Information Density: Gosign GmbH – Signal Evidence & AI Readability

[H1] Enterprise AI Agents
In your infrastructure. Under your control. Autonomous AI Agents for business-critical processes - with the goal of measurably reducing decision risks in HR and Finance. Model-agnostic, auditable, EU AI Act compliant by design. Governance by Design - not as an add-on. Schedule a Consultation View References
[IMG: Architecture stack diagram showing Agents, Governance, and Infrastructure layers]
Auswahl aus über 5.000 Projekten in 25 Jahren Softwareentwicklung
[IMG: Airbus]
[IMG: Volkswagen]
[IMG: Shell]
[IMG: Renault]
[IMG: Evonik]
[IMG: Vattenfall]
[IMG: Philips]
[IMG: KPMG]
[H2]
Enterprise AI Infrastructure & Agent Engineering
Gosign is an Enterprise AI Infrastructure & Agent Engineering Company. We develop and operate the infrastructure that makes AI Agents production-ready in enterprises: orchestration, governance, Decision Layer, and audit.
25 years of software engineering. 108 employees. Over 5,000 projects for e.g. Airbus, Volkswagen, Shell. Since 2023 focused on Enterprise AI Agent Engineering.
[IMG: Gosign 7-layer architecture for Enterprise AI: Presentation Layer (Chat UI, Dashboard, API), Orchestration Layer (Trigger.dev, Camunda), Agent Layer (Document, Workflow, Knowledge Agents), Governance Layer as cross-cutting concern (Audit Trail, RBAC, Decision Layer, Cert-Ready Controls), Model Layer (Claude, GPT, Gemini, Llama, Mistral, DeepSeek - model-agnostic), Integration Layer (SAP, DATEV, Microsoft Graph), Infrastructure Layer (Azure EU, GCP EU, AWS EU, Self-Hosted, Hybrid)]
[H2] Why Most AI Projects Don't Deliver Measurable Results
Most enterprises are already using AI. Very few achieve measurable results. Not because the technology doesn't work - but because nobody has defined which decisions AI may make and which must stay with humans.
Industry experience shows: For every euro invested in technology, you need four to five euros in processes, governance, and change management. Investing only in technology means investing past the problem.
The Decision Layer is the layer that makes the difference: It decomposes every business process into individual decision steps and defines for each - human, rule set, or AI. That's how an AI experiment becomes a production system.
From August 2026, the EU AI Act (UK: UK AI regulatory framework) high-risk obligations apply to AI systems in employment and financial services. Organisations without auditable governance infrastructure by then will not be compliant.
[H2] AI Agents for Your Department
[H3] AI Agents for Finance & Accounting
Decision automation for document processing, posting and audit preparation. Versioned rulesets, complete audit trail, Cert-Ready by Design. The Decision Layer makes every posting decision traceable.
Finance AI Agents
[H3] AI Agents for HR & People Operations
Auditable AI Agents for HR decisions. Compliant with employee representation requirements. Decision Layer with Human-in-the-Loop. Payroll, Onboarding, Document Processing, Compensation & Merit, Policy & Knowledge.
HR AI Agents
[H3] AI Infrastructure for IT & Enterprise
LLM hosting, RAG, orchestration. Self-hosted, cloud, or hybrid. Model-agnostic, Governance by Design, Cert-Ready by Design. The platform your agents run on in production.
Infrastructure
[H2] Specialized Agents for Enterprise Processes
01
[H3] Document Agents
The specialist: understands documents. A medical leave certificate arrives. The agent identifies the document type, extracts name, period, and diagnosis code, checks whether all required fields are present, and assigns the document to the correct employee. In Finance: The Document Agent reads an incoming invoice, extracts invoice number, amount, and service period, and validates against mandatory field requirements. Result: a structured data set with a posting proposal. Not template matching - real language comprehension. The Decision Layer evaluates every extraction: deterministic? Rule set. Confident enough? Agent decides independently. Discretion needed? Human reviews.
Document Agents in Detail
[IMG: Document Agent - The Specialist: One document arrives, the agent understands type, content, and context, Decision Layer routes through three tiers (rule set, AI autonomy, human review), structured data comes out.]
02
[H3] Workflow Agents
The coordinator: steers the entire process. The medical leave certificate is understood - now what? The Workflow Agent takes over: checks the HR system whether this is the third notification in six months, verifies against the collective agreement whether the return-to-work threshold has been reached, creates a task for the HR manager in SAP SuccessFactors, notifies employee representatives, and schedules a follow-up. Five systems, three decision points, one agent coordinating the entire process - including independent routing decisions where confidence is sufficient. Every step in the audit trail.
Workflow Agents in Detail
[IMG: Workflow Agent - The Coordinator: Steers a multi-step process across systems. Calls Document Agents, checks against rule sets, creates tasks in SAP, notifies representatives, schedules follow-ups. Every step in the audit trail.]
03
[H3] Knowledge Agents
The knowledge carrier: answers questions from enterprise knowledge. An HR manager asks: 'When does a return-to-work process need to be initiated after extended leave?' The agent doesn't just search - it interprets company policies, collective agreements, and regulatory requirements in the context of the question, delivering a specific answer with source reference, rule version, and validity date. When uncertain, it flags uncertainty explicitly. Without a verified source, the agent does not answer - no hallucination.
Knowledge Agents in Detail
[IMG: Knowledge Agent - The Knowledge Carrier: Question with context in, agent interprets verified sources, specific answer with source and version out. Uncertain: flags it. No source: no answer.]
[H2] Architecture Comparison: Copilot, SaaS Agent, Gosign
Three approaches to enterprise AI - different architectures, different consequences.
Dimension
Gosign Agent Architecture
Microsoft Copilot
SaaS AI Agent
Decision Depth
Domain decisions with Decision Layer
Assistance and suggestions
Preconfigured workflows
Auditability
Complete audit trail down to SQL level
Basic logging
Platform logging
Source Code Access
Full source code access · Configurations remain with client · No vendor lock-in
Microsoft owns code
Platform owns code
Model Choice
Model-agnostic (GPT, Claude, Gemini, Llama, Mistral)
GPT (Microsoft-bound)
Platform-bound
Governance
Own Governance Layer, Cert-Ready Controls, Auditor Portal
Azure governance
Platform governance
Human-in-the-Loop
Architecturally enforced for risk decisions
Optional
Configurable
EU AI Act
Compliant by design - transparency, explainability, oversight built in
Microsoft roadmap
Provider-dependent
Employee Oversight
Templates, logging, role concepts for employee representation bodies
No specific support
No specific support
Audit Readiness
GoBD (German record-keeping standard), ISA, IDW - audit documentation as architecture
Platform compliance
Platform compliance
Infrastructure
Client infrastructure (Azure, GCP, AWS, self-hosted, hybrid)
Microsoft Cloud
Provider cloud
Exit Strategy
Independent operation after 12-18 months
Platform migration
Platform migration
This table shows architectural differences, not quality judgments. Copilot and SaaS agents have different strengths - speed, ecosystem, simplicity. Gosign's strength is governance, ownership, and auditability in regulated environments.
[H2] Governance by Design
Agents only scale with infrastructure. Without governance, AI stays a pilot - with infrastructure, it becomes scalable.
Human-in-the-Loop: Every process is decomposed into decision steps. For each step: Does a human decide, does a rule set apply, or does the AI decide autonomously? Where the agent is confident enough and has permission, it decides independently - this is not if-then-else, this is judgment within defined guardrails. Bias risk, discrimination potential, or employee oversight requirements: the architecture enforces human review. The Decision Layer enforces this routing - technically, not organizationally.
Auditable: Every agent decision produces a complete record: input, model, assessment, confidence score, reasoning, decision path, outcome. Immutable, exportable, audit-ready.
Employee Oversight: Governance frameworks - collective agreements, works agreements, or company policies - as explicit constraints in the Decision Layer. Employee representation bodies can trace: what the agent does, why, and when a human intervenes. Role concepts and templates included.
Cert-Ready by Design: Controls are first-class data objects in the system. Every control has a technical implementation, an automatic evidence generator, and an evidence history. Auditors see live status in the Auditor Portal.
EU AI Act compliant by design: Transparency, explainability, and human oversight are architecturally built in - not retrofitted.
We work alongside your internal IT, security, and compliance teams. Agents become part of your existing IT governance - not a parallel universe.
[IMG: Three autonomy levels in the Gosign Decision Layer: (1) Human decides - agent provides data for salary adjustments, terminations, transfers, bias-relevant decisions, employee oversight matters (approx. 35% of HR processes). (2) Agent works, human reviews - for document processing, contract review, onboarding steps, reference letter creation, recruiting screening (approx. 40% of HR processes). (3) Agent autonomous - for FAQ answers, standard certificates, deadline checks, data validation, routine notifications (approx. 25% of HR processes). Every decision documented, every step auditable.]
[H3] Definition: Decision Layer
The Decision Layer decomposes every business process into individual decision steps and defines upfront for each: Does a human decide, does a rule set apply, or does the AI decide autonomously?
Where discretion, discrimination risk, or employee representation requirements are involved, the architecture enforces human review. Where a decision is deterministic - collective agreement terms, deadline checks, booking logic - the agent applies the rule set consistently. And where the agent is confident enough and has permission: it decides independently. It interprets documents, classifies situations, evaluates context - demonstrably more consistent and legally sound than manual processing. Confidence Routing controls when the agent acts autonomously and when it escalates.
Every decision is documented - who decided what, when, on what basis, with what outcome. Auditable for external auditors, employee representation bodies, and internal compliance.
Governance, Security & Audit
[H2] From PoC to Platform
1
[H3] Discover
1 week
Process analysis, rule mapping, system landscape assessment, use case prioritization. Outcome: a concrete plan for your first agent.
2
[H3] Build
3-4 weeks
Production PoC. One agent, one process, live in your infrastructure. Decision Layer, governance, audit trail - from day one, not retrofitted.
3
[H3] Scale
Ongoing
More agents, more departments, more locations. The architecture grows with your requirements. Same governance, same infrastructure.
After 12-18 months, you operate your agents independently. Full access to source code, prompts, and configurations. No vendor lock-in - even without a maintenance contract.
[H2] Agent Briefing
Practical knowledge on AI agents, AI infrastructure and enterprise integration. All articles →
[IMG: Why AI Projects in HR Fail]
HR & People Operations
[H3] Why AI Projects in HR Fail
Most AI projects fail not because of technology but because nobody defined the rules. Why the operating model matters more than the language model. February 23, 2026 6 min read
[IMG: EU AI Act: What HR Departments Must Do Now]
Governance & Compliance
[H3] EU AI Act: What HR Departments Must Do Now
The EU AI Act directly affects HR processes. Risk classification, bias monitoring, human oversight - what is now mandatory and how to prepare. February 20, 2026 8 min read
[IMG: Why HR Departments Need to Build Agent Governance Now]
HR & People Operations
[H3] Why HR Departments Need to Build Agent Governance Now
Agent governance is not an IT topic. It's an HR leadership topic. What CHROs need to know before AI agents enter core HR processes. December 5, 2025 7 min read All articles →
“Even as a global market leader, you want to keep moving forward. It is reassuring to have the technological expertise and infrastructure experience of Gosign on our side.”
[IMG: Arletta Korff]
Head of Innovation, Sony Music Entertainment “Gosign is not just about speed. It's about how much essential work happens in this time.”
[IMG: Truels Dentler]
Head of Customer Service & Technical Support, Libri GmbH
[H2] Frequently Asked Questions
Is the architecture EU AI Act compliant? The architecture addresses the core requirements of the EU AI Act as a design principle: transparency (Art. 13) via the Decision Layer, human oversight (Art. 14) via architecturally enforced Human-in-the-Loop routing, recording obligations (Art. 12) via the audit trail, and risk management (Art. 9) via bias monitoring and Cert-Ready Controls. How is the Cloud Act / data sovereignty handled? All agents run in the client's infrastructure - cloud, self-hosted, or hybrid. For cloud deployments in EU data centers (Azure EU, GCP EU, AWS EU), DPAs and Standard Contractual Clauses apply. For complete Cloud Act independence: self-hosted in an EU data center or on your own servers. No US provider has access to business data. Which certifications are supported? The architecture is Cert-Ready by Design. Controls are implemented as technical data objects with automatic evidence generation. Framework mapping to ISO 27001, SOC 2, ISA, PS 951, IDW, GoB/GoBD. The architecture is structurally certifiable - the actual certification is carried out by the client. Is this compliant with employee oversight requirements? Yes. Governance frameworks - collective agreements, works agreements, or company policies - are mapped as explicit constraints in the Decision Layer. Human-in-the-Loop is architecturally enforced for bias risk, discrimination potential, and employee oversight matters. Complete logging, role concept, audit trail. Templates for employee representatives are part of the architecture. Built for the most demanding regulatory environment globally - German co-determination law, EU AI Act, and GDPR - meeting or exceeding compliance requirements in virtually any jurisdiction. How long does a pilot project take? 4-6 weeks to a production PoC. Discover (1 week): process analysis, understanding rule sets. Build (3-4 weeks): one agent, one process, live in your infrastructure with Decision Layer and audit trail.
[H2] Which process should your fi

[H1] When AI makes decisions - who is accountable?
The Decision Layer makes every AI decision traceable, auditable, and compliant with employee oversight requirements. AI agents can process sick leave, classify invoices, and review contracts. But who decides what the agent may do on its own and where a human must step in? The Decision Layer defines this - for every single process step. Schedule a Consultation See Example Auswahl aus über 5.000 Projekten in 25 Jahren Softwareentwicklung
[IMG: Airbus]
[IMG: Volkswagen]
[IMG: Shell]
[IMG: Renault]
[IMG: Evonik]
[IMG: Vattenfall]
[IMG: Philips]
[IMG: KPMG]
[H2] Why AI Projects in HR Fail
HR processes depend on the knowledge of individual employees. Who knows which special leave policy applies at which location? Who remembers the difference between the company agreement from 2019 and the updated version from 2024? Who checks whether sick leave was correctly validated against the collective agreement?
This knowledge lives in people's heads, in email threads, in folders nobody can find. When someone leaves the team, the knowledge leaves too.
AI can help - but only when it's clear which rules apply. And who is ultimately responsible.
[H2] What Is the Decision Layer?
The Decision Layer decomposes every business process into individual decision steps and defines upfront for each: Does a human decide, does a rule set apply, or does the AI decide autonomously?
HUMAN: The architecture enforces human review. For discretionary decisions, discrimination risk, employee representation matters, and value thresholds above defined limits. The agent provides full context and a recommendation - but a human decides. This escalation is technically enforced, not organisationally agreed.
RULE SET: The decision is deterministic - there is no room for interpretation. The collective agreement states X, so X applies. A deadline expires on date Y, so rule Z triggers. Rule sets are versioned: every change creates a new version, previous versions remain traceable. Here, the agent is an executor - not because it cannot do more, but because there is nothing to interpret.
AI AUTONOMOUS: The agent makes independent decisions - because it is confident enough, has permission, and demonstrably performs the task better than manual processing. It interprets documents, classifies situations, evaluates context, and recognises patterns. This is not if-then-else - this is judgment within defined guardrails. Confidence Routing controls: high confidence and low risk leads to autonomous decision. Low confidence or high risk leads to escalation to a human. This Confidence Routing is precisely what distinguishes the Decision Layer from RPA.
(US: In the US, where works councils do not exist, the human review tier maps to internal compliance review, EEOC-aligned anti-discrimination checks, and management approval workflows. The architecture is jurisdiction-agnostic - the governance rules adapt to your regulatory environment.)
(UK: In the UK, the human review tier maps to obligations under the Equality Act 2010 and ICE Regulations 2004, plus internal governance frameworks.)
The Decision Layer is not an AI agent - it's the governance layer above. It complements existing systems like SAP SuccessFactors, Workday, or DATEV and controls what an AI agent may do with these systems. Every decision is automatically documented - for auditors, employee representation bodies, and internal audit.
How the Decision Layer addresses shadow AI and technically enforces company agreements is detailed in
Article 6 of the Blueprint 2026.
Without Decision Layer
With Decision Layer
Who decides?
Unclear - the agent delivers a result
Defined per step: human, rule set, or AI
Company agreements
Manually followed - or forgotten
Stored as fixed rules, technically enforced
Traceability
Result visible, decision path not
Complete documentation per decision
Auditor
Must manually review each case
Direct access to decision documentation
Employee oversight
Blocks - no transparency
Supports - every decision traceable
[H2] How Does the Decision Layer Work in Practice?
Sick leave example: 6 steps, clear accountability at each step. The Decision Layer defines for each: rule set, human, or automatic.
[IMG: Process example: sick leave processing with Decision Layer in 6 steps. Steps 1-2 automatic (read document, load employee data), steps 3-4 rule-based (validate against collective agreement, calculate continued pay), step 5 human decision (long-term illness over 6 weeks, duty of care), step 6 automatic SAP booking.]
[H2] Why Do AI Projects Fail at Employee Oversight?
A common reason AI projects stall in enterprises: employee representation bodies block them. Not because they oppose technology - but because they lack transparency. In Germany specifically, works councils frequently halt AI deployments for this reason. The Decision Layer solves this:
Every governance framework - collective agreements, works agreements, or company policies - is stored as a fixed rule. The agent cannot bypass it.
For decisions affecting employees, a human always decides. Technically enforced, not just agreed upon.
Every AI decision is documented: What was checked, which rule applied, what was the result.
Employee representation bodies can trace how any decision was made, at any time.
The difference: Others promise transparency. The Decision Layer enforces it technically.
Employee Oversight & Co-determination →
[H2] How Does an AI Decision Become Audit-Proof?
Your auditor sees exactly what happened.
Which document was processed - and when?
Which rule was applied - and in which version?
How confident was the agent in its assessment?
Did a human review - and if so, who?
What was the result and when was it booked?
[H2] Which Decisions Can AI Make on Its Own?
Some decisions an AI agent can make on its own. Others need human review. And for strategic questions, the agent only provides data. The Decision Layer defines this - per step, not per process.
[IMG: Three types of decisions in the Decision Layer: Left - human decides for people strategy, performance reviews, compensation policy (about one third). Center - agent works, human reviews for document processing, contract review, onboarding (about 40%). Right - agent autonomous for FAQ, standard certificates, deadline checks (about one quarter).]
[H2] Who Is the Decision Layer For?
Head of HR / CHRO
You want to use AI in HR - without losing control. The Decision Layer ensures governance frameworks are enforced, employee representation bodies have transparency, and every decision is traceable.
CFO / Head of Finance
Every AI-assisted booking is audit-proof. Your auditor sees the complete decision path. Correction bookings are reduced because deterministic rule sets leave no room for interpretation - and where the agent decides autonomously, Confidence Routing ensures escalation when needed.
Employee Representatives
No black box. Governance frameworks are technically stored and cannot be bypassed. For personnel decisions, a human always intervenes. In Germany: full co-determination transparency for works councils.
IT / CTO
Model-agnostic, infrastructure-agnostic, no vendor lock-in. Technical details in the Reference Architecture →
[H2] The Decision Layer for Your Processes
[H3] HR & People Operations
Sick leave, onboarding, employment references, contract review, policy queries. Compliant with employee representation requirements, EU AI Act compliant.
View HR Agent →
[H3] Finance & Payroll
Document processing, account assignment, payroll, closing entries. Audit-proof. DATEV and SAP integration.
View Finance Agent →
[H3] Document Processing
Invoices, contracts, certificates. Automatic classification and data extraction with rule-based validation.
View Document Agents →
[H2] Is the Decision Layer EU AI Act Compliant?
The EU AI Act requires transparency, human oversight, and documentation of AI decisions. The Decision Layer addresses these requirements as an architectural principle - not as an afterthought compliance project.
EU AI Act and HR in Detail →
[H2] Who Builds the Decision Layer?
The Decision Layer is developed and implemented by Gosign GmbH. Gosign is an Enterprise AI Infrastructure & Agent Engineering company based in Hamburg, Germany, with over 20 years of experience building complex systems for enterprises including Airbus, Deutsche Telekom, and Sony Music.
4-6 weeks to the first productive process. Full source code access, no vendor lock-in. Goal: After 12-18 months, you operate your agents independently.
About Gosign →
·
References →
[H2] Decision Layer in Practice: 48 HR Agents
The HR Agent Catalog shows for each of the 48 agents how the Decision Layer splits decisions across human, rule set, and AI agent - with complete micro-decision tables and decision records.
Explore the HR Agent Catalog →
[H2] Deep Dive in the Agent Briefing (Gosign Magazine)
Governance
[H3] EU AI Act and HR
HR
[H3] Agent Governance for HR
Governance
[H3] Works Council and AI - Co-determination
[H2] Frequently Asked Questions About the Decision Layer
What is a Decision Layer - simply explained? The Decision Layer decomposes every business process into individual decision steps and defines upfront for each: Does a human decide, does a rule set apply, or does the AI decide autonomously? Where discretion or discrimination risk is involved, the architecture enforces human review. Where a decision is deterministic, the agent applies the rule set. Where the agent is confident enough and has permission, it decides independently. This is not if-then-else - this is judgment within defined guardrails. Do I need to replace existing HR systems? No. The Decision Layer complements SAP SuccessFactors, Workday, Personio, or DATEV. It controls what the AI agent may do with these systems - and documents every interaction. No migration, no system replacement. How do employee representation bodies react to AI agents with a Decision Layer? Typically positively - because the Decision Layer provides exactly the transparency that employee representation bodies demand. Governance frameworks become technical rules, personnel decisions stay with humans, and every AI decision is traceable. In Germany specifically, works councils value the co-determination transparency this architecture provides. What distinguishes the Decision Layer from SAP Joule or Microsoft Copilot? SAP Joule and Microsoft Copilot are AI agents - they execute tasks. The Decision Layer is not an agent. It's the governance layer above: it defines which decisions an agent may make autonomously, where a human must step in, and where rule sets apply. The Decision Layer is model- and vendor-agnostic. How long does implementation take? 4-6 weeks to the first productive process. Week 1: Understanding your processes and rule sets. Weeks 2-5: Building and testing the first agent with Decision Layer. After that: Your team gradually takes over operations.
[H2] Which HR process costs you the most time?
Show us a specific process - we'll show you what the Decision Layer looks like for it.

Featured
HR & People Operations gosign.de/magazine HR & People Operations
[H2] Why AI Projects in HR Fail
Most AI projects fail not because of technology but because nobody defined the rules. Why the operating model matters more than the language model. February 23, 2026 6 min read
Featured
Governance & Compliance gosign.de/magazine Governance & Compliance
[H2] EU AI Act: What HR Departments Must Do Now
The EU AI Act directly affects HR processes. Risk classification, bias monitoring, human oversight - what is now mandatory and how to prepare. February 20, 2026 8 min read
Featured
HR & People Operations gosign.de/magazine HR & People Operations
[H2] Why HR Departments Need to Build Agent Governance Now
Agent governance is not an IT topic. It's an HR leadership topic. What CHROs need to know before AI agents enter core HR processes. December 5, 2025 7 min read Governance & Compliance gosign.de/magazine Governance & Compliance
[H2] Why We Don't Train AI Agents Anymore
92% accuracy without training. From August 2026, the EU AI Act requires explainable individual decisions. Trained models cannot deliver that. April 5, 2026 12 min read Finance & Payroll gosign.de/magazine Finance & Payroll
[H2] Automating Travel Expenses: True Cost per Report
$58 per report, 19% error rate, $52 per correction. GBTA data shows: manual expense processing costs enterprises millions - and it is avoidable. March 1, 2026 8 min read Finance & Payroll gosign.de/magazine Finance & Payroll
[H2] Enterprise Travel Expenses: SAP Concur Limits
SAP Concur captures receipts - but who decides on collective agreements, per diems and IROP? Why enterprises need more than an expense tool. March 1, 2026 10 min read Governance & Compliance gosign.de/magazine Governance & Compliance
[H2] DPA for AI Agents: What Standard Contracts Miss
Why standard DPAs fall short for enterprise AI infrastructure. With a requirements checklist for HR and compliance teams. February 27, 2026 12 min read Governance & Compliance gosign.de/magazine Governance & Compliance
[H2] The EU AI Act Applies Worldwide.
The EU AI Act isn't European overregulation. It simply writes down what every legal system already demands: Explain your decision. February 25, 2026 7 min read Infrastructure & Technology gosign.de/magazine Infrastructure & Technology
[H2] Agent Orchestration Platforms Compared (2026)
Where do your AI agents run? Trigger.dev, n8n, Camunda, Temporal, Make and Activepieces compared for enterprise use. With recommendation logic. February 23, 2026 10 min read AI Agents & Use Cases gosign.de/magazine AI Agents & Use Cases
[H2] From Chatbots to AI Agents: MCP, A2A and Multi-Agent Systems
What sets AI agents apart from chatbots. MCP and A2A protocols, agent architecture, multi-agent orchestration for enterprises. February 23, 2026 12 min read Infrastructure & Technology gosign.de/magazine Infrastructure & Technology
[H2] What AI Really Costs: TCO Comparison for Enterprises
Token prices are misleading. The four cost categories of enterprise AI - with three scenarios from €26K to €410K. February 23, 2026 8 min read Infrastructure & Technology gosign.de/magazine Infrastructure & Technology
[H2] AI Hosting: EU SaaS, German Data Center, or Self-Hosted?
Three hosting strategies for enterprise AI. Decision matrix by data sensitivity, cost, and control. February 23, 2026 10 min read Infrastructure & Technology gosign.de/magazine Infrastructure & Technology
[H2] Enterprise AI Infrastructure Blueprint 2026
Eight strategic decisions for your AI infrastructure. Models, hosting, interfaces, agents, orchestration, governance, costs, and regulation. February 23, 2026 5 min read Governance & Compliance gosign.de/magazine Governance & Compliance
[H2] Decision Layer & Shadow AI: Control Instead of Chaos
How the Decision Layer separates analysis from decision - and why that solves shadow AI, convinces works councils, and enables scaling. February 23, 2026 11 min read Governance & Compliance gosign.de/magazine Governance & Compliance
[H2] Decision Layer vs. SAP Joule vs. Copilot
SAP Joule and Microsoft Copilot are AI agents. The Decision Layer is the governance layer above them. Why enterprise organizations need both. February 23, 2026 5 min read Infrastructure & Technology gosign.de/magazine Infrastructure & Technology
[H2] Enterprise AI Portals: Five Open-Source Interfaces Compared
LobeChat, OpenWebUI, LibreChat, chatbot-ui and very-ai - five enterprise AI portals compared. Features, SSO, PII protection, governance, self-hosting. February 23, 2026 9 min read Governance & Compliance gosign.de/magazine Governance & Compliance
[H2] EU AI Act 2026: Status, Deadlines, Action Items
EU AI Act 2026: prohibitions active, AI literacy mandatory, high-risk deadline August 2026. Timeline, obligations, and action items. February 23, 2026 11 min read Governance & Compliance gosign.de/magazine Governance & Compliance
[H2] EU AI Act: HR AI Is High-Risk from August 2026
HR AI is high-risk under EU AI Act Annex III. Six mandatory obligations, deadlines, and how the Decision Layer meets each requirement. February 23, 2026 6 min read Infrastructure & Technology gosign.de/magazine Infrastructure & Technology
[H2] RAG & Document Intelligence for Enterprise (2026)
RAG makes enterprise documents AI-accessible - without training, without data egress. Plus: PII anonymization and contract redaction. February 23, 2026 10 min read HR & People Operations gosign.de/magazine HR & People Operations
[H2] Three Types of Decisions: Human, Rules, or AI
Not every decision needs a human. And not every decision should be left to AI. A framework for assignment - with concrete HR examples. February 23, 2026 7 min read HR & People Operations gosign.de/magazine HR & People Operations
[H2] Works Council & AI Literacy: The Organizational Questions
Why AI projects fail on organization, not technology. Co-determination as a design requirement and mandatory training since 2025. February 23, 2026 10 min read Finance & Payroll gosign.de/magazine Finance & Payroll
[H2] Decision Layer: Eliminating Payroll Errors
Payroll errors don't come from carelessness - they come from implicit expertise. The Decision Layer makes decision logic explicit and auditable. February 22, 2026 6 min read Infrastructure & Technology gosign.de/magazine Infrastructure & Technology
[H2] AI Infrastructure in Your Existing IT Landscape
How AI agents and LLMs integrate into SAP, Workday and cloud landscapes - no greenfield, no shadow IT, no platform migration. February 21, 2026 7 min read Governance & Compliance gosign.de/magazine Governance & Compliance
[H2] AI Governance Dashboard: Agent Monitoring for Enterprises
How an AI governance dashboard makes agent activities transparent for IT, works councils and internal audit. Audit trail, model monitoring. February 18, 2026 7 min read Finance & Payroll gosign.de/magazine Finance & Payroll
[H2] Measuring the ROI of AI Investments
How CFOs evaluate the ROI of enterprise AI. Process costs, error rates, audit effort as measurable KPIs instead of vague productivity promises. February 14, 2026 7 min read Governance & Compliance gosign.de/magazine Governance & Compliance
[H2] PII Anonymization for Enterprise AI
How to process documents containing personal data with AI while maintaining GDPR compliance. Roundtrip pseudonymization, Decision Layer, audit trail. February 10, 2026 6 min read Governance & Compliance gosign.de/magazine Governance & Compliance
[H2] Cert-Ready by Design - Audit-Proof AI from the Start
Cert-Ready by Design: controls as first-class data objects, automatic evidence generation, live auditor status. Architecture for ISA and SOC 2 February 7, 2026 5 min read Governance & Compliance gosign.de/magazine Governance & Compliance
[H2] Human-in-the-Loop - Architectural Principle, Not Checkbox
Human-in-the-Loop for AI agents means architecturally enforced human review, not optional approval. Confidence Routing, escalation rules, bias checks. February 4, 2026 7 min read Infrastructure & Technology gosign.de/magazine Infrastructure & Technology
[H2] Hosting DeepSeek in Your Own Infrastructure
How enterprises deploy DeepSeek R1 and other LLMs GDPR-compliant on Azure, GCP or self-hosted. Architecture, data sovereignty, model-agnostic approach. January 28, 2026 8 min read Infrastructure & Technology gosign.de/magazine Infrastructure & Technology
[H2] Model-Agnostic Architecture: Avoiding LLM Lock-In
Decouple business logic from the language model. Agents, Decision Layer, and rule sets stay unchanged when models switch. No vendor lock-in. January 21, 2026 4 min read HR & People Operations gosign.de/magazine HR & People Operations
[H2] Works Council and AI: Co-Determination by Design
Works agreements as technical constraints in the Decision Layer. Don't convince the works council, implement their requirements as rules. January 14, 2026 4 min read Governance & Compliance gosign.de/magazine Governance & Compliance
[H2] Decision Layer Explained: Governance for AI Agents
The Decision Layer: Rules Engine, Confidence Routing, Human-in-the-Loop, Audit Trail. Governance between AI agent and target system January 7, 2026 8 min read Infrastructure & Technology gosign.de/magazine Infrastructure & Technology
[H2] AI Infrastructure, Not Tool Hype: Enterprise Stack
AI tools vs. AI infrastructure: orchestration, governance, model-agnosticism, audit trail. Why enterprises need their own infrastructure layer June 19, 2025 5 min read Infrastructure & Technology gosign.de/magazine Infrastructure & Technology
[H2] LLM Self-Hosting for Enterprise - Azure, GCP, On-Premise
Self-host language models: DeepSeek, Llama, Mistral in your own infrastructure. Deployment options: Azure, GCP, on-premise, hybrid February 6, 2025 4 min read AI Agents & Use Cases gosign.de/magazine AI Agents & Use Cases
[H2] What Are AI Agents? Three Types for Enterprise
AI agents: Document Agents, Workflow Agents, Knowledge Agents. How they execute domain tasks autonomously and differ from chatbots and RPA January 9, 2025 9 min read Infrastructure & Technology gosign.de/magazine Infrastructure & Technology
[H2] AI Integration Into IT Landscapes: SAP, DATEV, Workday
How AI agents integrate into SAP, DATEV, Workday via Integration Layer and API decoupling. Booking logic separated from export. No parallel system. November 14, 2024 4 min read Governance & Compliance gosign.de/magazine Governance & Compliance
[H2] Shadow AI in the Enterprise - Governance Not Bans
Uncontrolled AI usage (Shadow AI) is a governance problem. The solution is controlled infrastructure with Audit Trail and Model Routing. October 8, 2024 5 min read Governance & Compliance gosign.de/magazine Governance & Compliance
[H2] ChatGPT Without Login at Work: From Risk to Infrastructure
Uncontrolled ChatGPT usage creates shadow AI at scale. How a GDPR-compliant, model-agnostic chat infrastructure with agent integration solves the problem. September 20, 2024 8 min read Governance & Compliance gosign.de/magazine Governance & Compliance
[H2] Data Security in Enterprise AI: Residency and GDPR
Data security in enterprise AI: Data Residency, EU-only processing, Row-Level Security, tenant isolation. Architecture decisions for CISOs and DPOs. September 12, 2024 5 min read

[H1] About Us
Enterprise engineering since 2001. Today: AI agents with governance. Auswahl aus über 5.000 Projekten in 25 Jahren Softwareentwicklung
[IMG: Airbus]
[IMG: Volkswagen]
[IMG: Shell]
[IMG: Renault]
[IMG: Evonik]
[IMG: Vattenfall]
[IMG: Philips]
[IMG: KPMG]
[H2] From Software Projects to Decision Architecture
Gosign was founded in Hamburg in 2001. For over 25 years, we have built complex software systems for mid-market companies and large enterprises - across more than 5,000 projects for organisations including Airbus, Volkswagen, Shell.
What has changed is not our standards - but the technology.
Our roots are in classical enterprise software: system integration, bespoke platforms, ERP-adjacent development and security-driven architectures. As generative AI emerged, we began testing LLM-based workflows in real enterprise environments - initially experimental, then productive in Azure-based enterprise setups.
Today, we build Enterprise AI Agents and the infrastructure that makes them accountable: the Decision Layer, which breaks every process into individual decision steps and defines for each step: human, ruleset, or AI.
[H2] What We Stand For
[H3] Decision Quality Over Automation
Enterprises rarely fail because of missing tools. They fail because of inconsistent decision logic.
HR and finance decisions in many organisations are not versioned, not reproducible and dependent on the knowledge of individual employees. Our architecture makes decision logic explicit, traceable and auditable - as a technical layer, not as organisational consulting.
[H3] Governance by Design
Compliance is not an afterthought in our work. Our agents are built so that decision logic is versioned, Human-in-the-Loop is architecturally embedded, audit trails are generated automatically and controls exist as data objects. When certification is required, the system is structurally prepared.
More on our Governance pages.
[H3] Integration, Not Replacement
We do not replace existing systems. SAP remains ERP. Workday remains the HR platform. SuccessFactors remains the system of record. We add a decision and governance layer - the Decision Layer that makes agents auditable and controllable.
[H2] How We Work
[H3] Co-Build with Business Functions
Every enterprise has its own compensation logic, its own collective agreements, its own governance requirements and its own ERP structures. That is why we develop AI agents in a Co-Build model with HR, finance and IT - not as a finished product, but configured to the specific processes and rules of the organisation.
[H3] Full Source Code Access
Our solutions run in the client’s infrastructure - on Azure, GCP, AWS or self-hosted. No proprietary SaaS model, no vendor lock-in. Full access to source code, configurations, and rule sets. Handover to independent operation after 12-18 months is part of the model.
Gosign is deliberately not a dependency model.
[H2] Technologies
Python, TypeScript/Node.js, Go (Golang) - Frontend: React, Next.js, Vue - Database: PostgreSQL, Supabase - Orchestration: Trigger.dev, Camunda, Airflow - Containers: Docker, Kubernetes - Cloud: Azure, GCP, AWS - On-Premise: vLLM, Ollama
[H2] Certifications and Competencies
Our team combines over 25 years of enterprise experience with current cloud and AI certifications. Our AI engineers hold Microsoft Azure AI certifications and work daily with LLM APIs, orchestration frameworks and enterprise integration systems.
[IMG: BVDW Member]
Member of the German Federal Association for the Digital Economy (BVDW)
[H2] Who We Are
Gosign is owner-managed. 108 employees across offices in Hamburg, Berlin, Kraków, Barcelona, Lisbon and São Paulo. Our largest development centre has been operating in Pakistan since 2002 - founded as a programming school, today an established engineering hub with close ties to the computer science departments at universities in Karachi, Lahore and Islamabad.
[IMG: Bert Gogolin, Managing Director Gosign GmbH]
[H3] Bert Gogolin
Managing Director
LinkedIn
[IMG: Dieter Gogolin, Managing Director Gosign GmbH]
[H3] Dieter Gogolin
Managing Director
Bert leads client engagements: from initial analysis through process mapping to architecture decisions. Dieter is responsible for operational and strategic business development - from infrastructure and international development teams to partnership strategy.
[H2] Why We Do This
Generative AI is changing how decisions are prepared, documented and accounted for. We believe that AI agents only scale productively when governance is structurally integrated, decision logic becomes explicit and humans remain deliberately involved.
That is what we build the infrastructure for.
All company data at a glance: Facts & Figures
[H2] Frequently Asked Questions
How long has Gosign been in business? Gosign was founded in Hamburg in 2001. For over 25 years, we have built complex IT systems for enterprises including Airbus, Volkswagen, Shell. Since 2023, our focus has shifted to Enterprise AI Agents and AI infrastructure. Why no SaaS model? Our solutions run in the client's infrastructure - Azure, GCP, AWS or self-hosted. Full access to source code, configurations, and rule sets. Gosign is deliberately not a dependency model. What sets Gosign apart from AI startups? Over 5,000 enterprise projects, 108 employees and 25 years of experience in regulated environments: SAP integrations, employee oversight requirements, compliance processes. We understand the reality of corporate IT, not just the possibilities of LLMs. Built for the most demanding regulatory environment globally - German co-determination law, EU AI Act, and GDPR.
[H2] Let's Talk
We look forward to your project. Get in Touch

[H1] References
From media agencies to audit firms: organisations with different requirements rely on Gosign infrastructure. Auswahl aus über 5.000 Projekten in 25 Jahren Softwareentwicklung
[IMG: Airbus]
[IMG: Volkswagen]
[IMG: Shell]
[IMG: Renault]
[IMG: Evonik]
[IMG: Vattenfall]
[IMG: Philips]
[IMG: KPMG]
Media Agency · 1,000 Employees
[H2] pilot Agency Group
Germany's second-largest independent media agency needed AI infrastructure that treats GDPR (UK: UK GDPR) compliance not as a constraint but as an architectural principle - for over 1,000 employees across seven locations.
Gosign delivered a self-hosted infrastructure with full EU data residency before other providers had even prioritised the issue.
[IMG: pilot Agency Group Logo]
"For me it was clear from the start: no data outside the EU, no compromises on GDPR. Gosign delivered exactly that - technically sound, no ifs or buts."
Read case study →
IndustryMedia & Communications
Employees1,000+
LocationsHamburg, Berlin, Munich, Stuttgart, Nuremberg, Mainz, Zurich
FocusGDPR-compliant AI infrastructure, EU data residency
ServicesAI Infrastructure, Agent Platform
Legal · Tax Advisory · Audit · 100 Employees
[H2] Roser Lawyers Auditors Tax Advisors
A multidisciplinary firm with 38 licensed professionals that audits others for compliance - and therefore sets the highest standards for its own AI infrastructure: professional secrecy under German criminal, bar association, tax advisory and auditor regulations.
Gosign delivered a self-hosted infrastructure with cryptographic client separation, Decision Layer and complete audit trail - documented to withstand chamber review.
[IMG: Roser Lawyers Auditors Tax Advisors Logo]
"As tax advisors and auditors, we audit others for compliance - so our own systems must be beyond reproach. Gosign's governance architecture meets exactly the standards we apply at our clients."
IndustryLegal, Tax Advisory, Audit
Employees100 (38 licensed professionals)
LocationsHamburg, Leipzig
FocusProfessional secrecy-compliant AI, Decision Layer, audit trail
ServicesFinance Agent, AI Infrastructure
Trade Logistics · 500+ Employees
[H2] Northern German Trade Logistics Company
An established trade logistics company with over 500 employees and one of the most efficient IT and logistics infrastructures in its industry relies on Gosign as a strategic advisory partner for AI integration.
Focus: Strategic consulting on integrating AI agents for customer support. The collaboration centres on how AI-powered support can be embedded into a complex, established IT landscape - without disrupting ongoing operations.
IndustryTrade Logistics
Employees500+
LocationNorthern Germany
FocusStrategic consulting, AI-powered customer support
ServicesAI Agents, AI Infrastructure
[H2] Frequently Asked Questions
Can Gosign share client names? We publish case studies with client consent. Many enterprise clients prefer confidentiality - the architecture and results are nonetheless representative. Which industries are the references from? Media, manufacturing, financial services, public sector. Our AI infrastructure is industry-agnostic - governance requirements are similar across sectors.
[H2] Which process should your first agent take over?
Talk to us about a specific use case in your organisation. Schedule a conversation

[H1] Talk to Us About a Specific Process
No sales pitch. We discuss your use case, your infrastructure, your rule sets.
[H2] Book Directly
30 minutes, video call, no obligation. No commitment, no pitch deck. We discuss your specific use case. ✓ Direct calendar link✓ Instant confirmation✓ No registration required
[H2] Contact Form
Write to us. We will respond within 24 hours.
[H2] Global Presence
Headquarters in Hamburg. Project management in your time zone. Engineering with 60+ developers.HeadquartersProject ManagementEngineering & Research
[H2] Headquarters & Governance
Executive leadership, governance architecture, DACH client projects.
[H3] Hamburg
Gosign GmbH
Hallerstraße 8
20146 Hamburg, Germany
[H3] Training Centre Hamburg
Grindelberg 77
20144 Hamburg
[H2] Project Management & Market Presence
Local project managers work with you directly - from discovery through to independent operation.
[H3] Berlin
Gosign GmbH - Berlin Office
Nogatstraße 46
12051 Berlin, Germany
[H3] Kraków
Gosign - Kraków Office
gosign.pl
[H3] Barcelona
Gosign - Barcelona Office
gosign.es
[H3] Lisbon
Gosign - Lisbon Office
gosign.pt
[H3] São Paulo (LATAM Office)
Gosign - São Paulo Office
gosign.pt
[H2] Engineering & Research
60+ engineers. University partnerships for research and talent development.
[H3] Lahore · Islamabad, Pakistan
Development centres with university partnerships:
University of the Punjab · COMSATS University
gosign.pk
[H2] What We Cover in the First Conversation
Which process should be automated?
Which systems are involved (SAP, DATEV, Workday, etc.)?
Which governance requirements apply (employee oversight, audit, compliance)?
Cloud, self-hosted, or hybrid?
Timeline and next steps