Prep every deal before the call
Researches accounts, keeps the CRM current, and assembles proposals so reps walk in ready.
Begin where work already crosses systems, approvals, documents, and follow-up. Scale with the same governance model across departments.
Each team gets agents that run their real workflows — scoped, approved, and auditable — with the same governance model underneath.
Researches accounts, keeps the CRM current, and assembles proposals so reps walk in ready.
Coordinates research, content production, and reporting across every channel.
Works reconciliations, chases approvals, and assembles the evidence behind every number.
Coordinates recruiting steps, runs onboarding, and resolves employee requests within policy.
Coordinates vendors, routes work internally, and keeps status current across systems.
Triages incoming cases, drafts responses, and follows up on escalations under review.
Drafts specs, preps the roadmap, and coordinates releases from idea to ship.
Reviews intake requests, collects supplier documents, and routes approvals cleanly.
Handles contract intake, researches clauses, and coordinates review before expert sign-off.
Processes access requests, enriches tickets, and follows up on every change.
Tracks account health, preps renewals, and flags churn risks before they grow.
Pulls the numbers, builds the dashboard, and shares the insight on schedule.
Softworker is the core platform to build, orchestrate, govern, and operate AI agents — not a narrow departmental tool.
People and agents work the same threads, tasks, and approvals. Every hour someone spends multiplies into 10x the output.
Hours saved, cost avoided, and throughput gained, tracked automatically for every agent.
Apps, files, workflows, and context are scoped to each agent's role before execution begins.
Assemble a goal, skills, tools, and approval rules in a visual studio, then reuse the agent across your whole team.
Observability, evaluation, and evidence trails keep quality, cost, and performance visible at every step.
A business user describes the outcome in plain language. The AI agent turns it into steps, dependencies, tools, and approval points — before anything runs.
Move from manual coordination and high-risk setups to governed, resilient execution.
Team members waste hours copy-pasting data between Slack, email, Salesforce, Jira, and spreadsheets to gather context for a single request.
Your AI agent monitors channels, ingests source files (PDFs, calls, emails), extracts key facts, and prepares the workspace automatically.
Deploying raw LLM API connections to external databases or customer communications risks hallucinations, data leaks, or unauthorized operations.
Natively built-in permission policies restrict what data your agents can access, ensuring they only perform actions within safe, predefined boundaries.
Critical reviews require manual pings, causing operations to stall for days, or worse, actions are executed without any human verification.
Built-in approval gates freeze execution for sensitive steps (like sending emails or triggering payments) until a manager approves it in one click.
Workflows break and deadlines are missed because a manager forgot to review an email or a system token expired silently.
Agents persistently monitor execution state, send polite follow-up reminders to human approvers, and alert admins if a step requires attention.
Understanding why an AI generated a specific output requires digging through developer logs, prompts, and database records.
Every single prompt, raw context file, model decision, and human sign-off is logged in a centralized, immutable audit trail for complete compliance.
Common questions about rolling out governed AI agents across departments.
Contact us directlySales, Finance, Support, HR, Operations, IT, and more. Each team starts with a real workflow and expands across the organization under the same governance model.
No. During early access we configure and deploy your first workflows directly with your team, so you can start without engineering resources.
Permission policies restrict the data an agent can access and the actions it can take to predefined boundaries, and approval gates freeze sensitive steps like sending emails or triggering payments until a human approves.
Yes. Start with a single real workflow in one department, then extend the same agents and governance model to other teams as you build trust.