Your team stays on product and design. You approve the plan — and a context-aware Claude fetches the real data, picks up the discussion from Slack and your tools, builds it, reviews its own PR, and hands you a clean one. Securely. On a flat-rate subscription. Not bound to any vendor — if you run it, we plug it in.
The challenges of running Claude in your org — and how Plugwright solves them, end to end.
Run Claude through Cursor or Copilot and you get a throttled fraction of it, an unpredictable token bill, and shallow integration. So your team burns weeks researching how to wire it in safely — and burns tokens exploring how to prompt it — instead of building product.
Your engineers focus on the product and design calls only humans should make. Once you approve the plan, a context-aware Claude runs the build → self-review → test loop and brings back a finished, reviewable PR.
The plans, security model, prompt library, skills, and engineers trained on all of it already exist. You skip the weeks of trial-and-error — and the tokens burned exploring — and get a working end-to-end solution fast.
Metered API and credit pools cap you fast or bill $500–1,500+/dev/month. A flat subscription seat gives effectively-unlimited, full-strength Claude — predictable cost that gets better per token the harder your team uses it.
Any stack — chat, source, CI/CD, cloud, data, monitoring — wired in behind a sealed envelope: no long-lived keys, audited egress, the AI never touches prod directly (it goes through your existing merge gate).
The moment you sign off on a plan, Claude pulls the real data, reads the discussion from Slack and your tools, remembers it, makes the next decisions on that context, reviews its own PR — and only then asks for yours. You stay in control at the points that matter; you don't babysit the mechanics.
Your team decides what to build and why. Hand Claude a ticket, a design doc, or a Slack thread — the intent, not the implementation.
YouClaude reads the request, the repos, and your guides and proposes a scoped plan — files, blast radius, rollback. Nothing proceeds until you approve or redirect.
Your checkpointOn approval it becomes context-aware: fetches the real data from your systems, reads the relevant code, and pulls the discussion from Slack/tickets — then stores that context so every later step is grounded, not guessed.
ClaudeImplements against the plan on a branch, following your conventions and security rules from memory. New context that lands mid-task (a Slack reply, a changed spec) is captured and folded into the next decision.
ClaudeClaude reviews its own PR first — correctness, tests, security, your style — and fixes what it can as ready-to-apply suggestion commits, not a wall of comments.
ClaudeYou get a clean, already-self-reviewed PR. You spend attention on the big judgments — product intent, architecture — not mechanics. Approve, or send notes it folds in.
Your checkpointRuns the suite plus staged/shadow tests; on merge, your existing CI/CD pipeline deploys. Claude never touches production directly — promotion stays human-gated.
Your gateWatches your dashboards for regressions, rolls back on a breach, and writes what it learned back to memory so the next task starts smarter.
ClaudeThe point: your engineers stop researching prompts, wiring integrations, and chasing context — and go back to product and design. Claude handles the loop in between, on context it actually fetched and remembered, with a self-reviewed PR waiting for you.
If a tool has an API — or even just a web UI — Claude can drive it. These are examples; your exact stack slots in the same way, scoped by least-privilege access.
Drive the agent from chat — and let it capture the discussion as context for deciding the next steps.
Reads repos and your guides, opens PRs, and self-reviews — returning fixed code as suggestion commits, not just comments.
Reads tickets for intent and auto-records design decisions, keeping the trail in your tracker without manual write-ups.
Triggers your existing pipeline on merge. The AI never touches prod directly — promotion stays your gate.
Operates your cloud via short-lived, least-privilege credentials — no static keys anywhere in the deployment.
Queries the real data to ground its work, scoped read-only by default, with the query and lineage attached for reproducibility.
Reads specs and prior context, writes reports and design write-ups, and keeps knowledge where your team already looks.
Watches deploys for regressions, correlates logs, and triggers rollback when a guardrail breaks.
Prototypes analyses and shares runnable cells server-side, without anything leaving the secured host.
Grounds data work in real schemas, ownership, and lineage instead of guesses.
Plug in any MCP server to add capabilities — the workspace discovers and uses them behind the same security boundary.
A headless browser lets the agent operate internal or third-party tools that have no API at all.
Your team doesn't start from a blank prompt box, burning tokens to discover what works. We bring battle-tested prompt templates and a library of skills, pre-loaded and tuned to your stack — and engineers trained to use them.
The agent loop lives server-side, so a long task, a test run, or a context-gathering pass keeps running after every human disconnects — and replays missed output the moment a client reconnects. Humans attach from anywhere; the engineering state and the gathered context live on the server.
A single server process hosts persistent Claude sessions and runs the plan-to-monitor lifecycle. Tools keep executing — and context keeps being gathered — after every human disconnects; you reattach to a session already in flight.
The opinionation layer. A skill library wraps your tools so the agent calls vetted commands; a headless browser / MCP covers anything without an API; hooks enforce house rules; two-tier memory keeps conventions and fetched context intact across long tasks.
Code, data, and tokens never leave the server; auth is SSO with no static keys; all outbound traffic passes a default-deny audited gateway where raw tokens are never exposed; and prod is reachable only through your existing merge gate.
Two costs disappear: the engineering time your team would burn researching, wiring, and hardening this themselves, and the token spend — both the exploratory waste of figuring out prompts, and the gap between metered tokens and a flat subscription. Drag the slider to see the compute side.
Heavy — all-day autonomous agentic engineering
Compute-cost comparison on current public pricing. Metered = raw API token spend per dev. IDE path = a representative paid seat plus token overage once the bundled credit pool is spent. Flat = the right Claude plan for the usage — about $20/seat (Claude Pro, incl. Claude Code) at light, ~$100/seat (Max 5× or Team-Premium) at medium/heavy — full-strength Claude at a predictable per-seat price. Illustrative estimates, not a quote — and separate from our engagement fee.
Standing up a secure, integrated, self-reviewing agent workspace is months of internal research and trial-and-error. We've done it; your engineers don't.
Teams burn huge budgets just exploring how to prompt and wire things. Proven prompts, skills, and trained engineers mean far fewer wasted tokens from day one.
Cheaper compute per heavy dev. Flat-subscription Claude vs. metered tokens or credit-pool overage — same full-strength model, predictable cost.
Surprise overage. Credit pools equal the subscription price, then bill overage. A flat seat has no pool to drain — the seat price is the final price.
| How you could run Claude | Compute / dev / month | Model access | Throughput | Verdict |
|---|---|---|---|---|
| Cursor Teams | $40–120 base + overage; ~$400–1,000+ for a heavy dev | Frontier models gated behind a paid mode; routing favors cheaper models | Credit pool = subscription $; drains in days, then metered overage | Predictable until real agentic work starts — then metered tokens with a markup |
| GitHub Copilot | $19–39 base + overage; heavy dev commonly $500–1,000+ | Usage-based; frontier tokens billed vs a small allotment | Allotment covers autocomplete, not all-day agent loops | Cheapest base, but the included credit is a rounding error for heavy use |
| Raw API, metered | $500–1,500+ for a heavy agentic dev | Full-strength Claude — but every token is a line item | Truly unlimited — and that's the problem; cost scales with output | Full access, zero cost ceiling — the most expensive way to run heavy agents |
| Flat Claude subscription deployed & secured by Plugwright | A flat per-seat subscription, regardless of how hard it's used | Full-strength Claude, unthrottled — the real model, not a fallback | Effectively-unlimited at a fixed price; heavier use = better $/token | Predictable, dramatically cheaper for heavy users — integrated & secured for you |
Add the engineering time your team gets back — no more researching prompts, wiring integrations, or building guardrails — to the compute savings above, and that's the real return. Our engagement fee depends on your stack and scope; we'll size budget, plan, and timeline with you on a call.
The AI works inside a sealed envelope, not on your laptop, and it can't over-act. This is built for the conversation with your security team — we'd rather start with a review than a demo. See our Trust Center →
The brain, working trees, gathered context, every credential, and all tool execution live on a hardened always-on server. A lost laptop exposes a session view — not your codebase or keys — and you revoke it by killing one session.
Zero static cloud keys or service-account secrets. The agent uses short-lived, auto-rotated credentials scoped to your cloud (AWS, GCP, or Azure); human access is SSO-gated, so de-provisioning in your IdP cuts access everywhere.
All outbound traffic passes a default-deny domain allowlist. Tokens live in a vault behind a PKCE exchange — neither human nor AI ever holds a raw value; the gateway injects it at request time. Every call is logged.
The AI has no prod credentials and no prod network path. Every change lands as a PR; promotion happens only when it's merged and your existing CI/CD pipeline (Jenkins, GitHub Actions, GitLab CI — whatever you run) deploys it.
The agent's access is scoped to exactly what the work needs, capped by a permission boundary in your cloud's IAM. Even a fully compromised agent or a prompt-injection can't escalate beyond the ceiling you set and version-control.
Every tool call, file edit, egress call, credential use, and lifecycle transition is logged with actor, timestamp, and context. Sessions persist server-side, so you can reconstruct exactly what the agent did and on whose approval.
Because the R&D came from a real production deployment, you skip the failure modes we already hit.
We don't hand you a tool and wish you luck. We configure it against your stack, then train your team to run it themselves — so adoption is fast and sticks. How long it takes depends on your stack and scope; we'll put a realistic plan to you on a call.
We map your stack, identity provider, repos, and prod-change path, then stand up the always-on brain with the security envelope first — SSO-only access, the egress / PKCE vault with allowlist + audit, and least-privilege access. Security by design, not a retrofit.
We wire in your chat, source, tickets, CI/CD, cloud, data, and monitoring — whatever you run — plus a headless browser / MCP for anything without an API. We run a verification matrix end-to-end so every integration is proven before anyone relies on it.
We install the lifecycle hooks, settings, and two-tier memory, load the prompt-template library and workflow playbooks tuned to your domain, then run live training cohorts. Engineers leave the room shipping real work, not watching demos.
Teams run real tickets through the full loop while we tune approvals and the allowlist against your actual traffic, then flip on full access. We hand over runbooks and a documented edge-case library so your platform team owns day-2 without us.
How to hand off intent and approve plans · how the context-fetch & memory keep the agent grounded · reviewing self-reviewed PRs fast (fixed code, not comments) · prompting with the template library, skills & memory · shipping safely inside the security envelope · scheduling recurring agent work.
Our engineers are trained specifically on this workspace — the architecture, the security model, the prompts and skills. They do the integration and the enablement, so your team gets a working, secure, end-to-end setup without spending its own cycles discovering all of it.
Once Claude is live and proving value in your org, the natural next step is joining the Anthropic Partner Network — so you can build on, resell, or co-sell Claude. We guide you through qualifying and applying, using your live Plugwright deployment as the proof.
We map your offering to the partner tiers and prepare a strong application — architecture, security posture, and evidence of real production Claude usage.
Your secured, always-on Plugwright workspace is exactly the kind of production Claude usage partner programs want to see. We package it as your evidence.
We ready your team to build on the Claude Developer Platform and to co-sell — with the delivery and security practices partners are expected to have.
We keep your setup aligned with Anthropic's evolving partner requirements and new model releases, so your status stays current.
Partner status is granted by Anthropic — we provide preparation and advisory support, not a guarantee. Ask us about partner enablement →
Every engagement runs on your Claude subscription, your accounts, your infrastructure — and you own the workspace at the end. Budget, plan, and timeline are scoped to your stack on a call; no fixed packages, because no two stacks are the same.
A fintech data-engineering org was running heavy agentic work through credit-pooled tooling — hitting model throttles and unpredictable token bills, with no clean way to give the AI deep stack access without handing out long-lived keys. We installed an always-on brain on a server in their environment, with thin chat, IDE, web, CLI & scheduler clients, a full skill library, a headless browser, two-tier memory, and the complete security envelope. The agent fetched real data, captured context from chat and tickets, built and self-reviewed PRs, and shipped only through their existing CI/CD merge gate. Engineers went back to design and product on flat Claude seats — deeply-integrated, secure, full-strength engineering without the throttling or the metered-bill surprises.
Tell us your stack and what's slowing you down. We'll show you the engineering time and token spend you'll get back, and scope budget, plan, and timeline together — no fixed packages, every org is different.
We deploy on your Claude subscription, your accounts, your infrastructure. We'll scope budget, plan, and timeline on the call — and we're happy to start with a security review instead of a demo.