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Wilcoe builds AI agent teams that do the work traditional SaaS only helps humans manage, inside the tools your company already uses.
For years, companies bought software to help humans do work. Now companies can deploy agents that do the work directly. The winners will not just have more apps. They will have better AI coworkers.
The problem is not that your team needs another productivity app. The problem is that your team already has too many. Every new tool adds another login, another dashboard, another set of buttons, and another workflow to maintain. AI agents change the equation. They do not just give your team a better interface. They take work off the interface entirely.
An AI coworker for every function, deployed inside the tools your team already uses. Each agent is trained on your company's context, connected to the right tools, governed by clear safeguards, and put to work where your team already is: Slack, email, dashboards, docs, or a custom portal.
The next generation of software may not be apps people log into. It may be specialized AI coworkers that live inside the tools teams already use.
Most productivity SaaS is a surface over a database. It hands people buttons, forms, dashboards, and automations, but a human still has to learn the interface, move between tabs, understand the workflow, and do the work. Agents invert that model. They can perform the work directly, using your context, tools, data, instructions, and approval rules. So the question stops being "what app should we buy?" and becomes "what work do we want this agent to own?"
One-on-one chat with an AI is useful. Team-based agents are different.
When an agent lives in Slack, the whole team can see the request, the context, the reasoning, the handoff, and the output. That makes AI feel less like a private chatbot and more like a coworker, working in the open where the rest of the team already talks, decides, and hands off.
Where this is going: Anthropic's Claude Tag points to the shift. Not a giant super app everyone has to adopt, but AI coworkers you can @mention inside the collaboration tools teams already use. Tagging Claude directly in a Slack channel is replacing the old Claude-in-Slack app. Boring Marketing is a market example of the same move: its product is now positioned as a Slack-based marketing agent that does the work, not another dashboard.
Not another assistant. A trained coworker for every function, built to own outcomes, not just help with tasks.
CEO, Owner, Partner
Turns scattered updates into decision briefs, market intel, meeting prep, and strategic options.
VP Sales, Account Exec, SDR
Monitors pipeline, drafts follow-ups, updates CRM notes, flags stalled deals, and prepares proposal language.
CMO, Marketing Manager
Plans content, drafts campaigns, repurposes assets, schedules posts, audits SEO, and reports what moved.
COO, Project Manager
Tracks open loops, generates SOPs, follows up on blockers, summarizes job status, and keeps projects moving.
CFO, Controller, Bookkeeper
Summarizes reports, prepares invoice follow-ups, flags budget variance, and organizes admin workflows.
HR Director, Recruiter
Answers policy questions, drafts onboarding materials, prepares review notes, and supports recruiting workflows.
Account Manager, Support
Summarizes accounts, drafts renewal outreach, prepares support replies, updates knowledge bases, and flags churn risk.
Technician, Installer, Driver
Delivers job briefings, safety checklists, step-by-step guidance, time-entry support, and issue escalation.
An agent is not a prompt. It is a trained teammate assembled from six parts.
The operating manual. How the agent thinks, writes, decides, and escalates.
The repeatable jobs it can perform: draft, research, summarize, analyze, schedule, update, report, and follow up.
The tools and data it can access: Slack, Notion, Google Drive, CRM, calendars, databases, analytics, and industry software.
The permission layer: what it can see, what it can do, when it needs approval, and what gets logged.
The recurring work it monitors or runs on schedule: weekly reports, pipeline alerts, content calendars, task follow-ups, and status checks.
Where it shows up: Slack, email, dashboards, mobile, voice, or a custom Wilcoe portal.
The more access an agent has, the more the rules matter. Wilcoe designs agents with approval paths, human-in-the-loop workflows, data boundaries, and clear escalation logic from day one.
Same work. Two different models.
| Traditional SaaS app | AI agent |
|---|---|
| Human logs into the app | Agent works where the team already is |
| App displays data | Agent interprets data and recommends action |
| Human clicks through workflows | Agent executes repeatable workflows |
| Tool has generic settings | Agent has role-specific instructions |
| Value depends on adoption | Value depends on work completed |
| Dashboard shows what happened | Agent summarizes, acts, and follows up |
| Another tab to manage | Another teammate to delegate to |
The best agents do not start as all-knowing digital employees. They start with one painful workflow, one repeated task, one measurable bottleneck. Once the agent performs that work reliably, we expand its skills, connections, and autonomy.
From first conversation to a fully operational AI team in weeks, not months.
We identify bottlenecks, repeated tasks, decision points, systems, and approvals.
We define the role, instructions, skills, tools, safeguards, success metrics, and surface.
We launch the agent where the team already works: Slack, email, docs, dashboards, or a custom portal.
We review outputs, tighten instructions, add skills, expand connections, and improve reliability over time.
The next wave of AI products may look less like SaaS dashboards and more like specialized agents that do the work.
These agents will not just display information. They will do work, ask for approval, learn company preferences, and operate inside the systems teams already use. Wilcoe helps companies build and deploy them before the market fully catches up. We do not start with the technology. We start with the work: the bottlenecks, repeatable tasks, approvals, risk, data, and outcomes.
The next major platform opportunity is agent templates: reusable, customizable agents built around specific workflows that businesses can install, permission, and deploy into Slack or other collaboration tools.
Wilcoe designs these agent systems around your actual workflows, not generic prompts. Every agent we build ships with:
Pre-configured agent teams for five high-impact verticals. Explore a live demo to see exactly what your company would get.
GC firms, HVAC, electrical, plumbing. 85 employees, $40M revenue.
Explore Demo →Clinics, dental, specialty practices. 45 employees, multi-location.
Explore Demo →Law firms, accounting, consulting. 30 attorneys + support staff.
Explore Demo →B2B software, dev agencies. 60 employees, $8M ARR.
Explore Demo →Regional manufacturers, wholesale distributors. 120 employees.
Explore Demo →Early deployments are already delivering measurable impact across every department.
Average time saved on routine tasks per employee
Hours per person reclaimed every single week
Average return on investment within the first year
No. Businesses will still need systems of record, databases, APIs, compliance layers, and specialized software. But the interface layer is changing. Many apps will become infrastructure that agents use in the background. The companies that win will either build the systems agents rely on, or sell agents that complete valuable work on top of those systems.
Where Wilcoe sits: at the workflow layer. We help companies turn their tools, data, and processes into agent teams that actually do the work.
Every tier includes agent instruction design, tool connections, safeguards, deployment into your workflows, and ongoing optimization.
$5,000 one-time setup. Up to 10 agents.
$10,000 one-time setup. Up to 25 agents.
$20,000 one-time setup. Unlimited agents.
Extend your AI team with additional capabilities as your needs grow.
What happens when a company is designed around agents first instead of apps first? Paperclip is Wilcoe's public experiment in the agent-first company. Instead of asking which SaaS tools a team should buy, we ask which agents can own the work: marketing, sales, operations, analytics, and reporting, with humans directing the system.
Every AI agent needs a brain. Here are the ones we build with, and how we use each to run marketing that actually works.
The thinking partner. Strategy, analysis, long-form content. Powers all 15 Wilcoe AI Skills.
Learn more →The Swiss army knife. Quick drafts, brainstorming, client-facing chat. Broadest use case.
Learn more →Lives inside your work. Deep Google Workspace integration, analytics, search, and ads.
Learn more →Give your brand a voice. Literally. Voice cloning, podcasts, video narration, multilingual.
Learn more →Research without the rabbit hole. Competitive audits, market analysis, trend tracking.
Learn more →Creative direction without a creative team. Ad visuals, brand imagery, mood boards.
Learn more →Everything you need to know before deploying your AI agent team.
Not all of them. Systems of record, databases, and specialized tools still matter. What changes is the interface. Instead of asking employees to log into every app and manually operate every workflow, agents can use those tools on the team's behalf.
A chatbot answers questions. An agent has a job. It has instructions, skills, tools, permissions, memory, and workflows it can execute or prepare for approval.
Slack is already where many teams discuss work, make decisions, ask questions, and hand off tasks. Putting agents there makes AI collaborative instead of private. The team can see the request, the context, the output, and the next step.
Yes. The goal is not to replace every tool. The goal is to connect agents to the right tools so work moves faster across the systems you already use.
We design each agent with clear permissions, approval rules, data boundaries, logging, and escalation paths. The more sensitive the workflow, the more human oversight we build in.
Most companies should start narrow. Pick one department, one workflow, or one recurring bottleneck. Once the agent proves value, expand into more roles and workflows.
Not at all. Each agent comes with suggested prompts and quick actions tailored to daily tasks. If someone can send a text message, they can use their AI agent. We also provide training sessions for your entire team as part of every plan.
Most deployments are fully operational within 2–4 weeks. Week one is discovery and agent design. Week two is build and testing. Weeks three and four are rollout, training, and optimization. Enterprise deployments with complex integrations may take slightly longer.
Yes. Monthly subscriptions can be cancelled at any time with 30 days notice. The one-time setup fee is non-refundable as it covers the custom development work. All your agent configurations and conversation history remain accessible during the wind-down period.
Book a discovery call. We will map your workflows and show you which agents would own the most valuable work first.
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Explore an Industry DemoContent coming soon.