Nine Health Pharma Growth Partner
⚙️ Module 06 · Custom AI Agent

Custom Agent — An AI That Actually Does the Work

Task-oriented autonomous agents that reshape enterprise workflows

In one sentence

Autonomous agents built around business tasks, reshaping how work gets done.

Timeline
4–12 weeks
Deliverables
6 items
Use cases
5 types
Pain Points · The Problem

Why a Rebuild Is Necessary

Repetitive work consumes the team

Content production, data wrangling and sentiment monitoring eat an enormous share of working hours.

Systems stay siloed

Business data sits across disconnected platforms with no orchestration layer to act on it.

Decisions lack live intelligence

Markets, sentiment and competitors move faster than manual tracking can follow.

Capabilities · What We Deliver

What You Get

Workflow Orchestration

Multi-step processes decomposed into agent task flows that run themselves.

Tool and API Integration

Calls internal and external systems, databases and third-party services — it genuinely acts.

RAG and Reasoning

Combines private knowledge with enterprise data to make grounded autonomous decisions.

Observable Operations

Task logs, cost controls and anomaly alerts are fully transparent.

Technical Foundation

Agent Technical Foundation

Not chat — execution. We build agents that call tools and finish tasks, wired into our proprietary GeoPilot capability.

Multi-agent Collaboration

Planner, executor and verifier roles cooperate to decompose complex tasks.

Tool-calling Framework

Browser, API, code execution and database access under one orchestration layer.

Enterprise Knowledge Access

Connected to private documents and GeoPilot monitoring data.

Security Sandbox

Risky operations require human confirmation, keeping execution controlled.

Cost and Quality Governance

Token optimization, output QA and rollback.

Continuous Learning

Strategy iterates on feedback.

Deliverables · Deliverables

What Lands in Your Hands

  • Process diagnosis and agent design
  • Workflow orchestration development
  • Tool and API integration
  • Private knowledge integration
  • GeoPilot interoperability
  • Operations and iteration
Use Cases · Use Cases

Where It Fits

  • Customer service agents
  • GEO content production agents
  • Sentiment monitoring agents
  • Partner and lead acquisition agents
  • Data analysis agents

Typical timeline: 4–12 weeks, including post-launch GEO operations.

Let’s Talk About Your Case

Tell us where you are and where you want to go. We start with a straight diagnostic — including whether this module is the right thing for you right now.

Call us · 18126259547

FAQ

Frequently Asked Questions

How does GEO differ from traditional SEO?

SEO optimizes ranking within a list of links; GEO optimizes citation rate within a generated answer. SEO competes for position; GEO competes for presence. When users read one synthesized conclusion instead of scanning ten blue links, an uncited brand effectively does not exist.

Which AI engines does Nine Health's GEO service cover?

Ten leading generative engines: ChatGPT, DeepSeek, Doubao, Kimi, Ernie Bot, Qwen, Tencent Yuanbao, Zhipu, Perplexity and Claude, each monitored separately for brand keyword answers and recommendation placement.

How long before GEO results appear?

Timelines depend on each engine's knowledge update mechanism. Engines with live retrieval typically reflect newly indexed content within days; engines relying on training corpora take longer. Nine Health uses AIO monitoring to track answer changes continuously.

Are there compliance risks for GEO in pharmaceuticals?

Yes, and they must be handled upfront. Pharmaceutical content in China is governed by the Advertising Law and internet advertising regulations, which prohibit efficacy guarantees and absolute claims. Nine Health embeds a compliance review stage staffed by team members with pharmaceutical backgrounds.

How is GEO effectiveness measured?

Through three engine-level metrics — citation rate, recommendation position and answer accuracy — tracked continuously per brand keyword and reported as change over time, rather than raw traffic.