AI agents for business — email, customer service, knowledge search
AI agents for business are autonomous workflows built on Large Language Models (OpenAI, Anthropic). Unlike scripted chatbots, an agent can read an email, search your internal documents (RAG), make a routing decision, and update your CRM — within rules you define. We deploy via business-tier APIs, so your data is never used for model training.
An AI agent can reason, call your tools, and complete multi-step tasks without a human prompt at every step. Most useful where the volume is high and the task is well-defined. From €500.
AI agents at a glance
Companies overwhelmed by repeat email triage, internal-document search, or first-line support volume.
Basic email agents from €500. Plan for roughly €50–500/month in LLM API costs depending on volume.
Basic agent: 1–3 weeks. RAG knowledge search: 2–4 weeks. Multi-step process agents: 4+ weeks.
Deployed via Azure OpenAI or Anthropic Enterprise APIs. Customer data never used for model training.
What we build, with typical scope and price
We focus on three pragmatic agent categories: communication (email, customer service), knowledge (RAG search across your internal documents), and orchestration (CRM and back-office actions). Most projects pay back in 3–6 months by removing manual triage time and reducing data-entry errors.
Reads incoming mail, retrieves the relevant client and product context, and drafts a recommended reply for the salesperson within 10 seconds. Native Gmail / Outlook and CRM integration.
Handles repeated questions 24/7 and escalates complex cases to a human with the full conversation history. Web, email, or Slack.
An employee asks a question in plain language; the agent searches SharePoint or Notion and returns a precise answer with source citations.
Takes meeting transcripts, extracts action items, creates tasks in Asana or Jira, and posts the summary to the team channel.
Before a sales call, the agent pulls client history, prior touchpoints, public news, and a recommended angle — delivered as a one-page brief in under 2 minutes.
Multi-step autonomous execution: the agent calls REST APIs, makes routing decisions, and updates backend systems within rules you define.
What we build with
Primary general-purpose model — strong reasoning and tool use, broad ecosystem.
Alternative reasoning model, strong on long documents and instruction following.
Agent orchestration, API connectivity, and self-hosted workflow runs.
Semantic storage with Pinecone or pgvector (Supabase) for retrieval-augmented generation.
Enterprise deployment with EU data residency for GDPR-sensitive workloads.
Gmail, Slack, Notion, SharePoint, Jira, Asana, HubSpot, Pipedrive, and custom APIs.
Goal-directed, not script-driven
An AI agent is not a generic chatbot. It receives a goal, decides which tools to call, executes, checks the result, and repeats until the goal is met or it hits a human-review gate.
Example: "From this email, extract the claim details, check them against the policy database, and create a CRM record." The agent calls tools in a loop until the goal is met or an exception triggers a human review.
Example: "Find the fastest shipping route that stays within budget X." The agent compares options against your constraints and returns a single recommendation, with the reasoning visible in the log.
Three steps to production
We identify the friction points where an AI agent will pay back. Ranked by expected ROI and implementation risk.
We build the agent against your systems and test it on real cases, including edge cases and safety guards.
Team training, documentation, and active monitoring of reasoning accuracy after launch. We iterate on the prompts and tools based on real usage.
Human controls and audit logs
Autonomy is a spectrum. We default to high-oversight mode and reduce it only where you are comfortable. Every agent comes with a clear audit trail.
Sensitive actions (sending external email, posting to the ledger) pause for a one-click human approval in Slack or Teams. You decide what counts as sensitive.
Every decision, every API call, every prompt is logged. You can replay any run end-to-end to understand exactly what the agent did and why.
For sensitive workloads we deploy via Azure OpenAI's EU regions. Your prompts and documents stay in the EU and are not used for training.
Kill switch: if anything looks wrong, an operations lead can freeze all agent actions instantly.
Fixed-scope tiers
Plus LLM API costs of roughly €50–500/month depending on volume. Try the ROI calculator →
Related: CRM lead routing automation
A logistics company with 25+ sales reps cut lead response time from ~60 minutes to 30 seconds by automating routing in Pipedrive with Make. The same routing logic underlies many of our AI email and customer-service agents.
Book a 30-minute call. We confirm whether an agent fits, and quote a fixed scope.
Frequently asked questions
- What is an AI agent and how is it different from a chatbot?
- An AI agent can make decisions, call external tools (APIs, databases), and complete multi-step tasks. A chatbot follows a pre-built script. An agent can search your documents, send emails, and execute actions on your behalf within defined rules.
- How much does an AI agent project cost?
- Email or meeting-summary agent: €500–1,500. Internal knowledge search agent (RAG): €800–2,500. Multi-step process execution agent: €2,500–8,000. On top of delivery, plan for roughly €50–500/month in LLM API costs depending on volume.
- Will my data be used to train OpenAI or Anthropic models?
- No. We deploy via OpenAI Enterprise or Azure OpenAI tiers (and Anthropic business APIs) where customer data is contractually excluded from training. This is the key difference from public consumer tiers.
- Can the agent search SharePoint, Notion, or Google Drive?
- Yes. With RAG (Retrieval-Augmented Generation), the agent can search SharePoint, Notion, Google Drive, or any source that exposes an API or can be exported to a structured format.
- How long does an AI agent deployment take?
- A basic email agent: 1–3 weeks. A RAG knowledge search agent: 2–4 weeks. Complex multi-step process agents: 4–10 weeks depending on data structure and integration depth.
Want to scope an AI agent?
A 30-minute call where we confirm fit, scope the work, and quote a fixed price. No commitment.
Request an AI agent consultation
Tell us about the workflow. We come back with a fit assessment and a fixed quote within 48 hours.
- ✅Free 30-min audit
- ⚡Response within 24 hours
- 📋Firm pricing quote
- 🔒GDPR compliant
- 🤝Zero initial obligations
47+ companies have already automated processes with our help