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AI data analyst
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AI Data Analyst in Munich

The best AI Data Analyst in Munich, at the best available price.

$0 upfrontPay per accepted checkpointSenior engineer, no agency markup

Your team asks in English. The service finds the tables, writes the SQL, runs it against a read-only replica inside your own cloud, and returns the number with the query attached. It cannot touch a table whose schema it has not just read, and that is enforced in code rather than requested in a prompt. Starts at $0: you pay at checkpoints you accept.

Munich's economy is built on large engineering and financial corporations: carmakers and their suppliers, industrial technology groups, and some of the world's biggest insurers and reinsurers. Software here is bought by corporate IT and procurement departments with long qualification processes, and the startup scene around the Technical University of Munich often sells deep tech into those same corporations.

Operations and actuarial-adjacent teams in Munich ask precise questions of structured data and need to check the answer. A read-only analyst that writes the SQL and shows it lets them verify every number before it goes into a report.

What you get

  • Deployed in your own cloud account against a read-only replica you create, so we never hold a credential to your database
  • A signed schema check the model cannot skip: no SQL runs against a table it has not just inspected, and the tokens expire in fifteen minutes
  • A grant list your data owner approves, naming the exact tables and columns the analyst may read, with everything else refused at the database
  • Exposed inside Claude or ChatGPT in your organisation's workspace, behind your existing Google sign in, so nobody learns a new interface
  • Every question logged with the SQL it ran, the tables it touched and whether it errored, which is what an auditor actually asks for
  • Runs on a private LLM on your own hardware where data security demands it, up to fully air-gapped, so nothing at all crosses your perimeter
Builds from $6,000. Assessment fixed fee, credited in full.

No payment before a checkpoint is delivered and accepted. Most builds land between $6,000 and $18,000 depending on how many databases are in scope and how much of the schema the first domain covers. Optional upkeep from $1,200 a month, and only if you want it.

Invoiced in USD or EUR, payable by Wise or bank transfer.

Request a callback

You speak to the engineer who does the work. No sales rep, no deck.

No spam and no sales team. You talk directly to Neeraj.

Munich, Germany

Who builds here

Automotive and mobility, led by BMW and its supplier baseInsurance and reinsurance, including Allianz and Munich ReTUM, UnternehmerTUM and the Garching research campusSiemens and industrial technology
Best fit in Munich

On-Premise LLM Deployment

Munich's insurers and engineering groups hold some of the most sensitive data in Europe, from claims files to unreleased designs, and they rarely allow it to reach a shared model API.

Try a question
1Question

How many free signups from June converted to paid, by week?

2Schema read
customers

plan tier is a column here, not a separate status table

subscriptions

one row per plan change, so take the first paid one

Schema proof issued, expires in 15 min
AQIAaPr2xQAAAACqL3d1Zw
3Verified SQL
SELECT DATE_TRUNC('week', s."startedAt")
         AS "Week",
       COUNT(DISTINCT c.id) AS "Converted"
FROM   customers c
JOIN   subscriptions s
         ON s."customerId" = c.id
WHERE  c.plan = 'free'
  AND  s.plan = 'paid'
  AND  c."createdAt" >= DATE '2026-06-01'
  AND  c."createdAt" <  DATE '2026-07-01'
GROUP  BY 1 ORDER BY 1
WeekConverted
2026-06-01214
2026-06-08188
2026-06-15243
2026-06-22201

Stage three refuses to run unless it is handed a valid token from stage two for a table in the query. The model cannot skip the schema read, because the check is a signature in code rather than an instruction in a prompt.

Architecture diagram of an AI data analyst that can only read: a question asked in Claude or ChatGPT goes to an MCP server running in your cloud, through a semantic layer of your metrics and definitions, into generated SQL, then a validation step that enforces read-only access, row limits and cited numbers, before it reaches a read-only replica. The answer returns with the numbers cited, and a write is refused by the database itself.
Every answer comes from a read-only replica through a validated query, with the numbers cited.
How you pay

Get it built at $0.

That is not a discount. It is when you pay. The work is split into checkpoints with acceptance criteria written down before anything starts, and each checkpoint is invoiced only after you have seen it and accepted it. No deposit.

$0 to start
You hold every dollar until a checkpoint is delivered and you accept it. No approval, no invoice.
Fixed cost, unlimited features
Or hire the team outright: one fixed monthly cost, unlimited feature development, any stack.
The engineer takes your call
The person on your first call is the one who architects and writes it. No account managers, no bench time.

A US agency quotes $50,000 to $150,000 for the same build and asks for 40 to 50% of it before a line is written. Account managers, project managers, sales commission and bench time. None of it appears in your product.

How it works

Three stages, nothing hidden.

01

Fixed-fee assessment

Five business days from the day read-only access is in place. Schema inventoried, the questions your team actually asks classified as answerable or not, the grant list drafted with whoever owns data protection, and acceptance criteria written as testable statements.

02

Build in checkpoints

About three weeks. Deployed in your cloud account, connected to your replica and to your assistant workspace, with the semantic layer written for the first domain. Each checkpoint has criteria agreed before work starts and is invoiced only once you accept it.

03

Handover and closeout

A real question from your team, answered end to end, with the SQL shown and the numbers reconciled against the source rows. Runbook, recorded handoff, code and semantic layer in your repository, and all our access removed with written confirmation.

Working in Munich

What actually applies here.

Regulation and data

GDPR and the BDSG apply, and private companies in Bavaria are supervised by the Bavarian State Office for Data Protection Supervision (BayLDA). Works council co-determination under Section 87(1) no. 6 of the Works Constitution Act is routine in Munich's large employers, so any tool that can monitor staff needs early agreement. Automotive suppliers are usually asked for a TISAX assessment, and vehicle software falls under the UNECE R155 cybersecurity rules. Insurers have applied DORA since January 2025, which replaced BaFin's VAIT for firms in its scope. Germany's NIS2 act has applied since December 2025. The EU AI Act has been in force since August 2024: general-purpose model rules applied from August 2025 and transparency duties from August 2026, while the 2026 Digital Omnibus moved the main high-risk obligations to December 2027 for stand-alone systems such as hiring or credit scoring, and August 2028 for AI built into regulated products.

Contracting and payment

You contract with an individual consultant based in India. Invoices in EUR or USD, paid by Wise or bank transfer. Your accountant handles an overseas supplier in the usual way, and any documentation they need is provided before access is granted.

Working hours

Four or more hours of daily overlap across your morning, Central European time, with same-day replies on working days.

Can you work with a TISAX or corporate IT qualification process?

Yes, on the engineering side. That means documented architecture, named limited-privilege access in your accounts, and evidence for your security questionnaire. The assessment itself belongs to your organisation, and we supply the technical material it needs.

Can the model run on our own hardware in Bavaria?

Yes. A private deployment can run on servers you own or in an EU region of your cloud account. Nothing is sent to a third-party model provider, and you keep the weights, configuration and logs after handover.

We already have a BI tool. Does this replace it?

No, and it should not. Your dashboards answer the questions somebody anticipated, and they will keep doing that. This answers the ones nobody built a chart for, which is where the waiting happens. Teams that keep both end up using the dashboards for the standing numbers and the analyst for everything else.

How long before somebody can ask a real question?

Usually inside the first week of the build, on a narrow slice of the schema. The first domain being useful early is the point of doing it in checkpoints: you see it working on your own data before most of the money is committed.

What if our schema is a mess?

Most are, and it changes the shape of the work rather than ruling it out. Undocumented columns, legacy tables nobody dares delete and two spellings of the same idea are normal. The assessment tells you plainly how much of that has to be untangled first, and if the answer is that you need a data cleanup before an analyst is worth building, you will be told that instead of sold this.