Anthropic
Claude models combine long-context reasoning, tool use, and constitutional AI safety. Built for enterprise workflows where accuracy and auditability matter.
ClearView
AI built around safety by design
Anthropic trains the Claude family of models with constitutional AI: behaviour is shaped against a written set of principles rather than by human preference feedback alone. For systems whose decisions must be explained to auditors and risk teams, a model whose behaviour traces to stated principles is easier to account for.
Tool use lets Claude drive interfaces the application defines, extended thinking gives the model longer to work on hard problems before answering, and context windows of 200,000 tokens and above hold hundreds of pages at once. Prompt caching and the Batch API shape what it costs to run all of this at volume.
Adaca partners with both Anthropic and OpenAI and holds no house default; the model follows the use case. Claude also runs Adaca's own delivery flow, through a combination of Claude Code and proprietary tooling, so the patterns described here are in daily use on our own work.
Claude is the wrong choice where the task is generating images or speech: its API offers neither, and OpenAI's platform covers both. It is also the wrong tool for predictive modelling over structured data, where the better path is a trained model on a platform such as Google Vertex AI.
- Document Processing
- Code Review
- AI Agents
- Customer Support
Our Anthropic capabilities
Claude API & tool use
The Messages API carries multi-turn conversations, with images and documents in the same request as the text that discusses them. Tool use is the mechanism that matters most in production: Claude proposes a call against a typed schema, the application executes it and returns the result, and authority stays with the code throughout. Structured JSON output gives downstream systems a fixed shape to parse, and streaming keeps long responses usable in interactive surfaces. We wrap the integration in retries and timeouts from the first build.
- Messages API
- Tool use
- Streaming
Long-context processing
Context windows at this scale change how document work is engineered. An entire codebase, a legal document set or a financial report goes into one request, so analysis reads the full text rather than whichever fragments a chunking pipeline kept. When a corpus outgrows even a 200K window, retrieval narrows the input first and answers cite the passages they rest on. The design judgement is knowing when long context alone suffices and when a retrieval layer earns its complexity.
- 200K context
- Document analysis
- RAG
Prompt caching & cost efficiency
Most production prompts repeat: system instructions, tool definitions and reference material are identical from call to call. Prompt caching stores that static portion so only the new part of each request is processed in full, which changes the economics of long prompts at volume. The Batch API carries the non-urgent share of the load, overnight document runs and evaluation sweeps, without competing with interactive traffic. Model routing completes the discipline: each task runs on the lightest of Opus, Sonnet and Haiku that passes its evaluation.
- Prompt caching
- Batch API
- Model routing
Why Adaca for Anthropic?
Regulated deployment
Claude in regulated banking and healthcare with audit logging and PII redaction.
Deep Claude API experience
Extended thinking, tool use, prompt caching, and Batch API for structured multi-step workflows.
RAG for accuracy
Retrieval-augmented generation with vector stores, reranking, and citation extraction.
Production observability
Token counts, latency, tool invocations, and quality metrics traced for every call.
Full-stack AI teams
Next.js front-ends, Python orchestration, vector databases, and CI/CD. One team from prompt to production.
Multi-model evaluation
Benchmarking Claude against GPT-4 and Gemini on actual use cases with structured evaluation.
How We Staff It
A Claude build is mostly ordinary software engineering with a thin model layer on top, and it is staffed that way. The core is backend engineers in Python or TypeScript, the languages the Claude SDKs lead with, alongside one engineer who owns prompts, evaluations and tracing as versioned artefacts.
Claude work rarely stands alone; the same engagement usually covers the API layer, the data pipeline and the interface around the model. Teams draw on the breadth of Adaca's engineering pool for that surrounding work, and engineers keep the customer's working day.
Common Questions
Should we build on Claude or on OpenAI models?
Both go through the same evaluation: recorded cases from the workload, scored the same way, with operating cost read alongside quality. Whichever model passes for a given task takes that task, and one system often routes different tasks to different providers.
The comparison is kept repeatable, so it can be re-run whenever either platform changes materially, and the routing follows the evidence rather than the announcement.
Do we need a retrieval pipeline, or is Claude's context window enough?
Long context is enough when the material is bounded and stable: one contract set, one codebase, one report. Retrieval earns its place when the corpus is large, changes often or differs per user, because each request then needs a selection step in front of the model.
The two combine well, with retrieval choosing the input and long context keeping it generous. Anthropic ships no embedding model of its own, so the retrieval side pairs Claude with embeddings from another provider, commonly OpenAI.
Can Claude be used inside a regulated environment?
Yes, with the controls engineered around it. In the regulated deployments Adaca has built, the model receives redacted input, every exchange is logged for audit, and steps with consequences keep a human approval in front of them.
Claude is also available through AWS Bedrock and Google Cloud Vertex AI, which suits organisations that need model traffic to stay inside an existing cloud agreement.
If we standardise on Claude, how hard is a later switch?
A switch is a measured event rather than a rewrite, provided two disciplines hold from the start. Calls pass through a single seam instead of being scattered through the codebase, and every prompt carries its own evaluation suite, versioned per model, because prompt behaviour does not transfer unchanged between providers.
Switching then begins by running the existing evaluations against the candidate model, which prices the move on your own cases before any code changes. What remains is a routing change.
What makes a Claude application easy to maintain?
Treating the AI layer as code. Prompts live in version control, every change runs against an evaluation suite before it ships, and the rest of the application is ordinary TypeScript or Python maintained like any other service.
These are the disciplines Adaca applies to its own delivery flow; its measured results are on the Technology page.
What does an Adaca team for Claude work look like?
Small at the start. Engagements begin at one engineer, and additional roles follow the surrounding product work: pipelines, interfaces and integration rather than the model itself.
A shortlist usually reaches you within 48 hours of a brief, drawn from people Adaca already employs or already knows, with dedicated recruitment covering specialised gaps. Terms are three, six, nine or twelve months, chosen up front.