AI Agent for Business Processes — a Worker That Never Sleeps
An AI agent for business processes is an autonomous software worker that triages inbound emails, qualifies leads, drafts offers, reconciles invoices and summarizes meetings — without an operator scripting every step. Unlike a chatbot that waits for a question, the agent initiates actions on its own, uses GPT-4 and Claude for reasoning, RAG for access to your data, and function calling to write into CRM, ERP and accounting systems. It replaces 0.4–1.2 FTE in a typical mid-sized business — no sick days, no peak-load delays.
What the agent actually replaces
Not marketing magic — concrete roles and process steps that today consume time from people with higher-value work. This is business process automation moved one level up — from if-then workflows to agents with reasoning.
- ✓Inbox triage — reads sales@ or office@, classifies each email (enquiry, invoice, complaint, partnership, spam), extracts key fields and creates a task in the right system. Handles 200–600 emails a day.
- ✓Lead qualification on BANT — asks 5–7 follow-up questions over email or chat, scores budget, need, timing and authority, and pushes only the hot ones into CRM development. Cuts junk demo calls by 60–70%.
- ✓Drafting offers and invoices — pulls parameters from the conversation, generates a PDF quote on the right template, applies rule-based discounts and sends it. Time from enquiry to offer: 4 minutes instead of 2 days.
- ✓Invoice-to-PO reconciliation — compares supplier invoices with purchase orders, flags differences in price, quantity and VAT above EUR 50 for human review. Saves 12–20 hours a month in finance.
- ✓Meeting summaries and follow-ups — ingests Zoom, Teams or Google Meet transcripts, produces decisions-and-owners summary, emails participants and creates tasks in Jira, Asana or your ERP. Covers 100% of meetings, not 30%.
- ✓Account research before calls — before a sales call the agent reads the company's site, LinkedIn, last-90-days news and prepares a 1-page brief with pain points, players and talking points in 8 minutes.
- ✓Payment reconciliation — matches bank statements against invoices in the accounting system, marks paid ones, escalates overdue accounts to finance with a ready dunning draft.
Who it is for
Agencies, consultancies, law firms, IT shops — where the core work is written communication and offers. One agent covers 3 inboxes, drafts 20–40 quotes a week and arms sales reps with account briefs. Replaces one assistant FTE.
Firms with 500+ supplier invoices a month, complex pricing and many SKUs. The agent reconciles invoices against POs, catches supplier errors (3–5% of orders show a delta) and recovers overpayments. ROI: 6–10x annually on the build cost.
Stores with 100+ orders a day and SaaS companies with tiered support. The AI agent answers 70% of tickets without a human, escalates the rest with a ready draft and writes refund or replacement entries directly into ERP development.
How we build it
We are not reselling an off-the-shelf AutoGPT platform. We build the agent as custom software on OpenAI and Anthropic APIs, with n8n or our own orchestrator, with full control over actions, memory and data access. Code and logs stay with you.
1. Discovery and process mapping
We sit with the team and write down 3–7 processes the agent will take over — step by step, who runs them today, which systems are involved and which decisions require a human. Output: a process map with 30–80 actions and clear agent-authority limits.
2. Model and tools selection
We pick GPT-4 or Claude Sonnet/Opus on a cost-quality balance — GPT-4 for fast classification (EUR 0.30 per 1000 emails), Claude Opus for complex offers and negotiations. We define function-calling tools: read_email, create_invoice, query_crm, send_quote — each tool is a real REST endpoint in your stack.
3. RAG memory over your data
We index product catalog, price lists, contracts and client history in PostgreSQL with pgvector. Before responding, the agent cites which document it used — for audit and explainability. All data stays on your server.
4. Orchestration and guard-rails
We wire steps with n8n, Make or a LangGraph workflow — set limits (no quote above EUR 5,000 without approval), blocklists and retry policies. Every action goes through an audit log with timestamp, prompt and result.
5. Pilot, measure, scale
We run the agent in shadow mode for 2 weeks — it makes decisions, a human approves before send. We compare accuracy with baseline. Once accuracy passes 95% on the defined tasks, we switch to auto-mode with post-hoc review of a 5% random sample.
Why Saitami
Prices are fixed in EUR — no seat subscription, no per-conversation fees. Related scopes: AI assistant for companies for internal chat over your data and AI sales automation for lead scoring and follow-up.
Frequently Asked Questions
How is an AI agent different from a chatbot or a Zapier workflow?
A chatbot waits for a question and replies with text. Zapier runs a fixed if-then chain. The AI agent decides the next move on its own — it can read 12 emails, decide 3 need a quote, ask for clarification on 2 and escalate 1 to a human. It reasons about state, uses tools via function calling and keeps memory across steps through RAG. See AI chatbot for the comparison.
Whose data does the agent see and where is it stored?
Data stays with you. We deploy on your server or VPC in AWS/Hetzner. The vector store (PostgreSQL with pgvector) and audit logs never leave. Only prompts travel to OpenAI or Anthropic — and through zero-data-retention mode, so they are not used for training. You sign a GDPR DPA with us and with the model provider.
What happens when the agent makes a mistake?
Mistakes are a normal part of the workload — so every critical action (offer above threshold, send to a client, write into accounting) requires human approval or runs through 5% sample review. The audit log shows which prompt, which document and which tool call drove the decision, so a correction takes minutes. Accuracy steadily rises from around 88% in month one to over 96% after 90 days of iteration.
How much does the build cost and what are the monthly fees?
From EUR 4,800 for a production agent with 3 processes (e.g. inbox triage, offers, follow-up), RAG over up to 1,000 docs, function calling into 5 systems and an audit log. Complex cases with 7+ processes and compliance requirements start at EUR 9,500. Monthly cost is OpenAI/Anthropic API tokens — typically EUR 80–450 per month — plus EUR 40–90 hosting. No per-seat subscription.
Ready for an AI worker that never sleeps?
Describe one process that consumes most of your time today — inbox triage, offers, invoice reconciliation. Within 5 working days we return a process map with expected accuracy, production price and a realistic estimate of monthly hours the agent will free.
Request a process audit →Related services: AI assistant for companies · AI email automation · Business process automation