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Production AI in weeks, built by a senior European AI Pod.

A small, senior team that ships your first production AI agent in four to six weeks — then keeps shipping a new production use case every month. Fixed price. Tokens included. No catalog lock-in.

Book a free AI Pod Fit Call

Four stages. One Pod. A published price.

The AI Pod is our productized way of delivering AI. You start with a 30-minute call, we scope together, your Pod ships a production agent in six weeks, and then the same team stays on as a standing capability — releasing a new use case every three to four weeks.

Every stage has a fixed shape, a fixed price, and a defined hand-off to the next one. Tokens, tooling, and evaluation infrastructure are included in the price through 2026. Regulated engagements in healthcare and financial services add a compliance review stream; Pilot pricing for those starts at €75,000.

Four stages. One Pod. A published price.

Every stage has a fixed shape, a fixed price, and a defined hand-off to the next one.

Stage 1 — AI Pod Fit Call

A qualifying conversation with a Netguru AI Architect. Duration: 30 minutes. Price: Free. Outcome: One-page Fit Memo with recommended next step.

Stage 2 — AI Pod Discovery Sprint

Structured intake, data and systems mapping, architecture draft. Duration: 1 week. Price: €12,000 (credited against Pilot). Outcome: Pilot SOW with fixed scope and definition of done. Optional.

Stage 3 — AI Pod Pilot

A 2–3 person Pod ships one production agent or AI workflow. Duration: 4–6 weeks. Price: From €60,000. Outcome: Live agent + evaluation harness + runbook.

Stage 4 — AI Pod Retained

The same Pod continues — new use case every 3–4 weeks, SLA on existing agents. Duration: 3-month minimum, monthly rolling after. Price: €52,000/month. Outcome: A standing AI capability, measured against your KPIs.

Why a productized Pod, and why now.

Most mid-market and enterprise teams in Europe face the same wall in 2026: a backlog of AI use cases, pilots that never made it to production, and a board asking where AI is on the roadmap. An AI Pod is the answer — senior delivery, a published price, and a real six-week clock.

You get to production in weeks, not quarters.

A 4–6 week Pilot is written into every Pod engagement as a fixed-fee contract with a definition of done. No 12-week discovery. No endless PoC. The first production deployment is the first invoice trigger.

The team is senior, European, and yours.

Your Pod is one AI Architect and one or two AI Engineers with at least two years of LLM production experience. A Product Designer is on call for product-shaped work. Everyone is based in Europe. Code, prompts, and evaluation data are yours from day one.

The price is the price.

Tokens are included. No per-token meter. No catalog subscription. No surprise overage in month five. One published monthly number for the Retained Pod, one fixed-fee number for the Pilot.

ARC Europe — Fintech

83% reduction in claims processing time, shipped in under six weeks. Pod designed an AI agent that triages, extracts, and routes claim data across a pan-European operation.

Merck KGaA — Healthtech

Compound research cycle reduced from six months to six hours, in five weeks. Pod built an AI research assistant that accelerates literature and dataset synthesis for R&D scientists.

Newzip — Retail

60% increase in engagement, 10% conversion lift, shipped in six weeks. Pod delivered an AI-powered personalization stack for a US commerce platform's European launch.

NEONAIL — Beauty Retail

Production virtual try-on for a European beauty leader. Pod shipped a computer-vision and AI experience directly on the brand's e-commerce site.

Inside the Pod.

A Pod is a small, senior team that pairs human judgment with agentic delivery leverage. Humans own architecture, review, and stakeholder communication. Agents handle execution — code, evaluation runs, test suites, routine tooling. That is how two or three people ship production AI on a weekly cadence.

AI Architect (always-on) — Owns the architecture, the evaluation design, and the stakeholder relationship. Seven-plus years of engineering, at least two years building with LLMs in production.

AI Engineer (always-on, 1–2 per Pod) — Builds agents, prompts, integrations, and the evaluation harness. Four-plus years of engineering, at least one year in LLM production work.

Product Designer (on-call, included) — Joins Pods that deliver user-facing AI. Included at no additional fee when the engagement is product-shaped.

Specialist streams (scoped separately) — Data Engineer, MLOps, security/compliance reviewer, or domain specialist added per sprint when the scope requires.

Weekly release to non-prod during Pilot and Retained engagements. Production cutover by end of Pilot, no exceptions without a written scope change. New production use case every three to four weeks during Retained. Monthly delivery report against your KPIs and SLA.

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How we actually ship in six weeks.

We do not sell a branded framework with a trademark. We sell a method that has shipped production AI for ARC Europe, Merck KGaA, Newzip, and NEONAIL — and that we use internally for our own AI systems. It is a small set of principles applied strictly.

  1. Start with the value, not the model.

    Every engagement opens with a named KPI the agent will move after deployment. If we cannot agree on a number, we are not ready to build.

  2. Production is the deliverable.

    Prototypes are not production. Demos are not production. The Pilot is not complete until the agent is serving real traffic under your authentication with observability turned on.

  3. The evaluation harness ships with everything.

    Every agent carries a Langfuse-style eval harness — traces, inputs, outputs, latency, cost, task success. It is your audit trail and our feedback loop.

  4. One-week release cadence.

    Non-prod deploy every week. Production every two to three weeks at Pilot, every three to four weeks at Retained. We measure the cadence and we publish it.

  5. Day-agent, night-agent.

    Humans steer and review in working hours. Agents execute approved tasks overnight — evaluation, regression, documentation. Every morning starts with reviewed overnight output.

  6. You own it from day one.

    Code, prompts, evaluation data, infrastructure configuration — yours, from the first commit. If the Pilot ends and you choose not to Retain, the agent keeps running without us.

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Ready to see what a Pod would look like for you?

Thirty minutes with one of our AI Architects is usually enough to know whether a Pod is right for your problem. You'll leave with a written Fit Memo — whether we work together or not.

Book a free 30-minute AI Pod Fit Call

Frequently asked questions.

Everything you need to know about the AI Pod model, pricing, ownership, and delivery.

What is an AI Pod?

A small, senior, agent-augmented team that ships production AI on a predictable cadence. Our Pod is 2–3 people plus an evaluation harness plus an agentic delivery workflow, operating inside your environment.

How is this different from a regular engineering team?

Team size is smaller, seniority is higher, and the deliverable is always production — always with evaluation. The cadence is published and measured. Pricing is fixed or monthly, not hourly.

Who owns the code, prompts, and data?

You do, from day one. That includes prompts, evaluation data, and infrastructure configuration. If you choose not to Retain, the Pod hands everything over and the agent keeps running.

What happens if the Pilot does not hit the definition of done?

We extend at no additional cost until we do, or we refund the final milestone — your choice. The Pilot is fixed-fee with a defined DoD signed before kickoff.

How quickly can you start?

Typical kickoff is two weeks after the Pilot SOW is signed. Faster is possible with a standing Pod and standard data-access terms.

What about data security and compliance?

The Pod works inside your environment, under your controls. We run engagements under NDA and DPA, support SOC 2 Type II and ISO 27001 audits, and add a compliance review stream for healthcare and financial-services engagements.

What models do you use?

Primarily the Claude family (Opus, Sonnet, Haiku), routed per task. We also run OpenAI, Google, and open-source models where you require it. Model choice is a scope decision you own.

Can I bring my own API keys?

In 2026, foundation-model tokens are included in the price. From 2027, a BYO-API-keys option is available with a published discount.

What is your cancellation policy on Retained?

One month's notice after month three, no penalty. We earn the renewal every month.

Do you do staff augmentation?

No. Staff augmentation is a different product. We sell a productized Pod with a named deliverable and a published cadence.