Claude AI Integration & Development
Anthropic gave you the model. We give you the product.
We design, build, and ship Claude-powered apps, agents, and workflows — prompt engineering, RAG, MCP servers, evaluation harnesses, and the production cost guardrails that keep your API bill predictable.
Claude Implementation That Ships
Anthropic gives you API access, a strong model, and excellent documentation. What it doesn’t give you is production engineering. The prompt versioning. The evaluation harness. The RAG pipeline. The cost controls. The human-in-the-loop checkpoints. The MCP integrations. The observability stack. That’s the work that turns a Claude API key into a product your team will actually use, and it’s the work that decides whether your pilot gets promoted to production or quietly stalls out.
That’s the work we do. We’ve spent 14+ years shipping production software and have delivered 730+ projects across financial services, healthcare, insurance, legal, and education. We bring that experience to every Claude AI engagement, whether you’re building net-new on top of Sonnet, Opus, or Haiku, plumbing a Claude integration into the systems your team already runs on, or hardening an existing build for production. We pick the right Claude model for each path, design the infrastructure around it, and stay on after launch when the real work starts.
From AI Strategy to Production Deployment
Whether you’re exploring AI for the first time or scaling proven use cases, we help you validate ideas, build responsibly, and implement solutions your team can actually use.
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Claude-Powered Product Development
Net-new applications built with Claude doing the reasoning: customer support copilots, document processing pipelines, internal agents, vertical SaaS workflows. We’ve shipped enough LLM-backed software to know that picking the right model is the easy part. We choose between Sonnet, Haiku, and Opus based on the actual workload, then engineer the infrastructure that holds it all up in production.
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Claude API Integration & RAG Architecture
Most teams don’t need a new product. They need Claude wired into the systems they already run on: CRM, knowledge base, internal docs, ticketing, and ERP. We design the retrieval layer (Pinecone, Weaviate, pgvector), the prompt orchestration, and the evaluation framework up front, so what we ship is testable from day one and stays trustworthy as your data grows.
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MCP Server Development
Model Context Protocol is how Claude reaches into your business, your tools, your data, and your workflows as first-class capabilities. We build custom MCP servers for proprietary APIs, internal databases, and the existing software your team relies on. The result: Claude can take action on your business, not just answer questions about it.
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Prompt Engineering & Evaluation
Most failed Claude pilots fail at the prompt layer. Brittle templates. No eval harness. Silent regressions occur when the model updates. We treat prompts like production code: versioned, A/B tested, regression-checked, and observable. So when something drifts, you find out before your users do, and you have the data to fix it.
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Cost, Safety & Production Operations
This is the work that separates a working demo from software your team can rely on. Token budgeting and cost dashboards. Content moderation and refusal handling. Prompt-injection defenses. Audit logging. SOC 2 and HIPAA-aligned data handling. Claude API key rotation. Unglamorous, essential, and the reason your AI investment doesn’t blow up six months in.
Problems We Solve
The hardest part of building with Claude isn't Claude. It's everything around it: the data plumbing, the prompts that don't break in production, the cost guardrails, the integration with your existing software, and the evaluation that tells you the model is still doing its job after the next API release. We've shipped LLM-backed software into real businesses, and we know where it tends to break.
How We Build with Claude
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Use Case Validation & Model Selection
For two weeks, we sit down with your team, map the workflow you want Claude to handle, and audit the data it will need access to. Then we stress-test the idea, because the most useful thing we can tell you sometimes is that this isn’t a Claude problem at all. A smaller fine-tuned model or a structured workflow might do the job better and cheaper. You leave with a clear recommendation, a scoped proof of concept, and a realistic timeline. No hype, no upsell.
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PoC Build with Real Data
Two to four weeks to a working prototype, built against your real workflow and your real data. This isn’t a Claude API demo. It’s a functional pipeline with prompts, retrieval, tool use, and evaluation built in. Your team then uses it. We measure it against the success criteria we agreed on up front. Then we tell you honestly whether it’s ready to scale, what needs more work, or whether the use case needs a rethink before you invest further.
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Production Build, Launch & Operate
This is where most projects either ship or stall. We handle the full production engineering: architecture, integrations, MCP servers where needed, prompt versioning, evaluation harnesses, cost monitoring, observability, and SOC 2 or HIPAA-aligned data handling. Then we stay on. Claude models update. Prompts drift. New use cases surface. Same team start to finish, no handoffs to a delivery org you’ve never met. The people who scoped it are the people who run it.
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2
weeks
From use case to validated Claude proof of concept
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$2M
projected annual profit increase
from AI-enhanced workflows (for an actuarial firm)
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10x
faster service turnaround
on AI-enhanced workflows (for our actuarial client)
Featured AI Projects
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AI-Powered Knowledge Assistant for Prosci
Prosci needed a better way for users to access and use its large library of research, training content, and tools. Information was spread across systems and was difficult to search. We built an AI-powered assistant using OpenAI that pulls from Prosci’s content and delivers clear, relevant answers. This improved how users find information and learn from it. The platform now supports over 10,000 users across 50+ countries, with faster access to the content they need.
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AI-Enhanced Self-Service Hub for Bullhorn
Bullhorn needed to improve efficiency and reduce support overhead across its platform. LaunchPad Lab built an AI-enhanced self-service hub that helps users find answers, navigate workflows, and complete tasks faster without relying on support teams. The result was improved efficiency and a better user experience across the platform.
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AI Workflows for an Actuarial Firm
An actuarial firm was relying on spreadsheets and manual reconciliation, with reporting cycles taking up to 60 days. LaunchPad Lab built an AI-enhanced platform that automates data intake, reconciliation, and reporting. The result was a 10x faster turnaround and a projected $2M annual profit increase.
Frequently Asked Questions
What is Claude AI development?
Claude AI development is the work of designing, building, and operationalizing software that uses Anthropic’s Claude language models as a core component. That includes net-new applications built around Claude as the reasoning engine, integrations that connect Claude to your existing systems, and the production engineering around it: prompt versioning, retrieval pipelines, evaluation harnesses, cost guardrails, MCP servers, and observability. The goal is the same as any other software project: ship something reliable, secure, and useful that your team can trust in production.
Which Claude model should I use — Sonnet, Opus, or Haiku?
It depends on what the model is doing in your workflow, not which one is newest. Sonnet (currently Sonnet 4.6) is the right default for most production reasoning, balancing quality and cost. Haiku (currently Haiku 4.5) handles high-volume, cost-sensitive paths like retrieval, classification, or first-pass triage. Opus (currently Opus 4.7) is best for complex agentic work and multi-step reasoning where quality matters more than cost. Most production systems use more than one, with each model handling the slice of the workload it’s best suited for.
How is Claude different from OpenAI's GPT models for product development?
Claude and GPT are both strong production-grade language models, and the right choice depends on the use case, not brand loyalty. Claude has a stronger safety and alignment posture and longer context handling that suits document-heavy and regulated workloads well. GPT has a broader tooling ecosystem and a wider library of pre-built integrations. We pick per project based on what the workload actually needs, and we’re happy to tell you when GPT is the better fit, even on a Claude-focused engagement.
What is MCP (Model Context Protocol) and when do I need a custom MCP server?
MCP is Anthropic’s open standard for connecting Claude to external tools and data sources. Think of it as the protocol that lets Claude read, write, and act on your systems as first-class capabilities, not just answer questions about them. You need a custom MCP server when Claude needs to interact with proprietary APIs, internal databases, or internal software that doesn’t have an off-the-shelf connector. For standard SaaS tools, an existing MCP server is usually enough. For your business-specific systems, custom is typically the right call.
How do you handle Claude API costs in production?
Claude API costs are managed through engineering choices, not just rate limits. We design with per-conversation cost ceilings, model routing (Haiku for retrieval and triage, Sonnet for reasoning, Opus only when the workload genuinely needs it), prompt caching to cut repeated token costs, output length controls, and evaluation-driven prompt compression. We also build the cost dashboards your team needs to actually see what’s happening. Most teams find that thoughtful routing and caching cut their API spend significantly without compromising the quality of the output.
Can you work with Claude in regulated industries (healthcare, finance, legal)?
Yes. Regulated industries are where we do a lot of our AI work, and they’re a strong fit for Claude given Anthropic’s safety and alignment focus. We’ve built HIPAA-compliant AI document processing for healthcare insurance, AI-driven workflows for consumer lending, and integrated AI into Salesforce environments where data handling and audit requirements are not optional. Our production builds include audit logging, content moderation, prompt-injection defenses, human-in-the-loop checkpoints, and SOC 2 or HIPAA-aligned data handling, depending on what your regulatory environment calls for.
Do you only build with Claude, or also with OpenAI, Gemini, and open-source models?
We build with whichever model is the best fit for the job. Most production AI systems use more than one model, and we pick per task. Claude is often the right answer, but sometimes a GPT model, a Gemini model, or a smaller open-source model is the better choice. We’ve shipped production systems on OpenAI, custom ML, and Salesforce-native AI alongside Claude work. The honest answer is that brand loyalty makes for bad architecture. Picking the right tool for each step makes for software your team can rely on.
Start building with Claude
Tell us what you're trying to build. Whether you're scoping a Claude pilot, stuck on the cost or evaluation problem, or ready to ship something to production, a 30-minute call with one of our senior engineers will tell you more than a dozen vendor pitches. No deck. No pipeline pressure. Just an honest conversation about what's worth building, what isn't, and what the path forward looks like.