Solutions Directory
AI agents built around your business outcomes
We build, connect, and operate agents that execute specific workflows across your existing systems. Explore our solution categories to see how we address core business operations.
Outcome-Driven Workflow Automation
AI agents are only as valuable as the business processes they execute. Rather than deploying generic conversational chatbots, Knemetic designs and operates bounded execution agents tailored to measurable operational outcomes across your stack.
Solution Categories vs. Reference Architectures
- Solution Categories define the operational business functions we serve—Revenue, Workflow Automation, Customer Operations, and Engagement.
- Reference Architectures demonstrate how concrete agents coordinate work, integrate systems, and enforce deterministic human controls.
Explore Our Solution Areas
Select a category to view its business value proposition, typical workflows, and flagship reference architecture.
Revenue
Agents that qualify leads, run outreach, and book meetings.
Leads sit in the inbox overnight while competitors call them back. Outbound pipelines run dry because personalised research takes too much manual effort. Revenue teams lose opportunities when your sales team doesn't have the capacity to act instantly on every lead.
A coordinated workflow combining prospect research, qualification, personalized engagement, and sales-system updates to manage the prospect-to-opportunity process.
Customer Operations
Agents that understand context, resolve tier-1 issues, and take action.
Traditional chatbots only answer questions. True customer service requires action. We build AI customer support agents that triage inboxes, fetch customer context from your billing systems, help resolve tier-1 tickets, and execute support workflows end-to-end.
An employee service workflow that retrieves approved knowledge, processes requests, initiates authorized actions, and escalates unresolved issues with the relevant context.
Workflow Automation
Agents that coordinate work and financial processes across your systems.
Most business friction happens in the gaps between systems. Invoices arrive in email and require manual validation against purchase orders before entry into the ERP. A form is filled, but the CRM isn't updated. A ticket is closed, but billing isn't notified. We build agents that coordinate across systems, handling document-driven workflows, financial reconciliation, and cross-API automation with strict business controls.
An AI-assisted invoice-processing workflow that combines document extraction, deterministic validation, exception handling, and controlled accounting-system updates.
Engagement
Agents that reach out proactively with actual customer context.
You have the data to know exactly when a customer needs to hear from you — upcoming renewals, reorder windows, lapsed usage. But triggering personalised outreach for every individual customer at scale is impossible manually. Valuable engagement moments simply pass by.
Triggering contextual communication based on milestones and customer usage patterns.
Selected Workflow Implementations
Real-world operational scenarios demonstrating how Knemetic designs, integrates, and operates agents across enterprise systems.
Missed calls converted to booked appointments — without a receptionist
HVAC company, ~15 field technicians, heavy inbound volume in peak season
During summer peak season, the office team couldn't keep up with inbound call volume. Calls went to voicemail, follow-up was inconsistent, and estimates suggested a meaningful share of missed calls were walking away to competitors before anyone called back.
Support inbox handled in the team's voice — drafts ready before staff arrive
E-commerce brand, 8-person operations team, high support volume relative to team size
The support team spent a significant portion of every morning working through an overnight inbox backlog — mostly order status questions, return requests, and shipping queries that required checking two or three internal systems and writing a consistent response. The work was necessary but not where the team added value.
Inbound reply triage that routes the right leads at the right moment
Staffing agency, 50-person team, running ongoing outbound to client companies
The team was sending outreach at scale but managing replies manually. A large share of replies — out-of-office messages, "not the right person" redirects, timing-based deferrals — were consuming as much time as genuinely interested responses, making it hard for the team to prioritise.
Work & Scenarios
Representative Workflows & Case Scenarios
Illustrative examples of how AI agents can be designed to resolve operational bottlenecks across diverse business functions.
Missed calls converted to booked appointments — without a receptionist
HVAC company, ~15 field technicians, heavy inbound volume in peak season
During summer peak season, the office team couldn't keep up with inbound call volume. Calls went to voicemail, follow-up was inconsistent, and estimates suggested a meaningful share of missed calls were walking away to competitors before anyone called back.
After-hours calls captured and intake started before morning
Personal injury law firm, regional, significant after-hours inbound volume
Prospective clients often call during evenings and weekends — frequently in distress and looking for immediate reassurance. The firm had no after-hours coverage. Calls went to a generic voicemail, follow-up happened the next business day, and an unknown proportion of callers had already retained another firm by then.
Personalised outbound at volume — without a research team
B2B SaaS company, 30-person team, no dedicated SDR function
The sales team was responsible for their own prospecting. In practice, this meant sporadic outreach with limited personalisation — they didn't have time to research properly and still hit meetings. The pipeline was thin and inconsistent.
Ready to map your workflow?
Tell us where work currently moves between people and systems. We'll examine your architecture and determine how an AI agent could execute it reliably with appropriate controls.