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chat app
We would like to develop a (demo) computational chat app for prompting, retrieving, and storing, responses from large language models (LLMs) and humans. Using vLLM and support the development of a broad experimental infrastructure for examining business solutions in application contexts. The ideal app should be a capable full stack developer, versed in front-end (TypeScript, React) and data analysis (Python), languages and frameworks. On top of that, we have an option to choose different models and have some metric to compare them as well like - cost, time, input / output tokens.
There are limitless possibilities for Checkmate! Is your business facing:
1. Customer Support: Dynamic Ticket Triage
Business Scenario:
A fintech company receives 10,000+ customer queries daily (e.g., "Why was my transaction declined?"). Their human team is overwhelmed, and generic chatbots often fail to resolve complex issues.
Sample Prompt:
“Automatically categorize and resolve 80% of customer support tickets. Escalate high-risk financial disputes (e.g., fraud claims) to human agents.”
CollabAI Response:
- AI Action:
- Routes simple queries (e.g., "How to reset my password?") to GPT-4 for instant resolution.
- Flags high-risk tickets (e.g., "Unauthorized wire transfer") with a low confidence score (e.g., <85%).
- Routes simple queries (e.g., "How to reset my password?") to GPT-4 for instant resolution.
- Human Action:
- Sends prioritized alerts to human agents via Slack with context (e.g., transaction history).
- Sends prioritized alerts to human agents via Slack with context (e.g., transaction history).
- Analytics:
- “Reduced average resolution time from 12 hours to 20 minutes. Saved $50K/month in support costs.”
- “Reduced average resolution time from 12 hours to 20 minutes. Saved $50K/month in support costs.”
Key Features Demonstrated:
- Confidence-based routing
- Integration with Slack/CRM systems
- Cost/time savings tracking
2. Legal Compliance: Contract Review
Business Scenario:
A law firm spends 200+ hours/month manually reviewing contracts for compliance with new EU data laws. Missed clauses risk million-dollar fines.
Sample Prompt:
“Analyze all contracts for GDPR compliance. Flag ambiguous clauses (e.g., data retention periods) for human lawyers.”
CollabAI Response:
- AI Action:
- Scans contracts using Claude-2 for clauses like “data storage duration” or “third-party sharing.”
- Highlights sentences with uncertain phrasing (e.g., “data may be retained for a reasonable time”).
- Scans contracts using Claude-2 for clauses like “data storage duration” or “third-party sharing.”
- Human Action:
- Sends flagged sections to lawyers via a secure dashboard with suggested edits.
- Sends flagged sections to lawyers via a secure dashboard with suggested edits.
- Analytics:
- “Cut review time by 70%. Identified 15 high-risk contracts in the first week.”
- “Cut review time by 70%. Identified 15 high-risk contracts in the first week.”
Key Features Demonstrated:
- LLM benchmarking (Claude vs. GPT for legal text)
- Secure human review workflows
- Risk mitigation metrics
3. E-Commerce: Product Moderation at Scale
Business Scenario:
An online marketplace struggles to vet 50,000+ daily product listings for prohibited items (e.g., counterfeit goods). Manual review is slow and error-prone.
Sample Prompt:
“Auto-approve safe product listings. Escalate listings with potential counterfeits (e.g., ‘Rolex watch $50’) to moderators.”
CollabAI Response:
- AI Action:
- Uses GPT-4 + image recognition to detect suspicious listings (e.g., mismatched brand/price).
- Blocks obvious scams (confidence >95%) and escalates borderline cases (confidence 60-95%).
- Uses GPT-4 + image recognition to detect suspicious listings (e.g., mismatched brand/price).
- Human Action:
- Sends escalated listings to moderators with AI-generated risk summaries (e.g., “Likely counterfeit: 80% match to Rolex logo”).
- Sends escalated listings to moderators with AI-generated risk summaries (e.g., “Likely counterfeit: 80% match to Rolex logo”).
- Analytics:
- “Increased daily listings processed by 300%. Reduced counterfeit sales by 90%.”
- “Increased daily listings processed by 300%. Reduced counterfeit sales by 90%.”
Key Features Demonstrated:
- Multi-modal AI (text + image analysis)
- Priority queues for human reviewers
- Fraud reduction tracking
4. Healthcare: Pre-Appointment Patient Screening
Business Scenario:
A hospital’s telehealth platform gets 5,000+ pre-visit patient forms daily. Nurses waste time on non-urgent cases (e.g., cold symptoms) while critical cases (e.g., chest pain) get delayed.
Sample Prompt:
“Triage patient forms by urgency. Route high-risk symptoms (e.g., shortness of breath) to doctors immediately.”
CollabAI Response:
- AI Action:
- Analyzes free-text responses (e.g., “I’ve had chest pain for 3 days”) with Med-PaLM (medical LLM).
- Tags low-risk cases (e.g., “sore throat”) as “AI-approved” and sends high-risk cases to doctors.
- Analyzes free-text responses (e.g., “I’ve had chest pain for 3 days”) with Med-PaLM (medical LLM).
- Human Action:
- Doctors receive HIPAA-compliant alerts with patient history and AI notes.
- Doctors receive HIPAA-compliant alerts with patient history and AI notes.
- Analytics:
- “Prioritized 200+ critical cases/week. Reduced nurse workload by 50%.”
- “Prioritized 200+ critical cases/week. Reduced nurse workload by 50%.”
Key Features Demonstrated:
- Domain-specific LLMs (Med-PaLM)
- Compliance with HIPAA/healthcare regulations
- Workload redistribution metrics
5. Marketing: Social Media Crisis Detection
Business Scenario:
A global brand’s social team misses early signs of PR crises (e.g., viral complaints about a product defect).
Sample Prompt:
“Monitor 100+ social channels for emerging crises. Escalate posts with sentiment <30% and >1K engagements.”
CollabAI Response:
- AI Action:
- Uses sentiment analysis (Python/NLP) to detect anger/disappointment in posts.
- Auto-generates draft responses for minor complaints but escalates viral issues (e.g., trending hashtag #ProductXFail).
- Uses sentiment analysis (Python/NLP) to detect anger/disappointment in posts.
- Human Action:
- Sends crisis alerts to PR leads with suggested action plans (e.g., “Issue apology draft”).
- Sends crisis alerts to PR leads with suggested action plans (e.g., “Issue apology draft”).
- Analytics:
- “Identified 3 emerging crises in 24 hours. Reduced response time from 6 hours to 15 minutes.”
- “Identified 3 emerging crises in 24 hours. Reduced response time from 6 hours to 15 minutes.”
Key Features Demonstrated:
- Real-time monitoring + sentiment analysis
- Automated response drafting
- Brand risk mitigation
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