Automated Service Ticket Response Assistant

Your AI-powered assistant for delivering faster, smoother, and more transparent customer support.

ASTRA ensures your requests are handled quickly and efficiently, by automating repetitive tasks, thus reducing wait times and improving the overall experience.

Using the customized LLM (based on OpenAI's ChatGPT, Google's Gemini or Anthtopic's Claude), ASTRA delivers reliable, high-quality responses that align with company policies.

Live Demo

$49.2K

Savings per year

9-15

Project Length (weeks)

Core Modules

24/7

Support Availability

VALUE

ASTRA: Value Delivered to Every User

We eliminate the pain points of traditional support - long waits, inconsistent answers, and opaque processes to deliver a truly satisfying customer experience.

Instant Resolution & Speed

AI automation cuts through queues and repetitive tasks, resolving common issues instantly. Fast and consistent responses are now the standard, not a luxury.

Reliable, Accurate Answers

ASTRA uses advanced models to ensure every response is consistently accurate and aligned with the latest company policies, building trust with every interaction.

Significant Cost Reduction

By automating over 80% of routine inquiries, ASTRA drastically reduces operational costs and overhead associated with high-volume support staff.

Effortless Scalability

Handle massive surges in customer demand without increasing headcount. ASTRA scales instantly to ensure zero drop in service quality or speed.

RESULTS

See ASTRA's Impact in Action

Real-world simulation demonstrating measurable improvements in efficiency, cost savings, and customer satisfaction

The Scenario

A company processes 10,000 support emails per month, with an average handling time of 5.5 minutes per email (reading + writing). This equals 916 hours/month, or about 6.5 full-time employees (FTE) dedicated to email support.

By implementing ASTRA, which automates 40–50% of repetitive queries within 6 months, and reduces email handling time to 45 seconds per email (verification only), the workload drops to 125 hours/month (~0.9 FTE).

Previous Solution

Manual triage + routing

6.5 FTE @ $2K/month each

With ASTRA

AI-assisted workflow

0.9 FTE needed

87%

Faster Processing

Response time: 48 hours → 6 hours

63%

Cost Reduction

Direct labor savings with better resource allocation

+24%

Customer Satisfaction

Faster, more accurate responses lead to happier customers

2-3x

Scalable Capacity

Handle volume spikes without hiring

Financial Impact

Workforce Comparison

Before ASTRA

6.5 FTE

Monthly Cost

$13K

After ASTRA

4.2 FTE

Monthly Cost

$8.4K

Savings Breakdown

Monthly Labor Savings

$4.6K

Annual Projection

$55.2K

AI Operating Costs

-$18K/year

Net Annual Savings

$49.2K

Payback period: ASTRA implementation costs recovered within first year through labor efficiency gains

ROADMAP

Implementation Phases

Our structured approach ensures smooth integration and measurable success at every stage

Phase 1 – Proof of Concept

Duration: 5–7 weeks

Goal: Validate AI response quality (at least 70% rated as "useful"). Verify organizational readiness for AI.

Queries are verified simultaneously by an employee and ASTRA
Responses from both sources are reviewed and compared by an expert
Activities: Collect source data, organize it, load into the model, identify edge cases, and perform initial model training

Phase 2 – Minimum Viable Product

Duration: 4 weeks

Goal: Ensure high-quality answers and model evaluation

ASTRA generates responses for all queries
Each response is rated by an expert on a 1–5 scale, with optional comments
In extreme cases, the expert can completely rewrite the response

Phase 3 – Production Implementation

Duration: 4 weeks

Goal: Ensure high-quality service and responding to user needs

Set confidence thresholds, enable auto-responses and auto-rejections
Complex cases are escalated to experts
Full integration with existing support workflows

FEATURES

Unleash Next-Level Capabilities

ASTRA's modular architecture combines specialized components to deliver comprehensive support automation.

Gen AI Message Drafting

Automatically generates high-quality response drafts for incoming requests, saving agents time while maintaining brand voice.

Dynamic Request Categorization

Classifies tickets in real time based on content, urgency, and complexity, enabling appropriate routing and prioritization.

Confidence Scoring Engine

Assigns confidence levels to AI-generated responses for transparency and decision-making about auto-approval vs human review.

Edge Case Detection

Identifies unusual or complex queries that require human intervention, preventing incorrect automated responses.

Human-in-the-Loop Workflow

Allows experts to validate, edit, or override AI responses, as well as score them and add comments ensuring compliance and quality.

Adaptive Learning & Model Training

Continuously improves response quality based on expert feedback and ratings, making the system smarter over time.

Tech Stack

Carefully thought out technologies that realize ASTRA

Core AI & NLP

Advanced language models and orchestration for intelligent automation.

LLM Platform: Azure OpenAI Service
Prompt Orchestration: LangChain / MS Agent Framework/Crew AI
Fine-Tuning & Embeddings: OpenAI Text Embedding 3 (small)

Data & Processing

Scalable data infrastructure for knowledge management and search.

Data Lake: Microsoft Fabric (OneLake)
Advanced Analytics: Azure Functions
Vector Database: Azure AI Search

Orchestration

Workflow automation and human-in-the-loop review processes.

Workflow Engine: Azure Logic Apps / Power Automate
Human Review: Custom UI + API integration

Integration

Seamless connectivity with enterprise communication platforms.

Channels: Outlook, Teams, Slack, Zendesk, ServiceNow
APIs: REST / Graph API

Monitoring & Reporting

Real-time insights and performance tracking dashboards.

Dashboard: Power BI
Observability: Azure Monitor

Security & Compliance

Enterprise-grade security and data protection measures.

Identity: Azure AD
Data Protection: Encryption at rest & in transit

REQUIREMENTS

Client Requirements

Successful implementation of ASTRA requires the following organizational readiness and technical capabilities

Organizational Readiness

Leadership support and team readiness for AI transformation.

Executive Sponsorship: Clear leadership support for AI-driven automation initiatives.
Dedicated Project Team: Includes IT, business analysts, and subject matter experts for knowledge base setup and validation.
Change Management Plan: Communication and training strategy to prepare employees for AI-assisted workflows.

Technical Prerequisites

Infrastructure and data requirements for successful deployment.

Data Availability & Quality: Access to structured knowledge base, FAQs, and historical ticket data for initial model training.
Integration Capabilities: APIs or connectors for email systems, ticketing platforms (e.g., ServiceNow, Zendesk), and collaboration tools (Teams, Slack).
Cloud Infrastructure: Azure or equivalent environment for hosting LLM, storage, orchestration, and analytics.
Security & Compliance: Identity management (Azure AD), encryption, and adherence to company policies.

Operational Requirements

Ongoing operational support and budget allocation for AI operations.

Human-in-the-Loop Workflow: Experts available to review AI responses during PoC and early phases.
Feedback Loop Participation: Human agents and experts provide ratings, comments, and quick validations to improve model accuracy.
Budget for AI Operations: Cover token usage, compute resources, and scaling costs (approx. $1.5K/month for LLM operations).