What happens during the discovery call?

The discovery call is a structured working session — typically 45 to 60 minutes — where we walk through how operational workflows currently run inside your organization. We map the end-to-end process: which teams are involved, what systems they use, where handoffs happen, and where manual coordination creates bottlenecks. The goal is to identify 2–3 high-impact areas where AI agents can eliminate repetitive work, reduce cycle times, or remove human error. You'll leave the call with a clear picture of what automation looks like for your specific operations, along with a rough timeline and scope estimate.

How long does it take to deploy an AI agent?

Most deployments move from discovery to a production-ready agent in 6–12 weeks. The first 2–3 weeks focus on process mapping, system access, and architecture design. Weeks 4–8 cover development and integration testing against your real data and systems. The final phase involves deployment to production, monitoring, and iterative optimization. Simpler workflows — like automated AP exception triage or CRM enrichment — can go live in as few as 4 weeks. More complex multi-system orchestrations involving compliance checks or cross-departmental coordination typically take 10–12 weeks.

What kinds of tasks can AI agents actually perform?

AI agents execute operational workflows that would otherwise require a human to coordinate across multiple systems. Common examples include: monitoring incoming data streams and triaging exceptions, extracting and validating information from documents and emails, routing approvals through the correct chain based on business rules, reconciling data across ERPs, CRMs, and spreadsheets, generating internal reports and summaries, and coordinating multi-step processes like vendor onboarding or deal desk operations. Agents don't just respond to prompts — they proactively execute tasks based on triggers, schedules, and business logic embedded in your operational workflow.

Can agents work with our existing enterprise tools?

Yes. Agents integrate directly with the systems already running your operations — including SAP, Oracle, NetSuite, Salesforce, Dynamics 365, HubSpot, ServiceNow, Jira, Slack, and cloud platforms like AWS and Google Cloud. We connect via APIs, database access, and existing middleware. There is no need to migrate off your current stack or adopt a new platform. ProcAI agents work as an orchestration layer on top of your existing infrastructure, reading from and writing to your systems in real time while respecting your existing access controls and data governance policies.

How much involvement is required from our internal org?

Minimal. Your team provides context on the workflow and systems involved — typically less than 20 hours spread across relevant stakeholders over the first few weeks. This includes a discovery session, access provisioning, and review of the agent's logic before production deployment. ProcAI handles the full lifecycle: process analysis, architecture design, development, integration, testing, deployment, and ongoing optimization. After launch, your team interacts with the agent through existing tools — no new dashboards or training required.

How do you ensure security and governance?

Agents operate within your existing system permissions and enterprise security policies — they never have more access than a human operator would. All data processing stays within your cloud infrastructure, and we support deployment on AWS, GCP, and Azure. We follow SOC 2-aligned practices for development and data handling. Audit logs capture every agent action, making it straightforward to trace decisions for compliance reporting. For regulated industries, we work with your legal and compliance teams to ensure the agent meets sector-specific requirements before going live.

Can agents be fully customized for our workflows?

Yes. Every deployment is designed from scratch around the specific workflows, systems, business rules, and exception-handling logic inside your organization. There is no generic product or template — each agent is purpose-built for your operational reality. This includes custom decision trees, integration mappings, escalation paths, and output formats. If your workflow changes — new vendors, updated approval thresholds, additional data sources — the agent is updated to match. ProcAI treats each deployment as a living system that evolves with your operations, not a static automation.

How does pricing work?

Engagements are structured based on three factors: the complexity of the workflow being automated, the number of systems involved, and the operational scope (volume of transactions, number of teams, geographic distribution). A typical single-agent deployment costs a fraction of one full-time employee — often 90% less on an annualized basis. Pricing includes discovery, development, deployment, and ongoing optimization. There are no per-seat fees or usage-based surprises. We provide a fixed quote after the discovery call once we understand the full scope of the engagement.

Supplier Parsing with Claude and Firecrawl

Many popular brands in the US and Europe have a dealer locator on their websites. This map can be used to compile a list of suppliers across different regions, countries, and cities.

Hy-Lok dealer locator map showing supplier locations worldwide

This list can be useful for:

finding a supplier with the best price
partnership proposals
competitive brand analysis

But extracting this data manually is tedious and time-consuming.

In this article, I will show you how to automate this process using Claude and Firecrawl.

How to use Claude and Firecrawl for supplier parsing

Here is how it works:

Send a link to the dealer page in the claude.ai chat. For this example, I used this brand: hy-lok.eu/distributor-locator

Claude AI chat with a dealer page link request

Once you send the link, Claude will analyze the website and figure out how to extract all the suppliers.
When Claude finishes, it will compile all the suppliers into an XLSX file.