AI & Machine Learning
Claude
Anthropic’s Claude, put to work in production — reasoning, tool use and agents, built by a team that runs on Claude itself.
Overview
Claude is Anthropic’s family of large language models, reached through the Messages API and also available on AWS Bedrock and Google Vertex AI. It is best known for a few things: strong reasoning and coding, a large context window that lets you hand it whole documents and codebases, reliable tool use and function calling, and careful instruction-following. Anthropic’s safety work — the Constitutional AI approach — is not just a research talking point; it shows up as a model that is harder to push off-task and more predictable in the hands of people who are not prompt experts, which matters a great deal in regulated and enterprise settings.
We build with Claude first-hand — this consultancy runs on it day to day — so what we tell you about it comes from operating it, not from a vendor deck. The API gives you streaming responses, tool use, vision for images and documents, prompt caching to cut the cost of long, repeated context, and the agentic and computer-use capabilities that let a model take multi-step actions rather than just answer a question. Those are genuinely useful primitives, but they are primitives: a reliable product is the engineering you wrap around them, not the model call itself.
Our stance is deliberately multi-model. Claude is, in our experience, an excellent default for reasoning-heavy, coding, and agentic work, and we reach for it often. But we design systems so the model is a replaceable component behind a clean boundary, because model versions change, pricing shifts, and no single provider is the right answer for every task. You get the benefit of Claude’s strengths without quietly building a system you can never move off.
Best for — Reasoning-heavy, coding, and agentic products where a frontier model earns its cost — built by a team that adds the evals, guardrails and portability the model itself does not give you.
Why teams choose Claude
Frontier reasoning where it counts
Claude handles the tasks that defeat smaller models — dense documents, ambiguous instructions, multi-step reasoning, real code — with a quality that changes what the product can credibly promise. We put it exactly where that quality pays for itself and nowhere it does not.
Long context that removes plumbing
A large context window lets you pass whole contracts, transcripts or codebases in one call, so you spend less engineering effort chopping, summarising and stitching. Combined with prompt caching, long shared context becomes affordable to reuse across many requests.
Agentic behaviour you can actually rely on
Reliable tool use and instruction-following are what separate an agent that works from one that quietly derails. Claude follows a tool schema, stays on-task, and asks for the right function at the right time far more consistently than weaker models — which is most of the battle in agent engineering.
Why businesses choose Claude
- You need the best available reasoning or coding quality for the core of a product, and a mediocre model would make the whole thing not worth building.
- You are building agents or tool-using workflows and want the model that fails least often at staying on-task across many steps.
- You value Anthropic’s safety posture and clear enterprise terms, and you want the option to run inside Bedrock or Vertex rather than sending data to a separate API.
- You want a team that uses Claude in anger every day, and that will build you a portable system rather than a dependency you cannot escape.
What we build with Claude
The capabilities this technology is genuinely strong at — and what we most often build with it.
The Messages API and streaming
We build on Claude’s Messages API — structured turns, system prompts, and token-by-token streaming so interfaces feel responsive rather than stalling on a long generation. Streaming also lets us start acting on partial output and cancel work early when a user changes course.
Tool use and function calling
Claude calls the tools you define against a JSON schema — querying a database, hitting an API, running a calculation — and we design those tool contracts tightly so the model does real work through code instead of guessing. Well-shaped tools are how you turn a chatty model into a dependable system.
Prompt caching for long context
When many requests share a large, stable prefix — a system prompt, a knowledge base, a codebase — prompt caching lets Claude reuse it instead of re-processing it every call. Used deliberately, this cuts both latency and cost on exactly the long-context workloads Claude is good at.
Vision and document understanding
Claude reads images and documents, not just text, so we can build workflows over scanned forms, screenshots, diagrams and PDFs — extracting structure from input that was never machine-readable to begin with.
Agentic and computer-use capability
Claude can plan and take multi-step actions, including operating a computer interface, which opens up genuine automation rather than single-shot answers. We treat this power with respect — scoping what an agent may touch and building in checkpoints — because an agent that can act is an agent that can act wrongly.
Deployment through Bedrock and Vertex
Beyond the direct Anthropic API, Claude runs on AWS Bedrock and Google Vertex AI. When your data governance or existing cloud commitments demand it, we deploy Claude inside that boundary so model calls stay within infrastructure and contracts you already control.
Use cases
Document analysis and extraction
Reading long, unstructured documents — contracts, reports, correspondence, forms — and turning them into structured, checkable data. Long context and vision let Claude take the whole thing at once instead of losing the thread across chunks.
Coding and developer tooling
Code generation, review, migration and explanation inside internal tools and developer workflows, where Claude’s coding strength does real work against real repositories rather than toy snippets.
Retrieval-grounded assistants
Assistants that answer from your own knowledge — policies, documentation, product data — by retrieving the right material and having Claude reason over it, with citations, so answers are grounded rather than invented.
Agents and workflow automation
Multi-step processes where the model orchestrates tools to triage a request, gather information, draft an action and hand off to a human — automating the reasoning while keeping consequential steps under control.
When Claude is the right choice
- You have a genuinely hard language or reasoning task — analysing long documents, writing or reviewing code, multi-step research, structured extraction from messy input — where model quality is the difference between a useful product and a demo. This is Claude’s home ground.
- You are building an agent or tool-using workflow, where the model must call functions, orchestrate steps, and stay on-task across a long interaction. Claude’s reliable tool use and instruction-following make this materially less fragile than it is with weaker models.
- You operate in a regulated or enterprise context and care about predictable, safety-conscious behaviour and clear data-handling terms — and you can meet Claude through Bedrock or Vertex to keep it inside a cloud boundary you already trust.
- Wrong for: simple, high-volume, latency-critical classification or routing where a small cheap model — or no model at all — would do the job for a fraction of the cost. Putting a frontier model behind every trivial decision is a way to burn money and add latency for no gain.
- Wrong for: anything that must be exactly deterministic or provably correct — pricing calculations, safety interlocks, legal or financial figures. Claude is a probabilistic system that can be confidently wrong; those jobs belong in ordinary code, with the model kept well away from the final answer.
Claude: pros and cons
Strengths
- Excellent reasoning and coding, and careful instruction-following that holds up in the hands of non-expert users, not just prompt engineers.
- Large context windows and prompt caching make long-document and long-conversation workloads practical and cost-manageable.
- Reliable tool use, streaming, vision and computer-use give you the primitives for agents and document workflows out of the box.
- Available through the Anthropic API, AWS Bedrock and Google Vertex, so you can meet it inside an existing cloud and compliance boundary.
Trade-offs
- It is a hosted, third-party model: your prompts leave your systems, which raises real data-handling and compliance questions. These are manageable — enterprise terms, no-training commitments, and Bedrock or Vertex deployment all help — but they must be designed for, not assumed away.
- Frontier quality costs frontier money. At scale, token spend is a first-order concern, and a naive design that sends everything to the largest model will be far more expensive than it needs to be.
- You depend on a vendor whose models change: a new version can shift behaviour, deprecate an old one, or quietly alter an output you relied on. Without version pinning and regression evals, that dependency bites you at the worst time.
- Like every LLM, Claude is non-deterministic and can hallucinate — state something wrong with total fluency. Anything that matters needs evaluation, guardrails, and a way to catch and contain a bad answer before a user acts on it.
Architecture
A product built on Claude is mostly the system around the model, and that is where we spend our engineering. For knowledge-grounded applications we build a retrieval layer — chunking, embeddings, a vector database, and a retrieval step that puts the right context in front of the model — so answers come from your data rather than the model’s memory. Retrieval quality, not model choice, is usually what decides whether a grounded assistant is trustworthy.
For anything that acts, we design tool orchestration explicitly: a set of well-scoped tools with tight schemas, a controller that decides when the model may call them, and clear boundaries on what an agent is permitted to touch. Around all of it we put guardrails — input validation, output checking, and human checkpoints on consequential steps — because a model that can take action needs the same containment you would give any untrusted component.
Crucially, we treat the model as a replaceable part behind a clean interface. Prompts, tools and evaluation live in our code, not welded to one vendor’s SDK, so Claude can be the default for reasoning while a smaller or cheaper model handles routing and classification, and a different provider can be swapped in for a specific task. This multi-model design is what keeps you out of lock-in and lets you route each job to the model that actually suits it.
Performance
Latency and cost are the two performance realities of building on Claude, and they trade against each other. Frontier models are slower and dearer than small ones, so the work is in not using more model than the task needs: routing simple requests to a lighter model or to plain code, reserving the strongest model for the reasoning that genuinely requires it, and streaming responses so the experience feels fast even when total generation is not instant.
The largest structural wins come from context discipline. Prompt caching makes long, repeated context cheap to reuse; tight retrieval means we send the model what it needs and not a wall of irrelevant text; and batching or parallelising independent calls keeps throughput up. We measure token spend per interaction as a first-class metric, because at scale it is a running cost you feel every month, and a naive design can cost several times what a considered one does for the same output.
Security
The central fact is that Claude is a hosted, third-party model: prompts and their context leave your systems. We design around that honestly. Sensitive data is minimised, redacted or tokenised before it reaches the model where it is not needed; enterprise terms and Anthropic’s no-training-on-your-data commitments are read and relied on deliberately; and where governance demands the data never leave a boundary you control, we deploy Claude through AWS Bedrock or Google Vertex so calls stay inside your existing cloud and contracts.
Beyond data handling, an LLM is an untrusted input and an untrusted output. We treat prompt injection as a real threat — especially in retrieval and agent systems where the model reads content it did not author — and constrain what tools an agent can call and what those tools can do, so a manipulated prompt cannot escalate into a damaging action. Output is validated before it is trusted, secrets never sit in prompts, and consequential steps keep a human in the loop rather than a model acting unchecked.
Scalability
Scaling on Claude is less about your own servers — the model is hosted — and more about cost, rate limits and reliability under load. We build for graceful behaviour when the provider is slow or throttling: retries with backoff, sensible timeouts, queuing for non-interactive work, and fallbacks so a rate limit degrades the experience rather than breaking it. Meeting Claude through Bedrock or Vertex can also give more predictable capacity within an enterprise agreement.
The design that scales cheaply is the multi-model one. As volume grows, the difference between routing every request to the largest model and routing each to the smallest that can do the job is enormous. Because we keep the model behind a clean boundary, that routing is a configuration decision rather than a rewrite — and version pinning plus a standing evaluation suite mean we can adopt a newer or cheaper model as it appears without gambling that it still behaves the way your product depends on.
Claude integrations & ecosystem
The technologies we most often pair with it — each links to how we work with it.
How we work
We start by deciding whether you need a frontier model at all, and where. Then we build the smallest thing that proves the hard part — usually the retrieval or the tool orchestration, not the model call — and we stand up an evaluation harness alongside it from the beginning. Without evals you cannot tell whether a prompt change, or a new model version, made things better or quietly worse; with them, every change is measured rather than argued about.
From there we harden: guardrails, cost and latency budgets, observability on what the model is actually doing, and the portability boundary that keeps the model replaceable. Because we operate what we build — and because we run on Claude ourselves — the decisions are made by the people who will live with the token bill, the failure modes and the maintenance. You get an honest account of where Claude is worth its cost and where a cheaper model or plain code would serve you better.
The service behind it
Delivered throughAI DevelopmentWhat we build with Claude
The disciplines this technology most often shows up in — from a first build to taking over and stabilising an existing one.
How we deliver
- 01
Discover
We map the system, the constraints and the business it serves — including the parts nobody documented.
Architecture brief
- 02
Architect
Decisions get made, written down and defended before a line of production code exists.
Decision records
- 03
Build
Short cycles against working software. You see progress in the product, not in a status deck.
Shipping increments
- 04
Operate
Monitoring, incident response and iteration. The system is alive, so the engagement is too.
Runbooks & SLOs
Industries we use Claude in
Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.
Also in AI & Machine Learning
Related terms
Why teams choose us for Claude
We build on Claude ourselves
This is not a technology we read about — we use Claude every day to do real work, so our advice on where it shines and where it disappoints is first-hand and blunt, not repeated from Anthropic’s marketing.
Senior engineers, evals from day one
A reliable LLM product is disciplined engineering, not prompt tinkering. The people building yours are senior, and they stand up evaluation and guardrails from the start — because that is what separates a system you can trust from a demo that impresses once and fails in production.
Portable by design, honest about cost
We keep the model replaceable and route work to the cheapest thing that does the job, so you get Claude’s strengths without lock-in or a runaway token bill. If a frontier model is the wrong tool for part of your problem, we will tell you.
Typical timeline
- 01
Discovery and evaluation design
One to two weeks defining the task, deciding whether and where Claude fits, and agreeing how success will be measured — the eval criteria come before the build, not after.
- 02
Proof on the hard part
Two to three weeks building the riskiest slice — usually retrieval quality or tool orchestration — and measuring it against the evals, so we prove the approach works before scaling it.
- 03
Build and harden
Iterative delivery of the full workflow with guardrails, cost and latency budgets, observability, and the portability boundary that keeps the model replaceable.
- 04
Launch and operate
Production rollout with monitoring on model behaviour and spend, a standing regression suite for new model versions, and handover so your team can run and extend it.
How pricing works
- Discovery and feasibility for AI work — a fixed-price assessment of whether Claude is the right tool, where it fits, and what it will realistically cost to run, before you commit to a build.
- Fixed-scope builds for a well-defined product — a grounded assistant, an extraction pipeline, an agent — quoted once the retrieval, tools and evaluation approach are understood.
- Monthly senior engagement for ongoing AI product work, where prompts, evals and model choices evolve with the product and you want continuity rather than a one-off deliverable.
- Note that model usage — the token cost of running Claude — is a separate, ongoing operating expense we design to minimise, distinct from our engineering fee.
Hire Claude engineers
Need Claude capacity on your own team? We embed named senior engineers into your existing team — reporting to your leads, working in your rituals — so you add capacity without a hiring cycle.
Hire Claude engineersCommon questions
Is our data safe if we send it to Claude?
Claude is a hosted model, so prompts leave your systems — that is the honest starting point. It is manageable: Anthropic offers enterprise terms and commitments not to train on your data, and you can run Claude through AWS Bedrock or Google Vertex to keep calls inside a cloud boundary you already control. On top of that we minimise, redact or tokenise sensitive data before it ever reaches the model. Safe, but by design rather than by assumption.
Will Claude make things up?
It can. Like every large language model, Claude is probabilistic and will occasionally state something wrong with complete confidence. We reduce this by grounding answers in your own data through retrieval, asking for citations, and validating output before it is trusted — and we keep the model away from anything that must be exactly correct, like financial or legal figures, which belong in ordinary code. The goal is to catch and contain a bad answer before a user acts on it.
Should we use Claude or another model?
Often both. In our experience Claude is an excellent default for reasoning, coding and agentic work, and we reach for it there. But a smaller, cheaper model — or no model at all — is the right answer for simple, high-volume tasks like routing or classification. We design systems so the model sits behind a clean boundary, which lets us route each job to the model that suits it and swap providers per task without a rewrite.
What does it cost to run Claude in production?
There are two costs: our engineering fee and the ongoing token cost of running the model, which is a separate operating expense. Frontier models cost frontier money, so at scale a naive design that sends everything to the largest model can cost several times what a considered one does. We treat token spend as a first-class metric — using prompt caching, tight retrieval and model routing — so you pay for the reasoning you need and not for waste.
What happens when Anthropic releases a new model version?
It is a real risk if you have not planned for it — a new version can shift behaviour or deprecate an old one, and an unguarded system can break quietly. We plan for it from the start by pinning the model version and keeping a standing evaluation suite, so when a newer or cheaper model appears we can measure whether it still behaves the way your product depends on before adopting it. Upgrades become a tested decision rather than a gamble.
Building on Claude?
A technical conversation with the engineers who would do the work. If we are not the right fit, we will say so on the call.