n8n workflows, OpenAI and Anthropic Claude APIs integrated into business processes. Lead assistant, document processing, RAG chatbot on your own data. Scope priced against saved hours, not promises.
Business context
Typical situations where it's worth talking to us. If more than one rings a bell, we can probably help.
The assistant spends 8–10 hours a week on email triage or data entry. n8n workflows can replace this, with measurable time savings and documented logic.
Prospects should get an offer within 24 hours — but often it's 48–72, because the request sits in an inbox. A lead-triage assistant classifies and prioritizes, sending an SMS when urgent.
PDFs (invoices, contracts, minutes) processed by copy-paste. OCR + LLM extraction with structured output: extracted fields go into Excel, CRM or ERP with validation.
Customer feedback, support tickets, sales call notes — information scattered. A RAG chatbot over your own docs: question → answer with source references. Support and sales find relevant data faster.
Deliverables
Concrete deliverables, not abstract capabilities. Documentation and handover included with every shipment.
Self-hosted n8n or n8n cloud, documented workflows. Webhook triggers, error handling, retry logic, monitoring. Workflow code exported and kept in git.
Prompt engineering, few-shot examples, output schema (JSON mode or tool use), cost monitoring. Token usage logged, monthly cost dashboard. Provider fallback.
pgvector or Pinecone vector DB, embedding generation, per-document chunk strategy. Source-linked answers so users can verify against the original document.
Inbound form/email classification (hot/warm/cold/spam), follow-up email draft, CRM ingestion (HubSpot, Pipedrive, Notion). Decisions logged for sales validation.
Tesseract or AWS Textract OCR, LLM extraction with JSON schema. Invoice processing, contract summarization, minutes structuring. Human-in-the-loop step on unusual values.
Output
Go-live is not the end, it's a handover. You receive every artifact below, and your team can work with all of them.
Every workflow exported as JSON and kept in git. You can migrate to your own n8n instance, or audit any time.
Version-controlled prompt store (Notion or git repo), with few-shot examples and change history. Your LLM team can iterate from here.
50–200 input-output pairs manually labelled, automatic regression test on every prompt change. Precision score queryable.
Token usage per workflow, daily and monthly cost, anomaly signals (e.g. a workflow's cost doubles).
For RAG projects, the pgvector or Pinecone index with documented schema and rebuild script.
Workflow descriptions for the business side, chatbot usage guide for end users, incident protocol.
Knowledge transfer call, joint troubleshooting of the first typical incidents, monitoring dashboard handed over.
Process
Six steps from discovery to support. The domain-specific content is tailored per project type.
Manual process mapping, ROI estimate, data source audit, data privacy review.
Workflow diagram (n8n), prompt engineering, RAG architecture, cost estimate (tokens).
n8n workflow building, OpenAI / Anthropic API integration, webhook endpoints, error handling.
Synthetic data tests, accuracy measurement, edge cases, hallucination check, prompt injection defense.
Live pilot for 2-4 weeks, error monitoring, fine-tuning, gradual rollout.
Cost monitoring (tokens), accuracy tracking, prompt refinement, new workflow additions.
Architecture
Typical RAG or workflow architecture: the user's input flows through n8n or a custom orchestrator, which decides which tools to call and which LLM to use.
Entry
Orchestrator
LLM and retrieval
Data
The user's input (web chat or inbound lead) enters the orchestrator, which is n8n or custom Node.js / Python code. The orchestrator decides: is retrieval (RAG) needed, is a tool call required, which LLM model fits this case.
The vector DB (pgvector or Pinecone) holds indexed documents. The knowledge base can be a PDF archive, Notion export, or a live API. Every LLM call is logged so we can measure cost and accuracy.
Stack
Proven, documented tools. When the project justifies it we deviate — but we always explain why.
Integrations
Typical AI stack and integration points. The choice depends on the use case.
Timeline
A simple n8n workflow 4–6 weeks. A RAG chatbot on your own data 8–12 weeks. A complex multi-agent system 12–20 weeks.
Use case definition, ROI estimate, initial eval set scoping.
Proof of concept with a minimal prompt against 10–20 examples to validate feasibility.
n8n workflow, prompt iteration, integration, cost monitoring stood up.
Accuracy measurement, prompt iteration, edge case coverage, user feedback loop.
Production deploy, monitoring on, knowledge transfer, 30-day hyper-care.
Limits
It's more honest to clarify upfront. Some kinds of work we intentionally don't take on — either it's not our profile, or we can't ship it at the quality we'd put our name on.
LLMs are non-deterministic. If output has to be 100% correct (invoice amounts, legal text), we don't put it on the critical path — a human review step is needed.
Foundation model training (LLM pre-training) isn't in our infra or knowledge base. Fine-tuning via the OpenAI/Claude API is on the table; training a new model isn't.
LLMs hallucinate by nature. We ship eval sets, source citations and human-in-the-loop validation. 'Guaranteed correct' is not a professional promise.
If the client doesn't want insight into the prompts and workflows, we're not the right partner. Everything is handed over documented and transparent.
First week
The first week is about validating the use case and producing a feasibility prototype.
Monday
What task we'd automate, what's the measurable goal, who's the end user.
Tuesday
Which data is available in which format, what's the GDPR posture.
Wednesday
A minimal prompt + 5–10 test cases to see baseline accuracy.
Thursday
Token usage estimate against expected volume, monthly cost.
Friday
PoC → MVP → Production scope, eval set design, decision points.
Pricing
Three engagement models. Each one comes with a precise scope after discovery — concrete numbers follow once we've seen the project.
Precise workflow description and fixed price after discovery. Eval set precision target (e.g. ≥90%) defined, handover precisely documented.
Ideal when
Well-scoped workflows, lead classification, document extraction with fixed templates.
Iteration delivered in sprints. Prompt tuning, eval set growth and new use cases work well in T&M because scope evolves naturally.
Ideal when
RAG chatbots, complex multi-step workflows where scope is experimental.
Monthly fixed capacity for workflow operation, monitoring, prompt tuning. SLA-like response time, guaranteed availability.
Ideal when
Maintaining production AI systems, continuous accuracy improvement, edge case coverage.
Case studies
Anonymized cases that fit this pillar. Detailed write-ups are prepared from our project logs.
FAQ
Next step
Concrete scope, transparent pricing, documented delivery. The first 30-minute call is free, and after NDA we share concrete numbers.