Use Cases
Real-Time Respond to Your Users:
Omni-Channel Brand Sentinel
Secure Your Social Reputation & Scale Capacity
The Problem
As the brand grows, every launch, story, and carousel brings more DMs (direct messages), comments, and public tags. The founder wants to stay close to the community, yet constant app-switching quietly drains focus and emotional energy.
Important messages can hide under a flood of notifications, and a single missed complaint or delayed reply slowly erodes trust. The team needs one place to see the full picture, decide what truly matters, and respond with warmth and precision.
The AI Agent
The system acts as a Social Response Command Hub.
It connects to official APIs for Instagram DMs, Threads tags, and keyword-based mentions, then streams all signals into a unified “social war room” view. Each item is auto-classified as support, collaboration, potential risk, or inspiration.
An agentic AI workflow then triages new messages into urgency levels, uses a custom Tone Pack trained on the founder’s voice, and drafts reply options that already sound human and on-brand. Sensitive issues trigger alerts and daily “T+1” briefs summarize public conversations with suggested responses, while every draft still passes through a human review step before sending.
Our Approach
This use case combines AI automation/ Agentic AI Workflows with brand-safe guardrails.
The Tone Pack is tuned from real conversations, branding strategies, and boundaries; workflows are designed around human-in-the-loop approval; and the architecture relies on official, compliant APIs instead of fragile scraping. The result is an operational layer that feels tailored to the founder’s way of speaking and thinking.
Leverage Users' Voices:
AI Creative Co-Pilot
Content Flow Automation Studio
The Problem
Audience quotes, feature requests, and personal insights appear across DMs, comments, interviews, and quick notes. Many of these UGC (User-Generated Content) moments could become powerful posts, yet they stay scattered in screenshots and chat logs.
At the same time, the brand needs a steady rhythm of carousels, Threads, newsletters, and videos. The creative team holds the taste and strategy, but much of their time goes into reorganizing raw material and writing first drafts from a blank page.
The AI Agent
The AI Creative Co-Pilot works as a content flow automation studio. It offers a simple “Idea Library” space where the team drops audience quotes, product feedback, and personal reflections, lightly tagged by topic, mood, and channel.
An agentic workflow then detects recurring themes, turns them into clear content pillars, proposes concrete posts, and auto-schedule the posts with human approval.
For each idea, the system generates multi-format drafts—a concise Instagram caption, a threaded text post, a short-video script outline—using the same Tone Pack as the response system. Each item comes with a structured visual brief that outlines layout, mood, and color direction, so designers can start from a focused specification instead of a vague request.
Our Approach
This use case is built on experience in content strategy, design collaboration, and language-model orchestration. The Agentic AI Workflows are shaped around how founders, writers, and designers actually work together: AI handles intake, clustering, and first tone-pack drafts; humans keep taste, nuance, and final approval. Visual briefs align with existing brand guidelines rather than replacing them.
Dynamic Pricing
Co-Pilot:
Volatile Product Quote Engine
Protect Your Margin & Free The Team's Focus
The Problem
For teams selling high-volatility products (e.g. index-linked or market-driven goods), every important quote becomes a mini-project. Someone senior has to check live reference prices, internal cost structures, FX impact, and the client’s past deals before deciding what feels “safe enough” to send.
That knowledge lives across spreadsheets, contract files, email threads, and memory. On busy days, this drains the team’s focus and emotional energy. Smaller deals or follow-up adjustments often receive less structured review, and the team has no single place that explains why a given quote “should” land at a certain level.
The AI Agent
The system acts as a Pricing Command Hub for high-volatility products.
It connects to licensed reference data for market-linked prices and FX rates, mirrors your existing spreadsheet logic in a hardened calculation engine, and ingests deal history plus contract notes to understand how each client and product has been priced over time.
An agentic AI workflow then:
calculates a fresh baseline price range for each upcoming quote based on live inputs,
cross-checks it against historical margin bands, typical discounts, and contract conditions for that client,
and generates a short, human-readable rationale: what changed, where the margin sits, and how it fits this relationship.
All of this is delivered as an internal brief (Email or simple view). The user does one thing only: review why the AI suggests this cost and price, then decide how to adjust based on strategy, negotiation, and how they want to grow this client.
Behind the scenes, floating reference prices, target costs, margin per client/product, and relationship patterns are already computed and aligned.
Our Approach
This use case combines AI automation / agentic AI workflows with relationship-aware pricing memory.
We start from the client’s current spreadsheets and real contracts, so the system reflects how the business already works.
Every quote and signed deal enriches an internal “client + product pricing memory” layer, allowing the AI to surface context like: “this client usually sits in this margin band for this product family at this volume.”
Human-in-the-loop review remains mandatory: the agent never talks to customers, only prepares baselines and explanations.
The architecture relies on licensed data sources and official APIs rather than scraping, and explanations use calm, neutral language that supports thoughtful decisions.