All agent outputs, Google Calendar, Gmail signals, news feeds, market context, priority drift patterns from prior week

Morning digest email, prioritized daily checklist, Telegram push for hot items, voice briefing summary

Agent state log, acted-item history, calendar context, user preference profiles, priority weight adjustments

Priority accuracy vs acted items → refine weighting model; track topic drift week-over-week

Person bio, company context, relationship strength, event URL, calendar context, positioning doc, prior outreach log, LinkedIn export

LinkedIn note draft, email draft, follow-up cadence plan, target contact list, talk tracks, exec question set per audience type

Outreach history, response rates by message type, relationship map, event learnings, question bank by exec archetype

Response rates + which exec questions opened dialogue → continuously refine personal outreach playbooks

Continuously ingests signals across support, adoption, engagement, and commercial layers. When composite risk score crosses threshold, generates prescriptive intervention plan — not just a flag, a specific recommended action mapped to the account's lifecycle stage.

Composite risk score with signal breakdown, specific accelerator or activity prescription, auto-updated Service Delivery Plan, escalation register with urgency ranking

Account health baselines per lifecycle stage, prior intervention outcomes and conversion rates, signal threshold calibration log, at-risk account history

Accounts recovered vs churned after each intervention type → refine signal weighting model and threshold sensitivity per vertical

24 hours before each scheduled touchpoint, agent aggregates signals across all connected sources to generate a prioritized agenda draft reflecting what has actually happened since the last call — not what the field engineer remembered to type in.

Structured prioritized agenda with signal rationale, unresolved action item rollup, adoption change callouts, recommended discussion topics with supporting context from account knowledge repo

Action item completion history, meeting pattern per account, prior commitments log, topic recurrence tracking, open case continuity across calls

Agenda accuracy vs actual discussion coverage tracked post-call → improve signal-to-agenda prioritization and reduce noise topics

Parses live and post-call transcripts for language patterns that precede trust degradation — frustration signaling, hedged language, decreasing engagement reciprocity, competitor references, commitment resistance. Outputs split by audience.

Proactive recommendations to shift narrative: reframe suggestion, value proof point to surface, accelerator to prescribe, open question to rebuild engagement — delivered before next scheduled touchpoint

Early warning risk flag with conversation components cited, trust degradation trajectory, recommended manager action (observe / support IC / join next call / escalate), and confidence level

Which prescriptions triggered by sentiment converted to customer actions → improve recommendation quality and threshold calibration

Sentiment history per account, trust trajectory baseline, language pattern library for risk signals, competitor mention log, prior prescription outcomes

When a field professional is preparing a touchpoint or responding to an issue, ARC retrieves the most relevant internal resources, prior case resolutions, and peer delivery patterns — eliminating context-switching across disconnected enterprise systems.

Ranked relevant resources with source citations, peer delivery approaches for analogous scenarios, prior case resolution patterns, accelerator recommendations mapped to current lifecycle stage

Query pattern library, highest-utilized resources by scenario type, retrieval quality ratings from IC feedback, feature release versioning, document freshness tracking

Resource utilization and IC outcome ratings → refine retrieval ranking model and surface higher-signal content earlier; low-rated retrievals flagged for content gap review

ESP32 microcontrollers distributed across coop zones, each handling local sensor aggregation before pushing to Make.com automation layer. Architecture designed for low-power continuous monitoring with alert thresholds per sensor type.

DHT22 temp + humidity sensors, ultrasonic water level sensors, load cell feed weight monitors, PIR motion detection, ambient light sensor, RFID individual bird tracking — all via ESP32 local aggregation

Planned: daily summary report via Telegram, anomaly push alerts (temp excursion, low water, low feed), individual egg count log, weekly trend summary, voice query response via Whisper + TTS

Planned: seasonal baseline per individual bird, weather correlation history, feeding pattern log, anomaly event registry, sensor calibration records

Egg count prediction vs actuals → calibrate seasonal laying model; anomaly false positive rate → improve sensor threshold tuning