AI Integration & Automation
AI that
actually works.
Custom integrations of language models, RAG pipelines and autonomous agents into your existing systems. Production-grade — not a wrapped ChatGPT button.
PoC timeline
1–2 Wochen
Deployments
Your infra
Stack
No lock-in
What we integrate
Six capability areas — each scoped to your specific process, not a general product.
LLM Integration
OpenAI, Anthropic, Mistral or open-source models embedded into your existing systems. Streaming, function calling, structured outputs — the full API surface.
RAG Pipelines
Your documents, knowledge base, or product catalogue as a retrieval source. Accurate answers grounded in your data — no hallucination from general knowledge.
Process Automation
n8n, Make or custom Python agents: manual work replaced by workflows that run 24/7. Trigger-based, event-driven, fully monitorable.
AI Agents
Autonomous systems that plan, execute, and self-correct. Tool use, multi-step reasoning, memory — agents that actually complete tasks without constant supervision.
Document Intelligence
PDFs, invoices, contracts, emails — automatically read, structured and routed. Zero manual entry, directly into your ERP or database.
Predictive Analytics
Demand forecasting, anomaly detection, churn prediction. ML models trained on your data — integrated directly into your dashboard or API.
How we work
- 01
Analyse
We map your current processes, identify the bottlenecks that AI can genuinely solve, and agree on measurable success criteria. No hype — just honest scoping.
- 02
Proof of Concept
A working prototype in 1–2 weeks, against real data. We test accuracy, latency and edge cases before committing to the full build.
- 03
Production Build
Hardened for production: error handling, rate limiting, fallbacks, monitoring, cost controls. Deployed in your infrastructure — not locked into ours.
Real code, not demos
Production patterns: structured extraction, Zod validation, graceful fallback. The kind of detail that matters when it runs at scale.
// Real production snippet — structured extraction with fallback
const result = await openai.chat.completions.create({
model: 'gpt-4o-mini',
messages: [
{ role: 'system', content: EXTRACTION_PROMPT },
{ role: 'user', content: documentText },
],
response_format: { type: 'json_object' },
temperature: 0,
})
const parsed = InvoiceSchema.safeParse(
JSON.parse(result.choices[0].message.content ?? '{}')
)
if (!parsed.success) {
// Fallback: flag for manual review instead of crashing
await flagForReview(documentId, parsed.error)
return
}
await db.invoices.upsert({ ...parsed.data, processedAt: new Date() })Stack we work with
PostgreSQL
Vercel AI SDKOllamaFrom the Blog
Articles on AI & Automation
n8n Workflow Automation: Building Complex Processes Without Code
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Custom Weclapp Integration vs. Standard Software: ROI Analysis for SMBs
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ERP Integration for SMBs: Make-or-Buy Decision Framework — When Custom Development Pays Off
Custom ERP development or standard solution? This guide helps SMBs determine when building in-house makes financial sense versus purchasing proven platforms.
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7 Processes KMU Can Automate Right Now
KI Automation Checklist
7 concrete processes — from document processing to predictive analytics. Which ones fit your company?
Most SMEs already have the data for AI automation — they just don't know where to start. This checklist shows you the 7 quickest wins...
Start a project
A specific process in mind?
Tell us what you're trying to automate — we'll tell you whether AI is the right tool, and what it would take.
Start a project