EA

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.

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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.

01

LLM Integration

OpenAI, Anthropic, Mistral or open-source models embedded into your existing systems. Streaming, function calling, structured outputs — the full API surface.

OpenAIAnthropicMistralOllama
02

RAG Pipelines

Your documents, knowledge base, or product catalogue as a retrieval source. Accurate answers grounded in your data — no hallucination from general knowledge.

pgvectorPineconeLangChainChroma
03

Process Automation

n8n, Make or custom Python agents: manual work replaced by workflows that run 24/7. Trigger-based, event-driven, fully monitorable.

n8nMakePythonWebhooks
04

AI Agents

Autonomous systems that plan, execute, and self-correct. Tool use, multi-step reasoning, memory — agents that actually complete tasks without constant supervision.

LangGraphAutoGenCrewAICustom
05

Document Intelligence

PDFs, invoices, contracts, emails — automatically read, structured and routed. Zero manual entry, directly into your ERP or database.

GPT-4oTesseractDocument AIunstructured.io
06

Predictive Analytics

Demand forecasting, anomaly detection, churn prediction. ML models trained on your data — integrated directly into your dashboard or API.

scikit-learnPyTorchFastAPIPostgres

How we work

  1. 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.

  2. 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.

  3. 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.

Structured JSON output with schema validation
Graceful fallback instead of runtime crash
Zero-temperature for deterministic extraction
Direct write to your database — no copy-paste
invoice-extract.ts
// 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

OpenAI APIAnthropicLangChainLangGraphpgvectorn8nPythonFastAPIZodPostgreSQLPostgreSQLVercel AI SDKVercel AI SDKOllama

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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.

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