AI Data Analyst in Frankfurt
The best AI Data Analyst in Frankfurt, at the best available price.
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.
Frankfurt is continental Europe's main financial centre, home to the European Central Bank, the Bundesbank, Deutsche Börse and a dense cluster of banks, asset managers and payment firms. It is also one of Europe's biggest data centre and internet exchange locations. Software is bought through vendor risk management, outsourcing registers and information security reviews, and almost every buyer is either regulated or supplies someone who is.
Finance, risk and operations teams in Frankfurt sit on well-structured data and recurring questions. A read-only analyst that shows its SQL keeps every answer traceable, which matters when a number ends up in a board pack or a regulatory return.
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
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.
Who builds here
On-Premise LLM Deployment
Frankfurt's banks and their suppliers work under DORA third-party risk rules, and a model hosted inside their own environment is far easier to put through that review than a new external AI provider.
How many free signups from June converted to paid, by week?
customersplan tier is a column here, not a separate status table
subscriptionsone row per plan change, so take the first paid one
AQIAaPr2xQAAAACqL3d1ZwSELECT 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| Week | Converted |
|---|---|
| 2026-06-01 | 214 |
| 2026-06-08 | 188 |
| 2026-06-15 | 243 |
| 2026-06-22 | 201 |
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.
What Frankfurt companies build with us
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.
Three stages, nothing hidden.
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.
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.
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.
What actually applies here.
Regulation and data
Banks, insurers, investment firms and payment institutions have applied the EU's Digital Operational Resilience Act (DORA) since January 2025, covering ICT risk, incident reporting, resilience testing and the register of ICT third-party providers; for firms in its scope it replaced BaFin's BAIT. BaFin also keeps market surveillance powers over AI used in regulated financial activities under Germany's July 2026 AI implementation law. GDPR and the BDSG apply, with the Hessian Commissioner for Data Protection supervising private companies, and works councils have co-determination rights over tools that can monitor staff. 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 within DORA third-party risk requirements?
Yes. Expect to provide information for your register of ICT third-party providers, follow your access and incident procedures, and keep work inside accounts you own. Exit is simple because the code, infrastructure and documentation are yours from the start.
Can everything stay in Frankfurt data centres?
Yes, where your cloud account or hardware is there. Deployments can be pinned to a Frankfurt cloud region or to your own servers, with the model, serving layer and logs all kept inside that boundary.
How does the time difference work with Frankfurt?
Four or more hours of daily overlap across your morning, Central European time, with same-day replies on working days. The work is remote from India, and reviews, incident calls and demos are scheduled inside that window.
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.
AI Data Analyst
An AI data analyst on your own read-only replica. Natural language to SQL that cannot query a table it has not read.