Build with Claude's frontier reasoning and coding capabilities. We integrate Anthropic's Claude 4.5 models for applications requiring reliable, sophisticated AI.
Anthropic's Claude 4.5 family represents the frontier of AI reasoning and coding capability. Claude Opus 4.5 leads industry benchmarks for software engineering and agentic tasks, while the model family offers options for different performance and cost requirements. We build applications using Claude for organisations that need reliable, sophisticated AI.
The Claude 4.5 family offers three tiers:
Claude Opus 4.5 is Anthropic's most capable model, particularly strong for coding, agents, and computer use. It is designed for the hardest tasks where quality and reliability matter most.
Claude Sonnet 4.5 balances capability and efficiency. It is often a good default for production applications that need strong performance without using the most heavyweight model for every step.
Claude Haiku 4.5 is optimised for speed. It can be a good choice for lightweight assistants and higher-volume steps when paired with appropriate validation.
All models support hybrid reasoning, offering instant responses or extended thinking based on task complexity.
Claude has characteristics that distinguish it from alternatives:
Coding excellence: Claude Opus 4.5 is considered the world's best coding model, with sustained performance on long-horizon, complex software engineering tasks.
Agentic capability: Strong tool use and instruction following make Claude exceptionally effective for agents that need to work independently.
Computer use: Claude can interact with graphical interfaces, navigating websites and applications to complete tasks.
Extended context: Support for 200,000 tokens as standard, with million-token windows available through beta features.
Safety orientation: Anthropic's focus on AI safety means Claude is designed to be helpful while refusing harmful requests appropriately.
Honest uncertainty: Claude acknowledges when it does not know something rather than fabricating confident-sounding answers.
Claude works well for:
Software development: Building features, refactoring code, debugging complex issues. Claude Code enables agentic coding from the terminal.
Complex analysis: Reviewing documents, evaluating arguments, synthesising information across sources.
Customer service: Handling nuanced enquiries where understanding context and responding appropriately matters.
Agent development: Building systems that take action, use tools, and complete multi-step workflows.
Content generation: Producing thoughtful, well-structured content that meets detailed specifications.
Computer-based automation: Tasks requiring interaction with web applications and interfaces.
Claude is most useful when paired with your systems and constraints: retrieval over your knowledge, tool integrations, and clear policies for what to do when it cannot answer safely.
Claude is available through:
Anthropic API: Direct access with full feature support.
Amazon Bedrock: Claude within AWS infrastructure with AWS security and billing.
Google Cloud Vertex AI: Claude as part of Google Cloud's model offering.
Claude.ai and Claude apps: For direct interaction and Claude Code development.
The right choice depends on your existing cloud environment and requirements.
We help organisations use Claude effectively:
Application development: Building chatbots, agents, and tools powered by Claude models.
Prompt engineering: Designing instructions that produce reliable, high-quality outputs.
Agent development: Creating systems that leverage Claude's agentic capabilities with appropriate controls.
Integration: Connecting Claude to your business systems and data sources.
Computer use implementation: Deploying Claude to automate browser and application-based workflows.
Working with Claude requires understanding its characteristics:
Model selection matters. Use the right tier for each step so you get quality where you need it without over-engineering the whole workflow.
Extended thinking is a trade-off. It can improve results on harder tasks, but it typically increases latency and resource usage.
Different steps benefit from different models. Many production systems use multiple models (or multiple tiers) for different parts of a workflow.
Refusal behaviour is intentional. Applications should account for appropriate boundaries and provide safe alternatives when the model refuses.
We design applications that work well within these parameters.
Use these prompts to explore fit, boundaries, and architecture decisions.
“Which parts of our workflow should use a high-capability model versus a faster model, and why?”
“What guardrails and approval steps do we need to keep this safe and reliable in production?”
“What evaluation plan will prove this works: test cases, metrics, and failure modes?”
Complex reasoning, careful analysis, and coding-heavy workflows—especially when tasks require sustained focus and good instruction-following.
It depends on your environment and governance needs. If you’re AWS-first or GCP-first, using the managed option can simplify controls and operations.
Not always. Many use cases work well with structured prompts and retrieval. Agents are useful when work is multi-step and tool-driven.
We ground responses in approved sources, validate outputs where possible, and design safe fallbacks when confidence is low.
We set a measurable quality bar, test against realistic scenarios, add monitoring, and tune model choice and prompting.
