Agency · Anthropic · Claude

The Anthropic agency.Claude, in production.

Anthropic gives you Claude and an API key, then a blank notebook that produces a slick demo and not much else. We build the real thing on the right Claude model, ground it on your data with RAG, and wire it into your product, n8n and Make.

No spam, ever. One-click unsubscribe.

★★★★★Verified Trustpilot reviews · AI, automation & growth agency

ActiveCampaignActiveCampaignAdaloAdaloAdCreative.aiAdCreative.aiAhrefAhrefAirtableAirtableAllo (The Mobile First Company)Allo (The Mobile First Company)AnthropicAnthropicApifyApifyApollo.ioApollo.ioAttioAttioAttio Implementation PartnerAttio Implementation PartnerBase44Base44BaserowBaserowBrevoBrevoBright DataBright DataBrowse AIBrowse AIBubbleBubbleCaptainDataCaptainDataChatGPTChatGPTClaudeClaudeClaude CodeClaude CodeClaude CoworkClaude CoworkClaude DesignClaude DesignClayClayClickupClickupCursorCursorDeepSeekDeepSeekDustDustElevenLabsElevenLabsFilloutFilloutFlutterflowFlutterflowFolk CRMFolk CRMFolk Implementation PartnerFolk Implementation PartnerFreepik SpacesFreepik SpacesGammaGammaGeminiGemini
What we do

An Anthropic agency builds you a product, not a demo.

Anyone can call the Claude API once. Building an agent your team trusts, grounding it on your data, and wiring it into your stack so it survives production is a different job. Here are the four things we own.

Method · 4 stages

We build with Claude like a system, not a demo.

Most Claude projects die the same way: a notebook demo that wowed the room, then nothing, because it was never grounded on real data and never wired into anything. So we treat it like infrastructure: built on the right model, grounded with RAG and tool use, shipped into the stack, handed to a team that knows how to run it.

  • Audit · map the use case, your data, and whether Claude is even the right model for it
  • Build · the agent, copilot or pipeline on the right model (Opus, Sonnet or Haiku)
  • Ground · RAG on your data plus tool use, so answers cite sources and take real actions
  • Ship · wire it into your stack, add prompt caching, monitoring and the error handling
Walk me through the method
Differentiator · no badge

An automation agency, not a model reseller.

We don't sell an Anthropic partner tier. We come from automation and AI, so we see Claude for what it is: a strong reasoning engine that has to plug into the rest of the stack to be worth anything. That's exactly what's missing when a project ends at a demo with no grounding and no integration.

  • We come from automation and AI, not from reselling a model. Claude is a node in your stack, not the whole pitch.
  • Model-agnostic: we pick Claude when it fits (reasoning, long context, safety, tool use) and say so honestly when GPT or an open model is the better call.
  • Honest on fit: if an off-the-shelf tool already does the job, we'll tell you instead of building you something custom you didn't need.
  • No partner badge to wave. We're judged on whether the thing ships and holds up in production, not on a tier.
Show me a typical build
What we build

Claude at the core, your systems around it.

We build the parts that actually decide whether an AI feature ships, then connect them to the tools your team already uses. Here's what a real build covers.

  • Build

    Claude API integration

    We wire the Anthropic API into your product or back-office with the SDK, streaming, structured outputs and retries, so Claude runs in production and not just in a demo notebook.

  • Build

    AI agents (Agent SDK / Managed Agents)

    We build agents that take real actions: the Agent SDK for custom flows, Managed Agents (sandboxed code execution, checkpointing, scoped permissions, tracing) when a task has to run unattended.

  • Build

    RAG on your data

    We ground Claude on your docs, tickets and catalogue: retrieval, chunking, embeddings and citations, so answers point to a real source instead of a plausible guess.

  • Build

    Prompt engineering & caching

    We tune prompts on your actual cases and turn on prompt caching to cut token cost and latency on the parts that repeat, so quality goes up while the bill goes down.

  • Build

    Tool use & MCP connectors

    We give Claude tools: function calling to hit your APIs, and MCP connectors so it reaches your systems through a clean protocol instead of brittle one-off glue.

  • Build

    Automation (n8n / Make + Claude)

    n8n, Make, Zapier: Claude inside the flows you already run, classifying, drafting, extracting, with the error handling and monitoring that keeps it alive after launch.

Free audit · 60 minutes

We scope your use case, you leave with a plan.

Before quoting anything, we take 60 minutes to look at your use case, your data and your stack. You leave with an honest read on whether Claude fits, which model to use, and what to build first. Zero pitch, just an operator's take on what's worth building and what isn't.

  • An honest read on whether Claude is the right model for you
  • What to build first, and on Opus, Sonnet or Haiku
  • How to ground it on your data and wire it into your stack
  • A frank take on when an off-the-shelf tool is the smarter call

No spam, ever. One-click unsubscribe.

Or send your brief instead
Our approach

How we run a Claude build.

Five steps, in order. We don't ship before it works on your real cases, we don't hand over a black box nobody can run, and your team owns it at the end. Each step has a deliverable and you sign off before we move on.

  1. Step 1 · Use-case & data audit

    Map the use case and check Claude is the right call

    We sit down with the people who own the problem, support, ops, product, and look at what you actually want: a support copilot, an assistant over your docs, a document extractor, a coding agent. We check your data, your stack and your constraints (latency, cost, privacy). You leave with an honest read on whether Claude fits, which model to use, and where an existing tool would be cheaper than anything custom.

  2. Step 2 · Build on the right model

    Build the agent, copilot or pipeline that fits

    We build on the Claude model family: Opus for the hard reasoning, Sonnet for the daily workhorse, Haiku when latency and cost matter most. We set up the Anthropic SDK (Python or TypeScript), streaming, structured outputs and prompt caching from the start. An operator on your side validates the behaviour on real cases before we wire anything into production.

  3. Step 3 · Ground it on your data

    RAG and tool use so answers are real, not guessed

    We ground Claude on your own knowledge: retrieval, chunking, embeddings, and citations so every answer points to a source. We add tool use and MCP connectors so the agent can actually read your systems and take actions, not just chat. The goal is answers your team can defend to a customer, not a confident hallucination you have to apologise for later.

  4. Step 4 · Ship & integrate

    Wire it into your stack with monitoring on top

    We integrate the build into your product, back-office or automation layer (n8n, Make or the API), turn on prompt caching to cut cost, and add the Agent SDK or Managed Agents when a task runs unattended. Every flow ships with its error handling, tracing and monitoring, not bolted on after the first incident. It runs in production, not in a demo tab.

  5. Step 5 · Train & hand over

    Your team runs it, you don't need us on retainer

    We train the people who'll own the build: how to read what the model does, where it's strong, where to keep a human in the loop. The setup ships with a short playbook. If you'd rather go deeper, our Claude training covers the build end to end. If you want us on call for what scales next, we talk about that separately instead of locking you in.

Proof · what the teams say

We're judged on what ships and holds up.

No partner badge to display, so we lead with what matters: feedback from the teams whose Claude build we shipped, and whether it kept working after we left. Our Trustpilot reviews come from those operators, not from a marketing deck.

  • The build ships with a playbook your team can run
  • Grounded on your data, signed off before it goes live
  • Wired into the stack with monitoring, not a demo tab
  • Trustpilot reviews come from the teams we built for
Talk to the team
FAQ · Anthropic agency 2026

The questions we get asked on repeat.

  • What does an Anthropic agency actually do?
    An Anthropic agency builds AI solutions on Claude and ships them into your stack, instead of handing you an API key and a demo. We build agents, chatbots and copilots on the right Claude model (Opus, Sonnet or Haiku), wire the Anthropic API into your product or back-office, ground it on your data with RAG so answers cite real sources, and add tool use, prompt caching and monitoring. The point is something your team uses in production, not a proof of concept that impresses once and dies.
  • How much does it cost to build with Claude with you?
    It depends on scope: a single support copilot is nothing like a full RAG assistant over your docs with tool use and agents on top. We don't throw out a flat package. We start with a free 60-minute audit to frame the use case, then quote a fixed scope. The Anthropic API usage itself you pay them directly, by token, and we set up prompt caching to keep that bill down. We'll also tell you upfront if an off-the-shelf tool does the job cheaper than anything custom.
  • Claude or GPT: which model should we use?
    Depends on the job, and we're honest about it. Claude is strong on reasoning, long context, careful instruction-following, tool use and safety, which is why we reach for it on document-heavy and agent work. But we're model-agnostic: if GPT or an open model fits your case better on cost, latency or a specific capability, we'll say so instead of forcing Claude to win the comparison. We don't have a partner badge to defend, so the recommendation follows your use case, not our incentives.
  • Can you ground Claude on our own data?
    Yes, that's the core of most builds. We set up RAG over your docs, tickets, product catalogue or knowledge base: retrieval, chunking, embeddings, and citations so every answer points to a real source. Claude's long context window helps, but retrieval done properly matters more than stuffing everything into the prompt. The result is an assistant your team can defend to a customer, not one that invents a confident answer you then have to walk back.
  • What are Claude Managed Agents and do we need them?
    Managed Agents (in public beta in 2026) let Claude run tasks on its own with sandboxed code execution, checkpointing, credential management, scoped permissions and end-to-end tracing. You need them when a task has to run unattended and safely, not for a simple chatbot. For custom flows we use the Agent SDK (Python and TypeScript) instead. We'll tell you during the audit which one fits, or whether a plain API call is all you actually need.
  • Can you integrate Claude with the rest of our tools?
    Yes, that's where we add the most value. We wire Claude into your product and back-office through the API, and into your automation layer (n8n, Make, Zapier) so it classifies, drafts and extracts inside flows you already run. We use tool use and MCP connectors so the model reaches your systems through a clean protocol instead of brittle glue. Monitoring and error handling ship with it, so it keeps working after launch instead of breaking quietly.
  • How long does it take to build something with Claude?
    For a scoped build (one copilot or assistant, grounded on your data), count 2 to 4 weeks: audit and a first working version early, then grounding, tool use and the integration. A full multi-agent system with Managed Agents and monitoring runs longer. We split into batches so you get a usable first version fast, rather than waiting months for everything to be perfect before anyone touches it.
  • Do you train our team or just hand over the build?
    Both, and the training is the point. A build nobody understands dies after the first edge case. We train the people who'll own it: how to read what the model does, where it's strong, where to keep a human in the loop, how to update the prompts and the data. The setup ships with a short playbook. If you want to go deeper, we run a Claude training that covers the build end to end.
Build with Claude

Stop fighting a blank notebook. Get a product.

A 60-minute audit, your use case scoped, a build plan that fits your data and your stack. If your team can run it in-house after we ship, we'll hand you the playbook. If we're the right fit, we handle it.

Book the free 60-min auditSee the agency
or just drop your email

No spam. Unsubscribe anytime.