DRS Automation Group publishes what it ships. Capabilities validated in production. Active research threads we're studying. The technical stack we're qualified to operate against. And field notes - short observations from real engagements, written for operators considering artificial intelligence.
Every capability listed here has been deployed, tested, and operated for real clients. No pilots. No proofs-of-concept. Production systems with real customers on the other end of the line.
Full conversational artificial intelligence handling inbound calls - greeting, qualification, scheduling, dispatch routing, escalation. Live in production with sub-second latency.
Lead-qualification and multi-day follow-up agents over native SMS. Handles inbound, drips outbound, books appointments directly into client calendars. Sub-30-second response time on first reply.
Real-time skill-matrix routing with day-off detection, geographic radius enforcement, and load balancing. Pulls live spot-truth from the field-service system; never books a tech who isn't working.
Brand-voiced conversational artificial intelligence on Instagram DM, Facebook Messenger, and WhatsApp. Handles inbound qualification, routes to booking, escalates hot prospects. Three brands in production.
Bidirectional integrations with major field-service platforms - FieldRoutes, HubSpot, Zoho, custom CRMs. Real-time spot reads, write-back of confirmed appointments, lead capture into the right pipeline stage.
Heartbeat monitoring, OpenRouter credit alerts, FieldRoutes rate-limit tracking, daily metrics aggregation, automated daily snapshots. Watchdog runs every 15 minutes.
A handful of research threads we're actively investigating. Each one ties to a specific operator problem we believe artificial intelligence will reshape in the next 18-36 months.
We're documenting performance differentials between vertical-artificial intelligence systems (engineered for one industry) versus horizontal-artificial intelligence receptionists deployed at the same operator. Early data suggests vertical agents close 30-50% more qualified bookings per hundred calls handled. Working paper in progress.
Benchmarking Claude, GPT, and open-source models across the specific tasks operators care about - lead qualification, scheduling logic, dispatch decisions. Optimizing for the precision-per-dollar curve at production volume, not for benchmark scores.
Built and deployed a Phase-B optimization layer that auto-assigns the optimal technician on every booking - skill matrix, day-off detection, real spot occupancy load balancing. Eliminated dispatch overhead for the first PestPilot deployment.
Building a public taxonomy of failure modes we've observed in production voice deployments - silent failures, schema-drift errors, latency cliffs, escalation logic gaps. The goal is a shared vocabulary the industry currently lacks.
Mapping the characteristics that determine when a custom build is productizable into a vertical operating system versus when it should stay bespoke. Pattern recognition across pest control, automotive, mentorship, and field services.
DRS isn't a certifications-on-the-wall firm. We're qualified by the systems we've shipped and continue to operate. Every technology listed here is in active production for at least one client.
The bias is intentional: we go deep on the platforms that matter to operator-led businesses, instead of broad on every framework. When we recommend a stack, it's because we've run it under load.
If your business runs on something we haven't operated against, we'll tell you - and either bring in a vetted specialist or refer you to someone better suited. Honesty about scope is part of the qualification.
Working observations from real engagements. Written for operators considering artificial intelligence - and for ourselves, so the firm's institutional knowledge compounds.
The most under-priced variable in operator-led sales is response latency. The data is two decades old. Operator behavior hasn't changed. The arbitrage is still open - and artificial intelligence is the first thing that closes it.
FULL ESSAY · 9 MIN READ →The thing operators call "the artificial intelligence didn't work" is rarely the artificial intelligence - it's the connection between the artificial intelligence and the system of record. CRM write-backs failing silently. Calendar slots that don't exist getting offered. The model is fine; the plumbing isn't.
FULL ESSAY · 10 MIN READ →A generic artificial intelligence agent gets good at the first 80% of pest-control conversations quickly and then stays there. A vertical agent built for pest control keeps improving - because every edge case the firm encounters becomes part of the system. The narrower the scope, the steeper the learning curve.
FULL ESSAY · 12 MIN READ →We ship every system into production before sending the final invoice. It looks risky on paper. In practice it accelerates client decision-making and weeds out the operators who wouldn't have signed off anyway. The system has to actually work for the relationship to actually start.
SHORT · 3 MIN READRequest an evaluation. We'll bring the thinking, the stack, and the production track record to bear on your specific business.