Social Media Mentions Digest

Grow2.ai sets up an AI agent that turns scattered brand mentions into a single readable summary. The daily task of collecting and reading posts is removed from the marketer: instead of five browser tabs and 40 minutes of scrolling — one document with insights and a list of items requiring a response.

What the automation does

1. Collects mentions from social media — by brand name, products, key people, target hashtags, and untagged mentions.
2. Filters out noise — duplicates, spam, irrelevant name matches, bot posts, auto-generated content.
3. Classifies sentiment — positive, negative, neutral — and groups by topic: product, delivery, service, pricing, competitor comparisons.
4. Summarizes — turns hundreds of posts into a short digest of 1-2 screens: what happened over the past day, where a response is needed, which topics are gaining traction, which public voices mentioned the brand.
5. Sends real-time alerts for critical cases — a sharp spike in negative sentiment, a mention in a large account, a post with clear signs of customer churn, a recurring complaint.
6. Delivers the summary — every morning to the team's corporate messenger, weekly — an extended report with trends for the marketing manager.

The digest format remains configurable to fit the team's process: it can be split by product lines, regions, languages, criticality, or any other dimension already used in department reporting.

What the automation does NOT do

1. Does not respond publicly on behalf of the brand — publishing responses remains a human responsibility, the AI agent drafts a reply and submits it for approval.
2. Does not replace in-depth product research — thematic insights are suitable for operational response, but strategic hypotheses require separate work by an analyst.
3. Does not see closed sources — private chats, closed groups, messenger mentions without an API, and non-public reviews remain outside the monitoring scope.

The technical architecture is built around three layers: sources → processing → delivery. Each layer is isolated from the others, which allows changing the data provider or output channel without rewriting the entire system.

Data flow

1. Data sources connect via social platform APIs or third-party aggregators of the vertical-saas class for social monitoring. Data is pulled on a schedule — from 5 minutes for critical keywords to 1 hour for background queries.
2. Raw posts enter the processing queue. An AI agent running on an AI model goes through each mention: it determines relevance, sentiment, topic, and language.
3. Relevant mentions are stored with a period timestamp. This allows building trends and comparing day-to-day or week-to-week.
4. Summarization runs on a schedule — typically once a day in the morning — and produces three blocks: critical (requires action today), important (requires discussion this week), informational (trends and background).
5. The finished digest is delivered to the team's communication channel — Slack, Telegram, or email — with a link to the full version in Notion or an internal dashboard.
6. An alert trigger runs in parallel: if a single post or cluster exceeds criticality thresholds for reach, spread rate, and emotional tone, a notification is sent immediately, without waiting for the morning digest.

Implementation steps

1. Define the keyword list — brand, products, key people, Cyrillic and Latin spellings, common misspellings.
2. Connect sources through the selected vertical-saas and verify coverage on retroactive data from the past 7-14 days.
3. Calibrate the AI agent on retroactive data: run the collected mentions through it, manually label 50-100 examples, adjust prompts for sentiment and topics.
4. Configure the digest template — what goes into the digest, how it is grouped, which KPIs appear in the header.
5. Connect the delivery channel and test it with the team for 1-2 weeks, collecting feedback on the format.
6. Enable alerts — separately for critical signals — and define the response protocol: who is on duty, how escalation works, what the SLA is.
7. Document the process and hand it over to the marketing team.

Typical components

The choice of a specific vertical-saas depends on the business geography and language requirements: for the Ukrainian and Russian-speaking segment, the tool selection differs from global English-language monitoring. Grow2.ai assists with selection and connection during the implementation stage.

Automation falls into the weekend complexity class: an MVP is deployed over a weekend, a production version — in 2-4 weeks.

Access and data

• Accounts for social platform APIs or a subscription to a vertical-saas for social monitoring.
• A list of keywords and spellings that describe the brand and key products.
• A communication channel (Slack, Telegram, email) with permissions to install a bot or webhook.
• Optionally — access to Notion or a BI system for detailed reports.

Team readiness

• A process owner in marketing who reads the digest once a day and makes response decisions.
• 1-2 hours of the marketing lead's time for initial prompt calibration and labeling of 50-100 test mentions.
• An agreed response protocol: who handles negative feedback, who escalates to the product team, who closes alerts.

Timeline

1. Week 1 — keyword collection, selection and connection of a vertical-saas, first tests on retro data.
2. Week 2 — AI agent calibration, digest template setup, delivery channel connection.
3. Weeks 3-4 — pilot with the team, collecting feedback, enabling alerts, handover to operational mode.

For the simplest MVP over a single weekend, a basic scheme works: one data source, one language, one delivery channel, no fine-tuning of topics. This is enough to verify the value before a full-scale implementation.