AI provider
Anthropic Claude integration
Claude is often chosen where the material is long and the instructions are detailed: contracts, reports, policies, and research. We build with it, and we test it against other providers on your own documents before recommending it.
- 1,200+ clients helped
- Building since 2010
- 15 services, one team
- 50+ technologies
At a glance
What it is
Claude is a family of language models from Anthropic that other software can call, often chosen for working with long documents, following detailed instructions, and using tools.
Problems it solves
Teams need an assistant or document feature that handles long, detailed material accurately, and are unsure which model and set-up suits it.
Who it suits
Teams working with contracts, reports, or large bodies of text, and leaders comparing AI providers.
How to choose
Compare Claude with other providers on your own documents and tasks. Claude is often a strong candidate for long, detailed material.
What it means
A model you call from your own software
Claude is a family of language models made by Anthropic. Software calls them through an API (a set of agreed requests that programs can make), sending text or files and receiving a response, and they can also be set up to use tools such as search or record lookups.
Providers differ in how much text a model can take in at once, how it follows detailed instructions, how it handles images and documents, and what it costs. Those differences matter more in practice than any league table.
That is why we recommend testing on your own material. A provider that suits one company's documents may not suit another's.
What DECHY can do
Working with Claude
Assess
- Testing Claude against your documents and tasks.
- Comparing it with other providers.
Build
- Document review and question answering with sources.
- Assistants connected to your systems.
Control
- Limits on tools, data, and spend.
- Human review where a mistake would be costly.
Maintain
- Re-testing when models change.
- Reports on quality and cost.
Where it fits
What are you asking the model to do?
Three common situations, and how we approach each.
What is the main task?
Choose this when
Contracts, policies, reports, or research papers, often with many pages.
- What you get
- A set-up where the model reads the whole document, answers specific questions, and cites where each answer came from.
- Keep in mind
- Long input costs more, and details can still be missed. We test on real examples.
Choose this when
Looking up records, drafting replies, and updating systems for a team.
- What you get
- An assistant with narrow permissions, a log of every step, and approval where it matters.
- Keep in mind
- Tool use needs limits on what the assistant may change.
Choose this when
Drafts that must follow a house style or detailed rules.
- What you get
- Drafts that follow written guidance, reviewed by a person before use.
- Keep in mind
- Style is easier to check than facts, so check both.
Worth knowing
Test before you commit to a provider
Public comparisons of AI models change every few months and rarely match a particular company's documents. A day spent testing three providers on fifty of your real examples is worth more than any ranking.
We run that test as a first step, and the examples stay yours, so you can repeat it whenever the market changes.
Alternatives
How we compare providers
| Question | Why it matters | How we test it |
|---|---|---|
| Quality on your material | General rankings rarely match your documents. | A set of real examples with expected answers, scored the same way for each provider. |
| Handling of long input | Some tasks need whole documents, not extracts. | Questions whose answers sit deep in a long file. |
| Data terms | Your obligations may limit where data can go. | Reading the provider's current terms against your rules. |
| Cost at your volume | Prices differ, and small models can be enough. | Estimating from real document sizes and request counts. |
| Flexibility | You may want to switch later. | Keeping the provider behind a clear boundary. |
Technical depth
How we build with Anthropic Claude
For your developers and IT lead: our Anthropic Claude choices on prompting and structure, tools and agents, and retrieval and cost, and the reasoning behind each.
We write instructions as clear, separate sections, ask for structured output validated against a schema, and keep prompts under version control with the tests that guard them.
- Tool definitions with narrow scope, such as read-only record lookups by default.
- Step limits and approval gates before any action that changes data.
- Full tracing of tool calls for review.
- For very large collections, retrieval of relevant passages with LangChain or custom code, instead of sending everything.
- Caching repeated context, and choosing smaller models for simpler steps.
Common questions
Questions about Claude
Is Claude better than the other models?
Not in general. Which is best depends on the task and the material. We test on your examples, and often the answer is a mix of models.
Can Claude read our whole document set?
Within limits. Large collections are usually searched first, so the model reads only the relevant parts. We design this around your volumes.
Is our data safe with a model provider?
We review the provider's current data terms against your obligations, send only what the task needs, and avoid sending sensitive data unless it is required.
Can we switch provider later?
Yes, if the design allows it, which is why we keep the provider behind a clear boundary and keep your tests.
Have a different question?
Tell us what you are weighing up and we will answer it plainly.
Ask a questionAlready a client? Email support@dechytech.com
Send us a sample of the material
Describe the documents and the task. We will test Claude and other providers on your examples and recommend one on the results.