SEO for AI automation and safe agents

Adopt AI agents safely, without losing control of tools or data

Paput builds custom AI agents and hardens AI workflows around tools, prompts, connectors, memory, approvals, evidence, and recovery paths.

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What Paput solves

Paput helps teams in the Balearic Islands and Spain turn repetitive operational work into controlled AI workflows. The point is not another tool; it is automation designed around permissions, data boundaries, human approval, and recovery paths.

• Custom AI agents for operations and support.
• Workflow automation with clear boundaries.
• Agentic security hardening before production.

How the work starts

The first step is a practical audit: map tasks, tools, sensitive data, risks, and the points where a person must approve. Then choose a small pilot with visible value and low operational risk.

• Opportunity and friction map.
• Pilot design and success criteria.
• Controls, evidence, and rollback plan.

Security and trust

Agents connected to email, CRM, documents, or internal systems increase the attack surface. Paput designs controls around prompts, connectors, memory, permissions, logs, and irreversible actions.

• Least-privilege access.
• Human approval for sensitive decisions.
• Action logs and recovery paths.

Local fit

For businesses in the Balearic Islands and Spain, AI adoption must work in Spanish, Catalan where relevant, and English for international customers. Paput pages describe real service coverage, not invented branches or unsupported proof.

• Hospitality, real estate, professional services, and SMEs.
• Multilingual workflows and local operations.
• Privacy and compliance by design.

What Paput builds

Paput builds custom AI solutions that remove repetitive work, connect the tools you already use, and give your team more capacity without losing control of your data. Six ways to work together, all starting from the same audit.

• Custom AI agents: lead qualification, guest messaging, invoice processing, weekly reporting, SOP answers, and support triage.
• Agentic security hardening: map, test, and lock down the tool, connector, and memory surface before you scale.
• AI and security audit: the starting point of every engagement.
• Pilot programs: four to eight weeks from kickoff to a working prototype.
• Team workshops: one to five day engagements on safe AI use.
• Retainers: drift watched, evals run, permissions reviewed, logs audited.

Your team is losing hours to repetitive work

Data entry, document handling, customer follow-ups, reporting, inbox triage, and internal coordination drain capacity every week. Generic AI tools rarely fit the way your business actually works. Teams lose 20-30% of their week to this repetitive work.

• Generic tools miss your workflow: off-the-shelf AI rarely understands your terminology, approvals, systems, or customer handoffs.
• Your data needs boundaries: useful automation should not expose customer records, internal documents, or operational decisions to tools you do not control.
• Disconnected tools create more work: AI that does not connect to your existing systems becomes another silo instead of removing manual work between them.

Human control comes before autonomy

Every agent Paput ships carries the same four production controls, and nothing consequential happens without a person in the loop. Sensitive actions stay draft-only until your team reviews and approves them.

• Granular rollback: consequential actions stay reversible.
• Human review queue: high-risk actions route to a named owner.
• Searchable logs: every decision can be reconstructed afterwards.
• Trusted evals: quantified thresholds catch silent drift before users do.

Agentic security hardening for production AI systems

AI agents do not just generate text. They use tools, read context, call APIs, trigger workflows, remember information, and connect to business systems. Every one of those links can become attack surface. Paput helps you map, test, and harden those systems before they reach customers or critical operations.

• Agent, tool, and connector attack-surface mapping.
• Prompt and indirect-injection testing.
• MCP and tool permission review.
• Memory and context poisoning checks.
• Workflow rollback, approval, and recovery design.
• Audit and evidence logging for trust reviews.

You do not need another chatbot

Most businesses need practical automation that fits their existing workflows, protects their data, and produces measurable operational improvements. The path from busywork to capacity is three steps.

• Step 1, map the workflow: we identify the repetitive work costing your team time.
• Step 2, build the agent: we design and test a custom AI system around your tools, rules, and data boundaries.
• Step 3, scale what works: we deploy, train your team, and expand the automation once ROI is proven.

Start with the safest workflow worth automating

In one practical audit we map repetitive work, identify the agent surface area, estimate operational value, and define the controls needed before a pilot touches live systems.

• Workflow opportunities prioritized by value, risk, data sensitivity, and implementation effort.
• A first-pass risk map for prompts, tools, connectors, memory, permissions, and human approvals.
• Data-boundary notes: what the agent needs, what stays human-approved, and where privacy or connector risk appears.
• A recommended 4–8 week pilot with success metrics, security checks, and next steps.

Specific agents for the work your team already does

The best AI projects do not start with a model. They start with a workflow that already costs time every week.

• EUCompli: a 16-agent GDPR and EU AI Act compliance scanner, built and operated by Paput.
• Guest messaging assistant: handles common guest questions across email or WhatsApp and escalates sensitive issues.
• Invoice and document processor: extracts data from PDFs and emails, checks fields, and prepares entries for approval.
• Lead qualification agent: reads inbound enquiries, enriches company context, scores fit, and prepares the follow-up in your CRM.
• Weekly reporting agent: pulls updates from business tools, summarizes blockers, and drafts operator-ready reports.
• Internal SOP assistant: answers staff questions from approved documents with source links and escalation rules.
• Support triage agent: classifies messages, drafts responses, detects urgency, and routes the cases that need a human.

Tools these workflows connect to

Typical workflows connect the tools you already use, with least-privilege access and clear data boundaries.

• Google Workspace: mail, docs, drive.
• Microsoft 365: Outlook, Teams, SharePoint.
• WhatsApp: customer messaging.
• Airtable: operational data.
• Notion: docs and SOPs.
• Make and Zapier: workflow automation.
• Stripe: payments and billing.
• Calendly: scheduling.
• Your CRM, internal databases, and documents.

User stories that make the work visible

Until public client case studies are available, these anonymized pilot patterns show how Paput scopes measurable AI work.

• Local services: a lead triage agent drafts the first response and prepares CRM fields for approval. Target metric: first useful response in minutes, not next-day admin.
• Hospitality and tourism: a guest messaging workflow answers routine questions and hands off anything sensitive. Target metric: fewer repeated inbox replies during peak season.
• Professional operations: an operations agent drafts the weekly summary and highlights missing inputs. Target metric: hours recovered from recurring reporting work.

Who is behind Paput

Paput was founded by Gary Dobkin, with over a decade in cybersecurity, customer success and operations, including Perch Security and ConnectWise. He built and operates EUCompli, a 16-agent GDPR and EU AI Act compliance scanner.

• Gary Dobkin: over a decade in cybersecurity, customer success and operations.
• Practical approach: security built in from day one.
• EUCompli: a multi-agent compliance system built and operated by Paput.

Useful AI needs boundaries, not theatre

Paput builds agents around your tools, approvals, and data limits so automation increases capacity without turning into an uncontrolled black box.

• Clear data boundaries: what the agent can read, write, retain, and escalate is defined before anything goes live.
• Human approval points: sensitive actions can stay draft-only until your team reviews them.
• GDPR-aware design: workflows are scoped around minimization, retention, permissions, and provider choices.
• Measured rollout: pilots start narrow, measure value, then expand only when the workflow proves itself.

Field notes on running AI agents

Paput publishes short, source-transparent explainers on the decisions behind production agents.

• The four production controls every AI agent needs.
• Agent autonomy levels, from L0 to L5.
• Permission boundaries: least privilege for AI agents.

Questions buyers ask

AI operator field notes

illmethinks.io publishes source-transparent notes on AI agents, tools, and operational risk monitored by Paput.ai.