Custom AI
Custom AI solutions: how to decide what to build
A plain guide for leaders who are asked to approve a custom AI project. The design advice comes from Anthropic's own engineering article and documentation as read on 10 November 2026; the buying advice is Kriv AI's own view.
Custom AI solutions are AI systems built around one organisation's own task, data and rules, rather than bought as a general product. Anthropic's advice for building with language models is to find the simplest solution possible and add complexity only when needed. It says optimising a single model call with retrieval and examples is usually enough for many applications. Its documentation also says to define success criteria and test against them first, and that not every failing result is best fixed by changing the prompt. Our view: define the task and the test before you commission a build. Sources read 10 November 2026.
plain answer
Custom AI solutions: what are they?
A custom AI solution is an AI system designed for one organisation's task, using that organisation's data, rules and systems, instead of a general product used as it comes. It can be as small as a well-written prompt with a few examples, or as large as an agent connected to several business systems.
Anthropic's engineering article says that when building with language models it recommends finding the simplest solution possible and only increasing complexity when needed, which might mean not building an agentic system at all. It says that for many applications, optimising single model calls with retrieval and in-context examples is usually enough.
Source: Anthropic, Building effective AI agents (https://www.anthropic.com/research/building-effective-agents), published 19 December 2024, read 10 November 2026. The page notes that much of the tooling it describes has changed since publication.
how to
What should you define before a custom AI build?
Anthropic's prompt engineering overview assumes you already have a clear definition of success criteria for the use case, some ways to test against those criteria empirically, and a first draft prompt. It says to establish those first.
Our recommendation for a buyer is to write down five things before any contract: the task in one sentence, the test that tells you whether the output is good enough, what data the system may see, which decisions stay with a named person, and who owns the system after hand-over. A supplier that cannot work from these is guessing.
Source: Anthropic, Prompt engineering overview (https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/overview), read 10 November 2026.
comparison
Prompt, workflow or agent: which does your task need?
Anthropic describes workflows as systems where models and tools are orchestrated through predefined code paths, and agents as systems where the model directs its own process and tool use.
Anthropic's documentation also says not every success criterion or failing test is best solved by prompt engineering, and gives the example that latency and cost can sometimes be improved more easily by choosing a different model. Our reading: the cheapest fix is often not the custom build, so ask a supplier to show why a simpler option fails your test.
Sources: Anthropic, Building effective AI agents (https://www.anthropic.com/research/building-effective-agents) and Anthropic, Prompt engineering overview (https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/overview), both read 10 November 2026.
kriv role
Where Kriv helps
Kriv AI designs and builds governed AI solutions and trains the teams who use them. We start from the task and the test, agree what the system may see and which decisions a person keeps, then build and test it with your staff.
Kriv did not write the sources cited on this page. Scope and price are agreed after a short discovery call, and your own team keeps ownership of the process the solution supports.
limits
What this page could not confirm
This page rests on one Anthropic article and one Anthropic documentation page, as read on 10 November 2026. Both come from a model provider, and the article is dated December 2024. We did not compare custom AI suppliers, review any supplier's work, or test a build. Fine-tuning a model is a separate option that this page does not cover; see our fine-tuning page for that.
Straight answers
Frequently asked questions about Custom AI solutions: how to decide what to build
What are custom AI solutions?
They are AI systems built around one organisation's own task, data and rules rather than a general product used as it comes. They range from a tuned prompt to an agent connected to business systems.
Do I need a custom AI build, or will a prompt do?
Anthropic recommends the simplest solution possible and says optimising a single model call with retrieval and examples is usually enough for many applications (read 10 November 2026). Test a simple version first.
What should I define before commissioning custom AI?
Anthropic says to start with clear success criteria, a way to test against them and a first draft prompt (read 10 November 2026). We add: what data the system may see, which decisions stay with a person, and who owns it afterwards.
Is custom AI the same as fine-tuning a model?
No. Fine-tuning is one option among several. Many custom solutions use prompts, retrieval and workflows without changing the model. Our fine-tuning page covers that option.
How much do custom AI solutions cost?
This page does not quote prices. Kriv AI scopes and prices work after a short discovery call.
Talk to the team that would do the work
Bring your requirements to a working session with the person who'll actually deliver.
Book a Discovery Call