AI That Runs Where Your Data Lives

Most generative AI vendors have the same answer to every question: send your data to our cloud. For a growing number of companies in Europe and Asia, that answer is not acceptable.

GDPR creates real liability when personal data is processed by US-based infrastructure. FINMA, BaFin, and sector-specific regulations in finance and healthcare impose explicit data residency requirements. Beyond compliance, there is competitive reality: your customer data, your pricing logic, your supplier relationships are the core of what you have built. Training an AI on them is valuable. Sending them to a third-party cloud you do not control is a risk most management boards would not sign off on if the question were put plainly.

Gradion builds generative AI systems that run where your data lives. On your own servers. On EU sovereign cloud infrastructure - StackIT (the Schwarz Group cloud, built for GDPR-compliant enterprise workloads), Hetzner, OVHcloud. In your existing AWS or Azure tenancy, within boundaries you define and can audit. The model layer runs on open-weight LLMs - Llama, Mistral, Phi - that are production-grade, fast, and do not require a single byte of your data to leave your perimeter.

This is not a constraint we work around. It is the architecture we design for from the start. That same architecture works equally well for companies on public cloud with no residency requirements - the sovereignty-first approach means the system runs within whatever boundaries you define, whether those boundaries are regulatory or simply prudent.

What We Build

Document intelligence Contracts, invoices, supplier emails, scanned forms, technical specifications - unstructured documents converted into structured, actionable data. We build classification, extraction, and validation pipelines with defined schemas and exception routing for human review. The pipeline runs inside your infrastructure. Output integrates directly with your ERP, CMS, or system of record. For a DACH insurance provider, we built a claims document pipeline processing thousands of documents per month with automated classification, field extraction, and exception routing - running entirely within the client's existing Azure tenancy.

Natural language to data Business users asking questions of their own databases in plain language - without needing SQL, without needing a data analyst as an intermediary. We build the agent architecture that bridges natural language queries to structured data sources, with accuracy as the primary engineering constraint. For procelo tosca, we delivered a working prototype in 8 weeks that achieved 80%+ SQL query accuracy across complex ERP schemas, handling semantic distinctions - orders versus purchases, returns versus credits - that naive LLM approaches consistently fail on.

Internal knowledge retrieval LLMs grounded in your internal documentation: policies, product catalogues, support histories, technical manuals. We design the retrieval architecture - chunking strategy, embedding model, vector store, context assembly - so answers trace to source documents and the model cannot hallucinate beyond what your knowledge base contains. For a European logistics operator, we built a retrieval system across 12,000+ internal documents that reduced support escalation time and ran entirely on-premise with no external API calls.

Supplier and back-office automation Invoice processing, email classification, purchase order reconciliation, vendor communication workflows. The same pipeline that handles 100 invoices per day manually handles 10,000 with an agent - with higher consistency and a full audit trail. The retailer reduced manual supplier communication work by 70% in an 8-week engagement using this approach.

AI governance and output monitoring Production LLM systems require the same instrumentation as any other production software. We build latency tracking, output quality metrics, retrieval accuracy scoring, and regression suites into every deployment. When the underlying model or data changes, you know before your users do. For regulated environments, we document the full audit chain from user query to model output to source document. This is not a separate add-on - it is built into every system we deploy.

Not every automation problem requires AI. Many manual processes - approval workflows, document routing, data entry from structured forms, status notifications - are better served by workflow automation or integration tooling than by an LLM. If the assessment identifies processes where deterministic automation is the right solution, we recommend that path instead. Gradion builds both: AI-powered systems where intelligence is required, and structured automation where reliability and speed are what matter. The assessment determines which approach fits each use case.

The Data Question Comes First

Before any model work begins, we assess what data is available, how clean it is, and where it lives. This is the decision that determines whether an AI system runs in production or runs in a demo.

An agent built on clean, structured, accessible data delivers predictable output. An agent built on fragmented systems with unresolved identity conflicts and inconsistent schemas will hallucinate, produce errors, and get switched off. We have seen both outcomes.

What the assessment covers. We map your data sources, evaluate quality and completeness, identify gaps that would compromise model accuracy, and assess the integration points between your existing systems and the AI layer. The output is a data readiness report that tells you what is buildable now, what requires data engineering work first, and what the realistic accuracy and performance expectations are for each use case.

When the data is not ready. We help you fix it. Data engineering - schema normalization, deduplication, identity resolution, pipeline construction - is often the highest-leverage investment before any AI work begins. We scope this as a defined phase with its own deliverables rather than absorbing it into the AI engagement where it becomes invisible and unmanaged.

When to Build Custom AI - and When Not To

Not every AI use case requires a custom system. Off-the-shelf products - Microsoft Copilot, Google Gemini for Workspace, industry-specific SaaS AI tools - solve many common problems well enough. The cost of building custom is only justified when one or more of the following is true:

Your data cannot leave your infrastructure for regulatory or competitive reasons. Commercial AI products process data on their own servers. If that is not acceptable, custom is the path.

The use case requires deep integration with your proprietary data - ERP schemas, internal knowledge bases, supplier networks - where a general-purpose tool cannot reach the accuracy threshold the business requires.

You need control over the model, the prompts, the retrieval logic, and the monitoring. Commercial products are opaque by design. In regulated environments, opacity is a compliance risk.

If none of these apply, a commercial product is likely the right choice and we will tell you that. Gradion's assessment phase is designed to answer this question before any build commitment is made.

Proof in Production

procelo tosca - 80%+ SQL accuracy in 8 weeks. procelo tosca needed business users to query complex ERP data in plain language without relying on data analysts. Gradion delivered a working prototype in 8 weeks that achieved 80%+ SQL query accuracy across schemas with semantic complexity that generic LLM approaches consistently fail on - distinguishing orders from purchases, returns from credits, and handling multi-table joins that require domain understanding. The system runs within the client's infrastructure with no external API dependencies.

The retailer - 70% reduction in manual supplier work. the leading German designer furniture retailer, a leading German designer furniture retailer, was processing supplier communications, purchase orders, and reconciliation tasks manually across procurement, warehouse, and finance teams. Gradion built an automated supplier management pipeline in 8 weeks. Manual work dropped by 70%. Cross-team alignment improved because the system enforced consistent data across all three functions. The pipeline runs as a production system, not a pilot.

DACH insurance provider - claims document processing at scale. A regulated insurance provider needed to automate claims document classification and extraction while maintaining full data residency within their existing Azure tenancy. Gradion built the pipeline to process thousands of documents per month with automated classification, field extraction, and human-in-the-loop exception routing. No data leaves the client's infrastructure. The audit trail satisfies regulatory documentation requirements.

European logistics operator - internal knowledge retrieval across 12,000+ documents. A logistics company with operations across multiple European countries needed to make internal policies, procedures, and technical documentation searchable and answerable through natural language. Gradion built a retrieval-augmented system grounded in 12,000+ documents, running entirely on-premise. The system traces every answer to its source document and cannot generate responses beyond the knowledge base. Support escalation time decreased measurably.

Many engagements are confidential. References available under NDA.

How AI Connects to Other Gradion Services

AI projects rarely exist in isolation. The data readiness assessment may reveal that the underlying systems need modernization before AI can deliver value - that is a legacy modernization or transformation roadmap engagement. A fractional CTO may identify AI as a strategic priority and need a team to execute. A technical debt reduction program may surface processes where automation eliminates the debt at its source rather than refactoring around it.

Gradion's AI practice operates within a delivery organization that covers architecture, engineering, and leadership. When an AI engagement requires work outside the AI layer, the response is immediate and coordinated - not a referral to a separate vendor.

Engagement Structure

Data Readiness Assessment 2–3 weeks. We assess your data landscape, evaluate quality and accessibility, identify the highest-value AI use cases for your specific data, and deliver a readiness report with a clear build-or-buy recommendation. If the recommendation is to use a commercial product rather than build custom, we will tell you. Scoped as a fixed-fee engagement.

Proof of Concept 6–10 weeks. A working prototype deployed in your infrastructure - not a slide deck, not a notebook demo. The PoC targets a single, defined use case with measurable accuracy thresholds agreed before build begins. The output is a system you can evaluate under real conditions and a clear cost and timeline estimate for production deployment. Scoped as a fixed-fee engagement.

Production Deployment 3–6 months. Full deployment of the AI system into your production environment with governance, monitoring, integration with existing systems, and the operational instrumentation required for ongoing reliability. Includes a defined handover period where your team assumes ownership of the system. Scoped based on system complexity, integration requirements, and data volume.

Ongoing Monitoring and Optimization For organizations that want Gradion to maintain production AI systems after deployment. This covers model performance monitoring, retrieval accuracy tracking, regression detection, and periodic optimization as your data and usage patterns evolve. A named engineer maintains continuity with your system. Scoped as a monthly retainer.