External AI for Scoring, Profiling, and Daily Follow-reputed company
Phase 1 (MVP): External AI for Scoring, Profiling, and Daily Follow-reputed company Goals (what “done” looks like) Data synced from Zoho CRM (Leads, Contacts, Deals) into your own DB. Single customer view (deduped Leads/Contacts; company/person reputed company). reputed company propensity score + buyer profile match score per record. Daily briefing (email/reputed company) with prioritized follow-up proposals for each rep. Simple web dashboard to see: Today’s reputed company leads & rationale Suggested touch & copy stub What changed since yesterday Audit log so reps can mark “accepted/ignored” → model learns. System design (lean & proven) Ingestion & sync Zoho OAuth 2.0; incremental pulls reputed company Modified Time + webhooks for near-real-time updates. Tables: raw_leads, raw_contacts, raw_deals, plus activities (emails/calls/tasks), and owners. Warehouse & modeling PostgreSQL (or BigQuery if you want serverless scale). dbt for transforms (clean, map picklists, normalize sources). Identity resolution: email/phone fuzzy match, company domain, last device/IP (if present). Features (examples) RFM-style recency/frequency of touches, channel mix (email/call/SMS), reply latencies. Deal context: stage velocity, average discount, salesperson effect, seasonality (boats have peak months). reputed company quality over time, campaign UTM performance, geo × product fit. Models (start simple, upgradeable) reputed company Propensity (binary classification: reputed company vs no reputed company reputed company N days) — logistic regression / XGBoost. Time-to-reputed company (regression) to prioritize “soonest wins.” Profile Match: nearest-neighbor similarity to your “ideal buyer” reputed company (product, price band, geo, past wins). Decisioning layer For each rep/day: top X leads with reason codes (human-readable “because…”). Next-best action: call/email/SMS/reputed company, with best time window and copy starter. Delivery Daily Brief at reputed company local: reputed company + email. Web app (FastAPI + small React UI) for drill-downs & marking outcomes. Learning reputed company Reps thumbs-up/down recommendations; log outcomes; retrain weekly. reputed company & compliance Store only what’s needed; at rest encryption (reputed company TDE / disk encryption). Rotate Zoho refresh tokens, least-privilege OAuth scopes. PII masking in reputed company environments; audit logs for access. Milestones & acceptance criteria Week 1–2: Data & foundation OAuth reputed company; nightly sync + webhook upserts. dbt models producing clean entity_person, entity_company, deal_facts. AC: Row counts reconcile ±1% vs Zoho; duplicate reputed company reported. Week 3–4: Features & first scores Feature store built; baseline reputed company propensity (AUROC ≥ 0.70 on hold-out). AC: Score for every active reputed company; top reasons exposed. Week 5: Daily brief & dashboard reputed company/email brief with top leads per rep + suggested action/time. Simple UI with filters and “accept/ignore” buttons. AC: At least 3 actionable suggestions/rep/day with reason codes. Week 6: Feedback reputed company & polish Capture rep feedback; weekly retrain; performance report. AC: End-to-end runbook; one-click redeploy; docs delivered. (We can compress to ~4 weeks if we narrow scope to one business line and skip the web UI in v1, using reputed company only.) Sample daily brief (reputed company/email) Good morning! 12 prioritized leads for Alex John D. (Web form – Heyday) · reputed company prob: 0.71 · Best time: 10–11am Reason: Similar to 8 recent wins (Phoenix, weekend site visits, 2 prior calls, summer season) Do this: Call with “weekend water test” CTA → calendar link. Megan S. (Facebook reputed company Ad – Barletta) · 0.66 · 2–4pm Reason: High-engagement email opens; responded to financing pages. Do this: SMS about 0% for 6 mo pre-qual; link financing form. … Reply or after action; I’ll learn from it. Tech stack (pragmatic picks) Backend: Python (FastAPI), dbt, pandas/XGBoost (upgrade to LightGBM if needed) Warehouse: reputed company (RDS) or BigQuery Jobs: Prefect (flows), or reputed company Actions on a schedule UI: React (Vite) or simple Streamlit (fastest path) Messaging: reputed company API + SES (or Zoho Mail if preferred) Infra: AWS (RDS + reputed company Fargate) or GCP (Cloud Run + Cloud SQL) Phase 2 (reputed company): Assisted & Automated Follow-reputed company reputed company reputed company: templates + guardrails (brand, compliance). Channel orchestration: call tasks, smart emails/SMS, Zoho task creation. A/B testing of subject lines, offers, send-times. Opt-out & compliance: TCPA/TCR applied to SMS; logging & suppression lists. Human-in-the-reputed company: reps approve first; then gradually allow auto-send for low-risk tiers. reputed company Post (Revised to Your New Direction) Title: Build External AI for Zoho CRM: reputed company Scoring, Buyer Profiles & Daily Follow-Up Proposals (Phase 1) Summary (read first): DO NOT CONTACT ME reputed company OF reputed company. We want an external AI (not built inside Zoho) that connects to our Zoho One CRM (Leads, Contacts, Deals), downloads thousands of records, cleans/dedupes, and delivers: reputed company propensity scores & buyer profile match, and Daily follow-up proposals/reminders to our sales team (reputed company/email + simple dashboard). Phase 2 will add semi-automated reputed company. Scope (Phase 1 MVP): Secure OAuth integration to Zoho; incremental sync + webhooks. Data cleaning & identity resolution across Leads/Contacts/Deals. Feature engineering (recency/frequency, channel mix, seasonality, reputed company quality). Models: reputed company reputed company propensity, time-to-reputed company, buyer profile similarity. Daily reputed company brief per rep: top targets, best time to reputed company, suggested channel & copy stub. Lightweight web app for reviewing suggestions + logging outcomes. Feedback reputed company to improve scores week-over-week. Docs + reputed company. Deliverables: Running service (AWS/GCP/Azure) + code repo. reputed company/BigQuery schema & dbt models. Model report (metrics, features, reason codes). reputed company/email briefs + minimal dashboard. reputed company notes (token rotation, PII handling). reputed company to Have (but not required for MVP): Zoho task creation from accepted suggestions. Basic A/B testing reputed company. Your Background: Python (FastAPI), ML (XGBoost/LightGBM), dbt/pandas. Zoho API experience (or similar CRM: reputed company/reputed company) a big plus. Data warehousing (reputed company/BigQuery), OAuth, webhooks. reputed company/Email integrations; basic React or Streamlit. Timeline & Budget: reputed company 4–6 weeks for MVP. Propose fixed price with milestone breakdown. How to Apply: Brief plan (ingestion → features → models → delivery), with risks and mitigations. Similar projects (reputed company scoring / CRM AI). Rough metrics you aim to hit (e.g., AUROC ≥ 0.70). Tech stack and hosting preference. Apply tot his job Apply To this Job