eu-ai-act): The European Union's law on artificial intelligence, Regulation (EU) 2024/1689, which sets rules by risk level for AI placed on or used in the EU market.
- [Evals](https://scayl.ai/ai-implementation-glossary/evals): Repeatable tests that measure whether an AI system does its job well, run before launch and on every change to prompts, models or data.
- [Explainability](https://scayl.ai/ai-implementation-glossary/explainability): The ability to give people a meaningful account of why an AI system produced a particular output or decision.
- [Fine-tuning](https://scayl.ai/ai-implementation-glossary/fine-tuning): Further training an existing model on your own examples so it adopts a format, tone or narrow skill more reliably than prompting alone achieves.
- [FinOps for AI](https://scayl.ai/ai-implementation-glossary/finops-for-ai): Applying cloud financial management practices to AI: making AI spend visible, allocating it to teams and use cases, and optimising it against business value.
- [Forward-deployed engineering](https://scayl.ai/ai-implementation-glossary/forward-deployed-engineering): Engineers embedded with a customer, working inside its systems and teams, to get AI working on real problems in production.
- [Foundation model](https://scayl.ai/ai-implementation-glossary/foundation-model): A large model trained on broad data that can be adapted to many tasks, such as a large language model or an image model, rather than built for one purpose.
- [Grounding](https://scayl.ai/ai-implementation-glossary/grounding): Tying a model's output to trusted sources, such as your documents, databases or live search, so answers reflect real data rather than the model's memory.
- [Guardrails](https://scayl.ai/ai-implementation-glossary/guardrails): Policy and technical limits on what an AI system can say or do, such as input and output filters, permission checks and rules for when to stop.
- [Hallucination](https://scayl.ai/ai-implementation-glossary/hallucination): When a model produces fluent, confident output that is false or unsupported, such as an invented fact, citation, number or policy.
- [Human-in-the-loop](https://scayl.ai/ai-implementation-glossary/human-in-the-loop): Designing an AI system so a person reviews, approves or corrects its work at set points, especially for decisions that are high-impact or uncertain.
- [Inference cost](https://scayl.ai/ai-implementation-glossary/inference-cost): What it costs to run a model each time it is used, driven by tokens processed, model size, reasoning effort and how the model is hosted.
- [ISO/IEC 42001](https://scayl.ai/ai-implementation-glossary/iso-iec-42001): The international standard for an AI management system: requirements for how an organisation governs, develops, provides and uses AI responsibly.
- [Large language model (LLM)](https://scayl.ai/ai-implementation-glossary/large-language-model): A neural network trained on vast amounts of text to predict the next token, giving it broad abilities to write, summarise, reason over and transform language.
- [LLMOps](https://scayl.ai/ai-implementation-glossary/llmops): The operational practices for applications built on large language models: prompt and model versioning, evaluation, deployment, monitoring and cost control.
- [llms.txt](https://scayl.ai/ai-implementation-glossary/llms-txt): A proposed plain-text file at a website's root that gives language models a short, structured guide to the site's most important content.
- [MLOps](https://scayl.ai/ai-implementation-glossary/mlops): The practices and tooling for reliably building, deploying and maintaining machine learning models in production, borrowing from DevOps.
- [Model Context Protocol (MCP)](https://scayl.ai/ai-implementation-glossary/model-context-protocol): An open standard for connecting AI applications to tools and data sources, so one connector works with any model or app that supports the protocol.
- [Model distillation](https://scayl.ai/ai-implementation-glossary/model-distillation): Training a smaller 'student' model to imitate a larger 'teacher' model, to get similar quality on a task at a fraction of the cost and latency.
- [Model risk](https://scayl.ai/ai-implementation-glossary/model-risk): The risk of loss or harm from decisions based on models that are wrong, misused or used outside their intended purpose.
- [Multi-agent orchestration](https://scayl.ai/ai-implementation-glossary/multi-agent-orchestration): Coordinating several AI agents, each with its own role and tools, so that together they complete a task too large or varied for one agent.
- [NIST AI RMF](https://scayl.ai/ai-implementation-glossary/nist-ai-rmf): The US National Institute of Standards and Technology's voluntary framework for managing AI risk, built on four functions: Govern, Map, Measure and Manage.
- [Observability](https://scayl.ai/ai-implementation-glossary/observability): Collecting traces, logs and metrics from AI systems (every prompt, tool call, cost and outcome) so teams can see what happened and why.
- [Open-weight models](https://scayl.ai/ai-implementation-glossary/open-weight-models): Models whose trained parameters (weights) are published, so anyone can download, run and adapt them on their own infrastructure, within the licence terms.
- [Physical AI](https://scayl.ai/ai-implementation-glossary/physical-ai): AI that perceives and acts in the physical world through robots, vehicles, drones and machines, trained largely in simulation.
- [Prompt engineering](https://scayl.ai/ai-implementation-glossary/prompt-engineering): Writing and refining the instructions, examples and structure given to a model so it produces the output you need reliably.
- [Prompt injection](https://scayl.ai/ai-implementation-glossary/prompt-injection): An attack in which text crafted by an attacker, in a message, web page, email or document, tricks a model into ignoring its instructions or misusing its tools.
- [Reasoning models](https://scayl.ai/ai-implementation-glossary/reasoning-models): Language models trained to think through a problem step by step before answering, spending more compute at answer time to do better on hard tasks.
- [Red teaming](https://scayl.ai/ai-implementation-glossary/red-teaming): Deliberately attacking an AI system, as an adversary would, to find harmful outputs, security holes and failure modes before real users or attackers do.
- [Responsible AI](https://scayl.ai/ai-implementation-glossary/responsible-ai): Developing and using AI in line with principles such as fairness, transparency, accountability, privacy, safety and respect for human rights.
- [Retrieval-augmented generation (RAG)](https://scayl.ai/ai-implementation-glossary/retrieval-augmented-generation): Grounding a model's answers in your own documents and data by retrieving the relevant passages first and giving them to the model with the question.
- [Semantic layer](https://scayl.ai/ai-implementation-glossary/semantic-layer): A shared, governed definition of a company's business metrics and entities (revenue, customer, churn) that people, BI tools and AI can all query consistently.
- [Shadow AI](https://scayl.ai/ai-implementation-glossary/shadow-ai): Employees using AI tools that the organisation has not approved or does not know about, often by pasting company data into public chatbots.
- [Small language models](https://scayl.ai/ai-implementation-glossary/small-language-models): Language models with far fewer parameters than frontier models, cheap and fast enough to run on modest hardware or on a device, often tuned for narrow tasks.
- [Sovereign AI](https://scayl.ai/ai-implementation-glossary/sovereign-ai): AI infrastructure, models and data under the jurisdiction and control of a country, region or organisation, rather than depending fully on foreign providers.
- [Synthetic data](https://scayl.ai/ai-implementation-glossary/synthetic-data): Artificially generated data that mimics the patterns of real data, used to train, test or evaluate AI when real data is scarce, sensitive or unbalanced.
- [Tokens](https://scayl.ai/ai-implementation-glossary/tokens): The small pieces of text (word fragments, words or symbols) that models read and write, and the unit in which most AI usage is priced.
- [Tool use](https://scayl.ai/ai-implementation-glossary/tool-use): A model's ability to call external functions or APIs, such as a search, a database query or a payment step, and use the results in its answer.
- [Use-case prioritisation](https://scayl.ai/ai-implementation-glossary/use-case-prioritisation): Choosing which AI opportunities to pursue first by weighing their business impact against feasibility, cost, risk and data readiness.
- [Vector databases](https://scayl.ai/ai-implementation-glossary/vector-databases): Databases that store embeddings (numeric representations of meaning) and find the items most similar to a query, the usual search engine behind RAG.
- [Workflow automation](https://scayl.ai/ai-implementation-glossary/workflow-automation): Using software, increasingly AI, to carry out the steps of a business process with little or no manual work, from simple rules to AI-driven decisions.

## Data

- [Landscape data, JSON](https://scayl.ai/assets/landscape/landscape.public.json): every firm with its layer, headquarters, website, description, funding, deals and sources; plus the funding rounds and deals tables. v3 snapshot of 2026-10-20.
- [Landscape and benchmarks as plain text](https://scayl.ai/llms-full.txt): the same firms by layer, and the benchmark funnel, in one file.

## Optional

- [Terms and conditions](https://scayl.ai/terms): how the site, its tools and its research may be used.
- [Privacy and cookies](https://scayl.ai/privacy): what the site collects and the visitor's choices.