More than automation

Artificial intelligence that not only understands, improves, and accelerates processes - but thinks ahead.

We integrate AI where it goes beyond automation – where it uncovers relationships, informs decisions, and measurably transforms processes. Not as a gimmick, but as part of a well-designed architecture that delivers impact.

Measurable results — through targeted use of AI

Our AI projects target short-term measurable value: shorter cycle times, fewer follow-ups, and automated decisions with verifiable sources. We build robust RAG pipelines and controlled agent workflows (MCP) that are audit-ready, traceable, and production-grade. No proofs of concept without follow-through — always a clear path to operational use.

< 3 minutes
Delivery time per letter instead of 12–24 hours through digital mail processes.
70%
fewer requests to analysts thanks to SQL RAG in sales.
~ €900,000
relief potential through AI-assisted document querying.
100%
controlled use of AI tools through central platform architecture.

More than automation: AI as a business tool

AI creates real value when deployed where operational friction exists — not as an end in itself. We deliver production-grade solutions that produce fast, tangible effects: relieved teams, reliable answers, and measurable cost advantages. Our projects are built for traceability, governance, and maintainability.

Assistive solutions that think ahead

AI assistants prioritize tickets, create summaries, and provide recommendations — measurable in reduced response times and higher first-contact resolution. Example: ticket prioritization reduced response times by 40% in a client project.

Automation where it works

We automate processes with high manual effort (e.g., incoming invoices, delivery notes, application data) and deliver operational solutions in days rather than months — including measurement of cycle times and error rates before/after rollout.

Security by design

AI solutions are built in secured environments: dedicated tenants, role-based access, logging, and deletion concepts. Results are traceable (sources + version) and audit-ready — ideal for regulated industries.

Scaling with care

We design AI as part of the architecture: containerized, centrally licensed, and modularly integrable. Outcome: predictable OPEX effects (example: up to 30% reduction in operating costs with scaled use of a single use case).

AI Risk & Governance

Shadow AI: How to stop hidden AI use & secure compliance

The debate about a potential AI bubble is more than buzzwords: lots of money and attention flow into pilots, yet without integration, hard KPIs, and product linkage, the benefit often fails to materialize. In parallel, frustrated users drive shadow AI: they secretly use external AI services because internal tools are too slow or cumbersome. Without pragmatic governance, clear KPIs, and user-centered design, real data, compliance, and reputation risks emerge — and officially launched projects lose measurable value.

95% failure rate

in enterprise AI pilots

MIT study shows: only 5% of GenAI pilots generate measurable P&L impact

95% of pilots currently deliver nothing actionable

Large-scale study shows: only a very small share of enterprise GenAI pilots generates measurable P&L impact; the majority remains stuck in PoC/trial — often due to integration and organizational issues.

Shadow AI isn’t a rumor — it’s measurable

Proxy/SSO scans often reveal employees using external LLMs/tools. Visibility (inventory) is the foundation of any prioritized countermeasure. (Deliverable: anonymized inventory CSV after the check).

Reasoning models have real limits

Studies show a “complete accuracy collapse” on very complex tasks — results from reasoning modules require verification before steering decisions.

4-step action roadmap

1

60-min Rapid Inventory & Risk Map

In 60 minutes we run a quick scan and deliver three concrete results: (1) an anonymized inventory CSV — a list of all detected AI tools with no personal data; (2) the top 3 risks with proposed owners; and (3) a one-page pilot brief (goal, metrics, scope). Timebox: 60 min. (Deliverables: CSV, short risk deck, pilot brief)

⏱ 60 min
2

Central Azure OpenAI interface (gateway)

Set up a central Azure OpenAI interface usable by all internal tools: an API gateway + management layer that centrally controls traffic, authentication, DLP, logging, and costs. Individual external LLM licenses become unnecessary; all model calls go through a controlled, auditable point. Timebox: 2–4 weeks (MVP). (Deliverables: architecture blueprint, gateway deployment (MVP), SSO/key management, DLP integration, usage & cost dashboard, migration plan for existing access)

⏱ 2–4 weeks (MVP)
3

Pilot design: 1–2 use cases with hard KPIs

We plan and launch 1–2 tightly focused pilots with clearly measurable goals (e.g., shorter cycle time, FTE saved, lower error rate). Includes hypotheses, success criteria, an integration plan (auth, logging, data flow, DLP), and a KPI report for evaluation. Timebox: 4–8 weeks. (Deliverables: hypotheses & criteria, integration plan, KPI report)

⏱ 4–8 weeks
4

UX MVP: Consumer-grade UX with enterprise controls

We build a simple, user-friendly MVP interface that integrates SSO, audit logging, and prompt masking. Goal: employees get a secure, convenient alternative to external tools. Includes an adoption dashboard and a communications package for early adopters. Timebox: 2–4 weeks. (Deliverables: MVP UI, usage dashboard, communications kit)

⏱ 2–4 weeks

Recommended attendees

  • IT/Security
  • Data Protection Officer
  • 1 Business Unit Representative
  • Dev/Tech Lead

Pilot KPIs (measurable)

  • Cycle time (h)
  • FTE equivalent (h/month)
  • Error rate (%)
60 minutes • Free • Immediate results

AI Risk & Opportunity Check

In 60 minutes: shadow AI status, a prioritized KPI-driven pilot, and 3 immediately effective actions.

Deliverables: inventory CSV • risk assessment • pilot brief

Sources: Sources (short): MIT Project NANDA ‘The GenAI Divide: State of AI in Business 2025’ (finding: very low P&L impact for many pilots). Anthropic: ‘Inverse Scaling in Test-Time Compute’ (arXiv). Apple Research: ‘The Illusion of Thinking’ (limits of reasoning models). Google: Gemini 2.5 / ‘Nano Banana’ blog post (example of multimodal risks & opportunities).

RAG: Retrieval-Augmented Generation – reliable answers from your data

RAG couples LLMs to verifiable, controlled sources. This reduces hallucinations, enables source citation, and makes answers audit-ready — a prerequisite for productive use in regulated environments.

Why DEVDEER for RAG?

We deliver RAG from data ingestion to secure answers — including governance, versioning, and ops.

Smarter access to documented knowledge

Indexing, metadata, and retrieval pipelines ensure reliable hits. Every answer is backed by a retrieval path (source, document version, score).

Data connectivity — secure & flexible

Connect SharePoint, Blob Storage, databases, and line-of-business systems via secure connectors. Encryption, Key Vault integration, and access restrictions are standard.

Governance & compliance by design

Per-document authorization, query logging, and deletion concepts ensure traceability. Index retention and source hashing reduce risks.

Concrete application examples

How our clients use RAG productively today — with clear ROI and audit evidence.

Document Q&A & SOP assistance

Policies, contracts, and SOPs are searched in seconds and answered with source citation. Result: fewer follow-ups, faster decisions, and complete traceability.

SQL RAG for Sales

Natural language → validated SQL answers. Sales gets precise information without SQL expertise and reduces back-and-forth with data teams.

Knowledge portals for service & operations

First-level teams resolve far more cases at first contact thanks to versioned, citable answers.

What this delivers in practice

Specific technical properties drive operational benefits and audit safety.

Answers with source citation & document version
Audit-ready via traceable retrieval paths (source + hash)
Answers in seconds, even on large datasets
Fewer follow-ups for service and compliance teams
Maintainable & upgradable through modular index pipelines

MCP: Model Context Protocol – controlled access to systems & tools

MCP connects AI assistants to your systems (Jira, SAP, Confluence, etc.) and controls actions via roles, approvals, and logs. Actions remain traceable, authorized, and reversible.

Why DEVDEER for MCP?

Because secure integrations build trust — and strong architecture makes the difference.

Standardized interface

Unified connectors enable secure, repeatable actions by AI agents without tool sprawl.

MCP security as an architectural principle

Authorization (OAuth2 / RBAC), approval workflows, and logging are integral to every integration.

User-centered, not tool-driven

Role-based flows and intuitive workflows reduce context switching and increase team adoption.

Concrete use cases

Secure AI automation that actually works day to day.

IT assistance

Create, prioritize, and annotate tickets — audit-proof with evidence of action.

Data maintenance

Structure and update master data and catalog entries — logged and verifiable.

Reporting

Generate and distribute reports automatically — consistent, versioned, and scheduled.

What this delivers in practice

MCP workflows improve quality, speed, and governance simultaneously.

Faster IT handling through assistant automation in chat
More reliable master-data maintenance with audit trail
Automated, scheduled reporting without manual effort
Role-based approvals increase trust and traceability
Fewer context switches — more operational effectiveness

How to bring RAG & MCP into practice.

In just 30 minutes, we’ll show which of your use cases are fit for a productive MVP — including source strategy, security model, and scaling path.

30 minutes, no sales pitch — just concrete insights

AI Business Cases

Every project starts with a real challenge — and ends with a measurable result. We cut costs, increase productivity, and build technological structures that generate real business impact. Here we show how our work delivers in practice.

Digital mail & SOP assistance (RAG)

Bank, 1,000 employees (production)

6 weeks (PoC → rollout)

Problem Incoming mail and SOP queries took 12–24 hours; many follow-ups slowed business units.
Solution RAG pipeline: OCR → indexing (Blob + metadata) → retrieval + LLM with source citation. Access via secured web UI, audit logs, and versioning.
Result Delivery time <3 minutes, significantly fewer follow-ups, audit-ready answers with source citation.
Duration 6 weeks (PoC → rollout)
<3-minute delivery time (instead of 12–24 h) — measured in live ops>800 hours saved annually (basis: process savings × volume).
Measurement: before/after comparison over 3 months; document base: 60,000/year; hourly rate assumption €60/h.

SQL RAG for sales operations

Industrial client (DACH), sales team 12 FTE

8 weeks (incl. data integration & security review)

Problem Sales often had to ask analysts; long response times delayed quotes.
Solution RAG layer that translates natural language into validated, read-only SQL queries, with pre-run safety checks and result citation.
Result 70% fewer requests to analysts (from ~1,000 → 300/month), faster quote cycles, and higher first-time accuracy.
Duration 8 weeks (incl. data integration & security review)
70% reduction in analyst requests (basis: 3-month comparison)Response time: seconds instead of hours
Reduction measured via ticket statistics; SQL queries run read-only with enforced RBAC and audit logging.

IT assistance & ticket automation (MCP)

Mechanical engineering supplier, 320 employees

4 weeks (MVP) → ongoing optimization

Problem Helpdesk overloaded with routine tickets; SLA breaches accumulated.
Solution MCP integration: AI agent creates, prioritizes, and annotates tickets in Jira; actions remain logged and reversible (approvals included).
Result 40% faster first response, 30% more tickets resolved at first contact, complete action logs for audits.
Duration 4 weeks (MVP) → ongoing optimization
40% faster response time (live measurement)30% higher first-contact resolution
Metrics based on ticket stats 2 months before/after rollout; all actions evidenced via audit trail.

Frequently asked questions about RAG & MCP

What’s the difference between RAG and classic prompting?

RAG adds a retrieval step: before generation, verified sources are searched. The answer then includes the retrieval path (source + document version), which reduces hallucinations and enables audit-readiness.

Which systems can be connected via MCP?

Typical targets: Jira/ServiceNow (tickets), Confluence/SharePoint (knowledge), ERP/CRM (master data), mail/calendar (communication). Integration uses standardized connectors with OAuth2/RBAC and comprehensive logging.

How do you ensure compliance?

Through dedicated tenants, encrypted storage (Key Vault), role-based authorization, source-versioned indexes, and audit logs. Every production action is traceable and can be evidenced in audits.

How quickly can an MVP go live?

With preconfigured Azure architecture and already defined pipelines, often in 2–4 weeks. Goal: a production-grade foundation with clear follow-on paths — not just a PoC without continuation.

Ready to create impact?

Tell us briefly what it’s about – by email or in a non-binding conversation. We listen, ask the right questions, and show how we can help in a solution-oriented and pragmatic way.

Stefanie Heine

Stefanie Heine

Executive Assistant

hello@devdeer.com
+49 (0) 391 – 55 68 00 5 0
Herderstraße 31, 39108 Magedeburg
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