Claude is Anthropic's model family — strong reasoning, long context, and a design bias toward safe, predictable output that enterprise teams tend to trust with sensitive data. We build the integration layer around it: chat interfaces, autonomous agents, document and data processing pipelines, wired into your existing stack with the privacy and reliability a production business needs.
Calling the Claude API is an afternoon's work. Building a reliable, cost-controlled, secure integration around it is not — that's where engineering judgment matters.
We match Claude model tiers to the job — fast, cheap models for high-volume classification, frontier models for complex reasoning — instead of defaulting to the most expensive option everywhere.
Retention settings, data residency, and access scoping configured deliberately — not left on defaults — so Claude fits inside your existing compliance posture.
From a single function call to a full agent loop with tool use and Anthropic's Model Context Protocol (MCP) — scoped to what the task actually needs, not maximal complexity by default.
Prompt caching, streaming, and batch processing applied where they save real money — most teams calling the API directly leave this on the table.
Retry logic, rate-limit handling, and graceful fallbacks so a model hiccup doesn't become a customer-facing outage.
Scoped to your product — from a single API call to a multi-agent system, not a fixed package.
Customer-facing or internal chat interfaces built on Claude, with context management and streaming responses.
Multi-step agents that use tools, call your APIs, and complete tasks autonomously — scoped with clear boundaries and human checkpoints where they matter.
Extraction, summarization, and structured-output pipelines for contracts, reports, and unstructured business data.
Connecting Claude to your internal tools and data sources via Anthropic's Model Context Protocol, or building your own MCP server.
Moving existing LLM integrations onto Claude, or upgrading between Claude model versions without breaking production behavior.
Reviewing an existing Claude integration for token waste, missed caching opportunities, and latency issues.
It depends on the task — Claude tends to be a strong fit for long-context reasoning, document-heavy workflows, and cases where enterprises want tighter, more predictable data-handling controls. For some products the other is a better fit. We'll evaluate your specific use case in a scoping call rather than default to one vendor.
We configure data retention and usage settings deliberately at the account level, avoid sending unnecessary sensitive fields in prompts, and can architect around zero-retention or regional requirements where your compliance needs call for it.
Yes. We build agentic workflows using Claude's tool-use capabilities and Anthropic's Model Context Protocol (MCP) to connect the model to your internal systems and data sources — scoped to the specific task rather than an open-ended agent with unbounded access.
It depends on scope — a single-purpose feature like document extraction costs far less than a multi-agent system with tool use across several internal services. We give a fixed estimate after a free scoping call rather than a generic range.
Tell us what you're building — chat, agents, or document processing — and we'll scope an approach and a fixed estimate.