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AI Consulting & Implementation · DACH Region

AI
you can trust.

We build RAG systems and AI applications that are production-ready - cleanly structured, LLM-ready data foundations, embedded in real enterprise context. No prototypes that collect dust after the demo.

Start a project →View services
End-to-End
Audit → Go-Live
100%
GDPR · EU AI Act-ready
0
PoCs gathering dust
In production at
Our position

Companies are sitting on vast amounts of valuable data - and can't use it.

Not because the technology is missing, but because the foundation is: cleanly structured, LLM-ready data, embedded in real enterprise context.

That's exactly what we build - hands-on, no detours, with systems that actually work.

Backed by data
Three numbers showing why most AI initiatives don't deliver - and where the real bottleneck is.
87%
of AI pilots fail before reaching production
VentureBeat · AI Report
2024
73%
cite data quality as the biggest AI obstacle
MIT Sloan Management Review
2024
90%
of enterprise data is unstructured
IDC · Global DataSphere
2023
Architecture at a glance

From raw document to traceable answer.

fig. 01 · pipeline schematic
codestra / rag.pipeline.v1live tracescale 1 : 1
01 / 03SourcesYour existing systems02 / 03FoundationStructured, annotated, indexed03 / 03ApplicationAnswers in business contextSAP · ERPSharePoint · DMSWikis · ConfluenceEmail ArchivesTechnical ReportsLayout ParserparseContext AnnotationenrichHybrid Indexvector + BM25Access ControlAEvaluationBKnowledge SearchContract Q&ATechnical SupportAgent WorkflowsProduct Data AssistantC · human-in-the-loop · feedbackABD
A
Access Control
Document and field level
B
Evaluation
Continuous, not just at go-live
C
Human-in-the-Loop
Feedback flows back into the index
D
Audit Trail
Every answer, versioned
Services
S1 - S2 - S3

Three services. Building on each other. Bookable individually.

S1
The starting point.

AI Strategy & Readiness

For companies that know AI is relevant - but don't yet know which use cases deliver real ROI and what foundation is missing.

  • .01
    AI Maturity Assessment & Data Strategy
    Structured assessment of data quality, IT infrastructure, and processes. Result: written report with gap analysis and recommendations.
  • .02
    Use Case Evaluation & ROI Prioritization
    Facilitated workshops - evaluated by measurable business value, data availability, and feasibility. Not brainstorming, but structured decision-making.
  • .03
    AI Roadmap & Business Case
    Implementation plan with ROI estimate, make-or-buy recommendation, and a decision-ready brief for leadership and investors.
S2
Our core business.

AI Implementation

We build RAG systems and AI applications that are production-ready - no prototypes that collect dust after the demo.

  • .01
    RAG Architectures & Knowledge Systems
    Unstructured enterprise data made accessible for LLMs: documents, databases, ERP exports, wikis. Precise answers with source citations, access control, traceability.
  • .02
    AI Agents & Agent-Based Workflows
    Autonomous systems for multi-step tasks. Human-in-the-loop as a core component. User feedback actively improves retrieval quality.
  • .03
    LLM Integration & System Connectivity
    Integration into existing software - ERP, CRM, DMS. API development, prompt engineering, guardrails. Cloud deployment on AWS, Azure, or GCP.
S3
Processes that understand context.

AI-Powered Process Automation

Automation of processes that previously failed on unstructured inputs - workflows that need to make decisions and adapt to exceptions.

  • .01
    Document Processing & Data Structuring
    Automatic extraction and classification from invoices, contracts, applications, technical reports. Makes data volumes LLM-ready that were previously processed manually.
  • .02
    End-to-End Process Automation
    Comprehensive automation in finance, HR, sales, operations - with AI decision layer, exception handling, and escalation logic.
  • .03
    AI-Powered Customer Communication
    Email triage, context-aware chatbots, ticket routing - integrable into web, Teams, Slack. Responds based on your own data, not generic training data.
What sets us apart

Most AI projects don't fail because of technology - they fail because of poor data quality and missing evaluation.

We build benchmarking and quality measurement in from day one. So you always know how well the system really performs - not just whether it responds.

Our approach
4 phases · one clear outcome per phase

Every phase ends with a concrete result your team can build on.

Four clear phases, each with a deliverable your team builds on. After go-live, your team takes over - clean handoff, not dependency.

01
Phase 01

Requirements Analysis & Data Assessment

We assess your data landscape, systems, and use cases. Result: a structured decision basis - not a brainstorm.

Data Audit · Use Case Matrix · Architecture Sketch
02
Phase 02

Proof of Concept

One focused use case, built with real data, in your environment. Benchmarking from day one.

Working PoC · Eval Baseline · Risk Report
03
Phase 03

Production Deployment

Integration into ERP/CRM/DMS, cloud deployment, quality assurance. Continuously visible in the system.

Integration · Monitoring · Access Control
04
Phase 04

Handoff & Knowledge Transfer

Your team operates the system independently. Full documentation, clean handoff - no dependency on us.

Runbook · Training · Documentation
Why codestra

Three ways companies try. Why none of them is enough.

Not sufficient

Build in-house.

Internal teams are often too close to day-to-day operations to consistently implement clean data pipelines, quality assurance, monitoring, and access control.

Our answer

We take no shortcuts from day one.

Years of hands-on project experience - the shortcuts that seem tempting internally end up being expensive later.

Not sufficient

Strategy without implementation.

A roadmap is valuable - but only once someone consolidates the data, builds the system, and hands it over to operations. That's the gap we work in, often hand in hand with strategy partners.

Our answer

We build what the strategy promises.

We know the pitfalls between concept and production - and we deliver the implementation that turns concept decks into real impact.

Not sufficient

Wait and see.

The question isn't whether AI will become relevant, but who builds a reliable data foundation first that others can't catch up to.

Our answer

Every quarter counts.

Every quarter without a structured AI foundation is a quarter of advantage for your competitors.

Compliance

Not an add-on. Standard.

Every system we build is GDPR-compliant and EU AI Act-ready. Governance is built into the data layer, not around it - saving your IT team rework later.

GDPR
Compliant per Art. 5, 25, 32. Data processing in EU region, documented audit trail.
✓ ready
EU AI Act
Categorization and risk assessment from day one. Documentation ready for high-risk classification.
✓ ready
FAQ
Click to expand

Asked directly. Answered without detours.

How do you comply with GDPR and the EU AI Act - and what role do we vs. you take on?+
Typically you are the deployer of the AI system, and we act as data processor and provider of the underlying components. The role split under the EU AI Act and GDPR is fixed contractually: data processing agreement under Art. 28 GDPR, technical documentation aligned with Annex XII of the AI Act, transparent list of all sub-processors. Our RAG architecture follows the German DSK guidance on RAG from October 2025.
Where is our data processed and which models do you use?+
Hosting in your own Azure, AWS, or GCP tenancy in an EU region, or fully on-premise. We are model-agnostic and choose per use case between OpenAI, Anthropic, Mistral, Llama, or custom fine-tunes - swapping models after go-live is a configuration change, not a re-implementation project. Your data does not flow back into model providers' training sets, secured both contractually and technically.
How do you prevent hallucinations - and how do we know the system actually works?+
Every answer is traced back to concrete sources; without sufficient evidence the system refuses to answer rather than guess. Before every release, an automated evaluation suite runs against a golden dataset of your real questions - measuring faithfulness, citation accuracy, answer relevance, and more. In production we continuously monitor the same metrics, plus drift in the data base and user behavior.
How does the system integrate with M365, SharePoint, Confluence, SAP - and are permissions respected?+
Standard connectors for Microsoft Graph, SharePoint, Confluence, SAP, and common databases; anything else we wire up via REST or SQL. Permissions are checked against the source system at query time - users only see content they would have access to in the original system. Changes to the data base flow into the index via delta sync or webhook.
Who owns the code, index, and models - and who runs the system after go-live?+
Source code, configuration, index, and fine-tunes belong to you. For operations there are three models: we keep running the system (managed), we hand it over to your internal IT, or to a third-party operator of your choice. Handover includes runbook, architecture documentation, and the eval suite - whoever runs the system in future can also prove its quality.
Let's talk

30 minutes. One specific topic. After that, you'll know if it's a fit.

No sales funnel. You speak directly with someone who has built systems - not an account manager.

Cologne
Gleueler Straße 81 50931 Cologne, Germany
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We respond within 48 hours.