Over 110 AI services across 7 consoles — from agents and infrastructure, through data, voice and security, to apps, PropTech and deployments.
Each area is a terminal full of concrete deployments — from customer service and sales agents, through self-hosted LLM and RAG, to data pipelines, lead scoring, and offer portals. We start with an audit and pilot: pick one process, demonstrate a working system, and only then discuss scaling. Access to models always goes through a router, with PII masking before the cloud.
cashcrown@lab: ai.agents --listready
$ collections / payment-reminder agent— polite reminders, escalation on your thresholds, hard cases to a human
$ meeting-scheduling agent— finds calendar openings, proposes slots, confirms — conflicts go to a human
$ content-moderation agent— flags comments and submissions by your rules; borderline calls go to a moderator
$ candidate pre-screening agent— sorts CVs and intake answers against your criteria; the recruiter decides who to invite
$ monitoring & alerting agent— watches metrics, price changes or mentions and pings only when it actually matters
$ multi-agent orchestration— a coordinator agent splits work across specialist agents and keeps the result coherent
$ contract-review agent— flags risky clauses and deviations from your template; the final call stays with a lawyer
$ invoice-processing agent— reads invoices, matches them to orders and pre-posts them; mismatches go to human approval
$ data cleaning— standardization, gaps, typos — with a report of what changed and why
$ deduplication & record matching— merges the same companies/people across sources, thresholds for human review
$ document digitization (OCR)— scans and PDFs → structured data, accuracy in ranges, human verification
$ audio & video transcription— recordings → timestamped text, run locally when data is sensitive
$ data labeling for ML— training annotations with quality control and double-checking
$ knowledge graph— entities and relations from your data, a base for RAG and search
$ anonymization & pseudonymization— anonymization takes data out of GDPR scope; pseudonymization reduces risk but data stays in scope — we match the method to the use case
$ data quality monitoring— rules and alerts for anomalies before they reach a report or model