Stravolith is a new company. The builds below are demonstration systems we built to prove capability — not client case studies. Each demonstrates one of our five service categories. Once real client work exists, it gets its own section here with measurable outcomes.
Demonstration system — fictional home-services business
Problem solved
Missed calls become missed revenue — voicemail rarely gets a callback.
What the system does
Answers inbound calls, handles common questions, checks live calendar availability to book/reschedule/cancel appointments, transfers or escalates urgent and out-of-scope calls to a human, and texts a confirmation before the caller hangs up.
Technology stack
Vapi voice AI, Twilio telephony, calendar integration, SMS confirmations.
Safety & fallback behavior demonstrated
Urgent or ambiguous calls escalate to a human rather than being guessed at. Asking for "a person" hands off immediately. It identifies itself as an AI assistant when asked.
Demonstration system — multi-tool lead processing
Problem solved
Team manually copies data between email, forms, and CRM. Data entry errors, missed follow-ups, and hours wasted.
What the system does
Captures leads from multiple sources (forms, email, Slack), validates and deduplicates, enriches data, syncs to CRM, and triggers follow-up workflows automatically.
Technology stack
n8n/Make, API integrations, CRM (HubSpot/Pipedrive/Salesforce), email, Slack.
Demonstration system — invoice extraction and validation
Problem solved
Accounting team hand-enters data from hundreds of invoices per month. Errors, duplicates, and weeks of manual keying.
What the system does
Extracts vendor name, amount, date, line items from PDFs, validates against existing invoices, flags duplicates/errors for review, and syncs to accounting software.
Technology stack
OCR/LLM extraction, QuickBooks/Xero/NetSuite sync, cloud storage, n8n/Make workflows.
Demonstration system — website support assistant
Problem solved
Support team answers the same questions repeatedly. Knowledge lives in docs nobody reads. Customers wait for hours.
What the system does
Answers customer questions by searching approved company documentation, cites sources, hands off complex issues to humans, learns from feedback.
Technology stack
RAG (Retrieval-Augmented Generation), LLMs, document embeddings, website widget or Slack integration.
Demonstration system — multi-agent research assistant
Problem solved
Need AI capabilities not covered by standard products. Proof-of-concept before full build. Custom agent for unique workflow.
What the system does
Varies by project — could be a custom multi-agent system, AI feature added to existing software, specialized workflow automation, or proof-of-concept for new capability.
Technology stack
Custom LLM applications, multi-agent frameworks, your existing tools and infrastructure.