Integrations · Anthropic Claude

We ship production work on Claude. Not slides about Claude.

Anthropic Claude consulting for mid-market operations teams that have decided Claude is the model and need a partner who can ship it in production. Model selection across Sonnet, Opus, and Haiku. Deployment on Anthropic API direct or AWS Bedrock. MCP server architecture, eval harnesses, audit-grade observability. We've been building on Claude since the API opened and we use Claude Code on our own engineering work every day.

MODELS
Sonnet · Opus · Haiku
Selected per workload,
not per fashion cycle
DEPLOY
Anthropic API · Bedrock
Direct or in your AWS,
Vertex when warranted
PATTERNS
Extraction · agents · RAG
MCP servers, evals,
tool-use orchestration
BASED IN
Phoenix, AZ
Engineering across
two time zones
01. What we ship on Claude

Four patterns we ship most often. All in production, none of them slides.

Every pattern below is something we’ve shipped to production for a real client running real workloads. We don’t lead with what Claude is theoretically good at; we lead with what we’ve already built on it and what we’d build again.

01

Structured extraction from field documents

BOL parsing, invoice intake, inspection reports, contracts. Messy field document in, clean structured data out, human review where the risk warrants it. We ship the prompt design, the structured-output schema, the eval set that proves accuracy on your real document mix, and the integration that writes the extracted data into the system of record. Watch a real workflow get automated →

Sonnet primary
Schema + evals
Human-in-the-loop where it matters
02

Multi-step agent workflows

Workflows where Claude orchestrates a sequence of tool calls into your existing systems — ServiceNow, Salesforce, internal dispatch boards, your custom queue. We design the agent boundaries (what it decides, what it escalates, what it never touches), wire the orchestration through n8n or custom workers depending on the failure modes you care about, and ship eval harnesses that catch drift before users do.

Tool-use + MCP
n8n or custom workers
Bounded autonomy
03

Internal copilots over company data

RAG over your contracts, your historical RFPs, your inspection archive, your case files. Audit-grade citation, source-of-truth retention, and a UI an actual operator will use. We build the ingestion pipeline, the retrieval layer (typically Postgres + pgvector or a managed vector store, sized to the corpus), the citation surface, and the eval harness that catches when answers drift away from sources.

RAG + citations
Postgres + pgvector
Source-of-truth retention
04

Claude Code engineering augmentation

Helping internal engineering teams adopt Claude Code without the usual pitfalls. What to put in CLAUDE.md, how to wire it into existing CI and PR review, where it pays back and where it slows the team down. We use it ourselves daily on our own builds, so the advice is from inside the workflow, not from a slide deck.

Internal-engineering work
CLAUDE.md + hooks
CI + review wiring
02. How we work with Claude

The technical substance, not the marketing.

Model selection

Sonnet is the default. It handles the bulk of operational work — document extraction, multi-step agents, internal copilots — at a price point that lets you actually deploy at volume. Opus comes in for the workloads where reasoning depth genuinely changes the outcome (complex contract analysis, multi-step deduction over ambiguous source material, evals where every accuracy point compounds). Haiku is the right pick for high-volume classification or routing where latency and cost matter more than the marginal accuracy. We pick per workload, not per fashion cycle.

Deployment surfaces

Anthropic API direct is the simplest path and what we default to for new builds. AWS Bedrock is the primary path when the client needs data residency inside their AWS account, has a Bedrock commitment to spend down, or runs the rest of the stack on AWS. Google Vertex AI is supported but less common in our work; we’ll use it when the workload genuinely lives inside Google Workspace or BigQuery. The integration shape is consistent across surfaces; the differences are quota management, model availability, and IAM.

MCP server architecture

We build MCP servers when an agent needs to call into the same internal systems repeatedly from different surfaces (chat, agent runtime, Claude Code), and you’d otherwise be reimplementing the tool interface three times. We don’t build MCP servers for one-off integrations where a direct function call is fine. The decision lives in the audit, not in a tech-stack-by-default reflex.

Eval harness and observability

Every prompt-driven workflow ships with an eval set — typically 30 to 100 representative inputs with expected outputs (or expected shape, for free-form generation). The suite runs on every prompt change, every model-version bump, every change to the surrounding code. Observability layers on top — Sentry releases tied to deploys, structured logs into your existing aggregator, runbooks for every on-call scenario we can predict. The same operational governance substrate we run every production engagement on. Same approach we apply on our broader agentic AI work.

03. Reference work

Claude in production. Real operators, real data.

Two named references where Claude or a Claude-class model sits in production. DocuPaint and LoadQuest both run multi-year retainers with our team and surface the kind of operational depth a Claude integration actually requires — not pilot demos, not single-prompt POCs.

QC PLATFORM · INDUSTRIAL COATINGS · MULTI-YEAR

DocuPaint. AI-assisted inspection report generation inside a SaaS 200+ orgs run their work on

Structured report generation from field-captured inspection data, draft narratives reviewed by certified inspectors before submission, full audit-grade trail. Production scale, multi-tenant, AMPP-endorsed. The kind of constrained-AI work where the model writes the draft and the human signs the audit chain.

Read the case study →
FREIGHT BROKERAGE TMS · DISPATCH AUTOMATION · 4 YEARS

LoadQuest. Document extraction and dispatch automation across an 80+ carrier network

BOL intake from email, scan, and driver app. Structured extraction with human review on the edge cases. Dispatch automation for inbound RFPs, rate matching against historicals, and carrier payout processing. Built across the React Native + NestJS + Postgres stack we deploy for most operational AI work.

Read the case study →
04. How we engage

Three named engagement modes. Pick where you are.

Whether you’re scoping a first Claude build, mid-implementation on an agent system, or running production AI that needs to evolve. We have a fixed-scope entry point. Each one ships with a written deliverable and a defined timeline.

— DIAGNOSE

The Claude Diagnostic

From $25K · 2-3 weeks

We map the workloads end-to-end. Which fit Claude today, which fit GPT-4 or Gemini better, which are still too early for the LLM tier altogether. Output: a written 90-day plan with named integration points, model picks per workload, and a real estimate.

— BUILD

The Build Engagement

$80K-$300K · 4-12 weeks

The implementation. Agent design, model selection, MCP servers where warranted, eval harnesses, integration with your existing systems. Built and shipped to production. Code in your repo. Infrastructure in your cloud or ours, your choice.

— EMBED

The AI Retainer

Capped hours · Monthly

AI workloads need maintenance the way no other software does. Models update, prompts age, edge cases emerge. We retain a fractional engineering presence for monthly evals, drift reports, and the next workload your COO wants in production. Capped hours, monthly billing.

05. Frequently asked

The questions we get asked first.

Are you an Anthropic partner?

No formal partnership. We're an independent engineering studio that ships production work on Claude. We've been building on the Anthropic API since it opened, run Bedrock deployments for clients who need AWS data residency, and use Claude Code internally on our own engineering work every day. The lack of a partner badge means our model recommendations aren't bent toward partner-program incentives — if a workload fits GPT-4 or Gemini better, we'll say so and build it that way.

Why Claude over GPT-4 or Gemini?

Workload-dependent. We're model-agnostic and ship on all three. Claude tends to be our default for long-context document understanding (multi-document review, contract analysis, structured extraction from messy field paperwork), workflows where tool-use needs to be reliable, and any operation where the team values straightforward refusal behavior over confident bluffing. GPT-4o is often the right pick for high-volume, latency-sensitive workloads. Gemini wins when the workload lives natively inside Google Workspace or Vertex. The audit answers this for your specific workloads.

Do you work with Claude Code?

Yes, daily, internally. Most of our engineers use Claude Code as part of the day-to-day build loop. We've also helped clients evaluate it as an internal-engineering tool — what to put in CLAUDE.md, how to wire it into existing CI, where it pays back and where it doesn't. We don't sell Claude Code seats; we sell the implementation work around it.

Can you deploy on AWS Bedrock?

Yes. Bedrock is a primary deployment path for clients who need their data to stay inside their AWS account, who already have a Bedrock budget commitment, or who run the rest of their stack on AWS and don't want a separate Anthropic billing line. The integration shape is essentially the same as direct Anthropic API; the differences are mostly around quotas, model-version availability, and IAM.

What about data privacy and HIPAA?

We build HIPAA-ready architecture (BAA in place between client and provider, encrypted at rest and in transit, audit-grade event logs, no training on client data via Anthropic's default API terms). We're on a SOC 2 trajectory ourselves but not yet certified, and we'll say so directly rather than gloss it. For workloads with stricter residency or compliance constraints we typically deploy on Bedrock or Vertex inside the client's existing cloud account.

Do you build MCP servers?

Yes, when the integration pattern warrants it. MCP makes sense when an agent needs to call into the same internal systems repeatedly from different surfaces — chat, agent, IDE — and you'd otherwise be reimplementing the tool interface three times. It doesn't make sense for one-off integrations where a direct function call is fine. The audit calls out which integrations should be MCP servers and which shouldn't.

How fast can you start?

Audit kicks off within 2 weeks of contract signature. Build engagements start 2-4 weeks after the audit delivers, depending on team availability and how much architecture is settled in the audit. Retainers can start within a week once scope is agreed.

What if I don't know if Claude is the right model?

The audit answers that. We score each workflow against the model classes that could realistically solve it today, and we tell you honestly if GPT-4 or Gemini fits better for any specific workflow. We've turned down audit engagements when it was clear up front that the workloads were a GPT-4 fit and the client only wanted Claude — that's not us being precious, it's us not wanting to take fees for work we can't do honestly.

What does a Claude integration build cost?

Engagement-dependent. The diagnostic is a fixed fee — typically $25K for a Claude-specific deep audit (longer and more model-architecture-focused than our generic operations audit). Build engagements range from $80K to $300K depending on surface area, number of tool integrations, eval-harness scope, and whether MCP servers are part of the work. Retainers run monthly with capped hours. Every engagement starts with a written quote and a fixed deliverable.

Do you also do platform integration work alongside the AI?

Often, yes. Most Claude builds land inside an existing operational stack — Procore, Salesforce, NetSuite, custom dispatch boards, internal portals. We build the integration layer alongside the agent work because the two halves usually share data models. Our Procore work (see /integrations/procore) is the same engineering team applying the same approach.

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