Your Claude implementation partner, from pilot to production

FullStack builds on Claude every day — our engineers, our products, our delivery. We bring the same discipline to your Claude rollout: scoped use cases, evals from day one, and a system your team can run without us.

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Built for real-world AI

FullStack is an AI engineering firm that implements Anthropic's Claude in production for US companies.

We deliver Claude Code across engineering teams, Claude-powered agents and applications, MCP integrations into existing systems, and the evaluation and governance layer that keeps them auditable.

We build on Claude internally before we recommend it, which is why our advice on where Claude fits, and where it doesn't, comes from operating experience rather than a partner deck.

We'll map where Claude fits your workflows, and where it doesn't.
You'll leave the first call with a scoped use case and a baseline to measure it against.
We'll show you what we run on Claude ourselves, including what broke.

How FullStack works with OpenAI

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We run on Claude

Claude is one of our primary development partners. Our certified engineers not only use it to develop high quality and compliant code, they also use it to power our spec-to-ticket pipeline, as a brainstorming partner, for creating accelerated technical specs, managing epics and Jira tickets without a manual rewrite.

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We build client systems on Claude

When Claude is the right model for the job, we design and ship the whole system on it (agents, intelligent applications, MCP connectors into your real tools) and we deploy it wherever your compliance posture requires: the Claude API, Amazon Bedrock, or Google Vertex AI.

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We build the Claude skills, then hand them over

Our engineers train on Claude continuously. We focus on getting results: we provide role-specific training, help you configure Claude Code, and measure success by actual performance rather than just seat licenses.

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We invest ahead of the roadmap

We commit engineering time to Claude capabilities before clients ask for them, which is how our teams were already running agentic workflows in production when the enterprise demand arrived.

Success stories of Claudein FullStack and our clients' projects.

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AI Call Auditor automates 99% of compliance reviews

A regulatory compliance firm partnered with FullStack on a system that reviews calls for potential SEC violations and scores accuracy and confidence on every transcript. Reviewers went from reading every transcript to adjudicating the 1% the system flags — an estimated 5,500 labor hours and $232,000 a year of review capacity returned to the team.

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Claude is our grading and evaluation partner for our asynchronous technical interviews

FullStack's vetting pipeline scores and prioritizes candidate applications automatically, and Claude now grades the large majority of asynchronous candidate interviews — replacing a legacy in-house machine learning model and generating the written feedback summary with it. The interview a client watches is still the interview a human reviewed.

Spec-to-ticket, without the manual rewrite

FullStack's Spec Deck workflow converts early-stage brainstorming sessions into engineering requirements and generates the technical specs, epics and bug tickets in Jira directly. The pipeline is agentic end to end; an engineer approves the output rather than retyping it.

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We invest ahead of the roadmap

We commit engineering time to Claude capabilities before clients ask for them, which is how our teams were already running agentic workflows in production when the enterprise demand arrived.

Prove it on your codebase before you commit to a rollout

Every Claude engagement starts with a fixed-fee assessment or a paid pilot, run on your systems and your data. You see the result before you scope a build.

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A working pilot in two to four weeks*

One use case, instrumented, with an eval set built on your data — not a demo on ours.

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Measured from a Week 0 baseline

We record what the workflow costs in time and money before Claude touches it. Without that number, nothing afterwards is provable.

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Start with one workflow, not a platform

Claude Code across one team, or one agent in one process. The rollout compounds from something that already works.

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Your team runs it after we leave

Configurations, evals and runbooks are handed over and documented. If your engineers can't operate it without us, we haven't finished.

*These are typical time estimates and actual times may differ based on project complexity and scope.
honest limits

Where Claude is the wrong answer

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Claude is not always the right model, and we will tell you when it isn't.

For high-volume, latency-sensitive, non-interactive workloads, a smaller task-specific model usually wins on cost and speed. For workloads that are already inside a Microsoft-centric estate, the deployment path — not the model — is often the deciding factor. For anything where the data cannot leave your perimeter under any circumstances, the answer may be open weights on your own infrastructure, and we will say so even though it is a different engagement for us.

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Owning the weights is rarely the cost saving people expect.

Self-hosting a frontier-class open model runs roughly 10–11× more per token than the hosted version of the same weights. Owning the control plane — gateway, routing, caching, guardrails, evals, cost attribution — is cheap, reversible and where the savings actually live. We'll tell you which side of that line your workload sits on.

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What we don't do.

We don't resell licenses and we aren't a reseller of Anthropic products. We don't run your security review, your procurement, or your legal sign-off — though the assessment output is written to survive all three.

Explore FullStack's Claude implementation services

From a single Claude Code pilot to a governed agent estate, FullStack designs, builds and operates Claude systems on the infrastructure you already approve. Each service stands alone; they compound when combined, because they're built as one delivery system rather than four vendors meeting at a seam.

  • Claude Code rollout across engineering teams — configuration, hooks, permissions, CI/CD integration
  • Agent development on the Claude Agent SDK, with bounded autonomy and human checkpoints
  • Deployment on the Claude API, Amazon Bedrock, or Google Vertex AI to match your data-residency posture
  • Claude Code enablement for non-engineering roles
  • MCP server and custom connector development against your real systems
  • Evaluation harnesses and golden-dataset regression suites
  • Token economics: prompt caching, context strategy, model routing across Haiku, Sonnet and Opus tiers
  • Permission-aware knowledge retrieval over your existing wikis, drives and tools
  • Agent identity, least-privilege scoping, adversarial testing and audit trails

Partner with FullStack and put Claude to work where it pays back

Enterprise Partnerships

Claude inside a regulated, multi-cloud estate

For organizations with data that can't leave the perimeter and agents scaling across AWS, GCP and Databricks, we deploy Claude through Bedrock or Vertex and build one governance layer that holds across all of it.
Mid-Market Solutions

Claude in production without an AI platform team

For teams with real AI spend and nobody whose job is the AI platform, we scope one workflow, ship it, prove it against a baseline, and train your engineers to run it.

Common questions about working with OpenAI

Is FullStack an Anthropic partner?

FullStack builds on Claude across our own engineering and internal products, and implements Claude for client systems. We are not currently claiming a formal partner tier, and we'd rather say that than imply one.

What does a Claude implementation actually involve?

Four things, in order: picking a use case narrow enough to finish, measuring what it costs today, building it with an eval set on your data, and handing over the configuration and runbook. The model work is the smallest part. Integration, evaluation and adoption are where the time goes.

Can you deploy Claude without sending our data to Anthropic?

Claude can run through Amazon Bedrock or Google Vertex AI inside your existing cloud agreements and data-residency boundaries, which is how most regulated clients deploy. If the requirement is that no data leaves your infrastructure at all, that is an open-weights conversation and a different architecture.

How do you roll out Claude Code across an engineering team?

Start with one team and a real repository, not a company-wide license. Configuration, permissions and CI integration first; then measure against a Week 0 baseline on the metrics that already matter to you. Rollout follows evidence, and non-engineering roles usually come next.

How long before we see something working?

Two to four weeks for a scoped pilot with a working eval set. Longer if the integration surface is unusual or access approvals are slow — and access approvals are the most common cause of delay, not engineering.

Who owns the system when the engagement ends?

You do — code, configurations, evals, prompts and runbooks. Capability transfer is written into the engagement rather than offered afterwards, because a partner who leaves you more dependent has sold you the wrong thing.

Do you also work with OpenAI?

Yes. FullStack builds on both, which is why we can tell you which one fits a given workload. See our OpenAI implementation partner page.

What does a Claude engagement cost?

It depends on the scope, and we publish rates rather than hiding them. The assessment is fixed-fee, so the first number you see is the only one you're committing to. Rates and vetting are visible before you sign.

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Start with one Claude workflow you can measure

Bring us the process that's slowest or most expensive today. We'll scope it, price the assessment, and tell you honestly whether Claude is the right tool for it.