AI Integration Capabilities

Transforming Enterprise Products with Intelligent Solutions

Proven Track Record in AI-Powered Enterprise Solutions

Scroll to explore

Our Track Record

Our AI Integration Experience

We have successfully integrated AI into production enterprise systems across multiple industries, delivering measurable business value through intelligent automation.

RAG & Knowledge Retrieval Document Intelligence Conversational AI Voice AI Computer Vision MCP Integration
3 Production Systems
|
10+ AI Capabilities
|
Multi-Industry Experience
|
Azure, OpenAI & MCP Stack
PROJECT 01

Compliance Portal

Government & Education  |  Public Sector Compliance

The Challenge

Government agencies manage vast amounts of content across documents, websites, and databases. Staff spent excessive time manually searching for information, processing public records requests, and ensuring compliance with privacy regulations.

The Solution

An AI-integrated portal that automates document processing, enables intelligent search through RAG-powered chatbots, and transforms public requests into actionable task lists.

3AI Capability Areas
6+AI-Powered Features
AzureCloud Platform

AI Capabilities Overview

Three integrated AI pillars powering the platform

Intelligent Knowledge Retrieval (RAG)
  • Contextual chatbots for complex data queries
  • Azure OpenAI + Azure AI Search integration
  • Source-backed, grounded answers
  • Private data grounding for draft responses
Document Intelligence & Compliance
  • Automated PII detection & redaction
  • AI compliance auditing (copyright)
  • Multi-stage document processing pipeline
  • Sensitive data protection at every stage
Workflow & Task Automation
  • Auto-extract tasks from public requests
  • High-density AI summarization
  • Intelligent submission guardrails
  • Real-time PII & copyright validation

RAG Architecture: End-to-End Workflow

From data ingestion to AI-powered answers

1

Setup

On-demand provisioning per agency

2

Ingest

OCR, PII detection, chunking, embedding

3

Search

Hybrid vector + full-text search

4

RAG

AI-generated grounded answers

Phase 1: Organization Onboarding
New Organization Registers
System Saves Configuration
Data Source Mapping
Search Index Name
No Azure Search resources created yet. Just metadata — waiting for first data
Phase 2: Environment Auto-Setup
User Uploads First File
User Submits First Website
Search Pipeline Exists?
Yes → Go to Phase 3
Azure Functions Trigger
Create Search Index → AI Skillsets → Indexers
Environment Ready ✓
Phase 3: Continuous Ingestion
File Pipeline (Daily)
New File uploaded
OCR
PII Detection
Website Pipeline (Hourly)
New Website Content
Scrape & Clean
Smart Chunking
AI Embedding Generate Vector
Unified Search Index
Text chunks · Vector embeddings · Org filter · Access filter
Phase 4: AI Chatbot (RAG)
1. RETRIEVE — Hybrid Search
Vector search (Meaning) · Full-Text search (Keyword) · Org filter · Access level
2. AUGMENT — Build AI Prompt
System instructions · Retrieved content chunks · User's question
3. GENERATE — Grounded Answer
AI generates answer grounded in actual content with source attribution
User receives accurate, sourced answer ✓

AI-Powered Chatbots in Action

RAG-based conversational AI with source attribution

Data Accounting Chatbot
PIMS Data Accounting Chatbot
Public Records District AI
Public Records District AI chatbot

Users query complex data in natural language and receive grounded, source-backed answers with citation links.

Intelligent Request Analysis & AI Response

AI generates analysis, recommendations, and draft responses automatically

Right To Know / Requests — AI Response
AI Analysis and Request Response

AI Analysis

Automatically summarizes incoming requests and generates recommendations for staff.

AI Requestor Response

Generates draft responses grounded in agency data, ready for staff review and approval.

Key Features

Request summary generation
Eligibility criteria analysis
Step-by-step response drafting
Source attribution with citations

Automated Document Intelligence

PII redaction and compliance validation powered by AI

Smart Redaction Manager
Smart Redaction Manager screenshot
Document Validation & Compliance
Document Validation and Compliance screenshot

AI detects and redacts PII (SSN, addresses, phone numbers, emails) from documents. Copyright compliance is validated automatically before upload.

Automated Task Extraction from Requests

AI decomposes public requests into structured to-do checklists

Right To Know / Requests — Task Extraction
Automated Task Extraction screenshot

How It Works

  1. Public request is submitted through the portal
  2. AI analyzes the request description and attachments
  3. Decomposes into individual actionable tasks
  4. Generates a structured To-Do checklist for staff
  5. Staff track progress with completion percentage

Business Impact

  • Eliminates manual request interpretation
  • Ensures no action items are missed
  • Reduces response time significantly
  • Provides clear accountability tracking

Compliance Portal Technology Stack

Built on Microsoft Azure cloud services

Conversational AI
Azure OpenAI (GPT-4)
Embeddings
Azure OpenAI (text-embedding-3-small)
Search Engine
Azure AI Search (Hybrid: Full-Text + Vector)
AI Enrichment
Azure AI Services (OCR, PII Detection, Language)
Orchestration
Azure Functions (Serverless)
Storage
Azure Blob Storage + Azure SQL Database
PROJECT 02

Vehicle Service Platform

Automotive Industry  |  Digital Vehicle Service Platform

The Challenge

Vehicle service shops rely on manual customer interactions for quotes and bookings. Missed calls, slow responses, and limited hours result in lost revenue and poor customer experience.

The Solution

AI-powered chatbot and phone bot that provide 24/7 automated customer support, real-time pricing, vehicle diagnostics, and appointment scheduling.

2AI Channels (Chat + Phone)
24/7Customer Availability
Real-timeQuote Generation

AI-Powered Web ChatBot

Intelligent conversations that drive bookings

ChatBot Workflow

👤
Customer Starts
Chat Online
1. Inquiry or Request
🤖
AI ChatBot
2. AI ChatBot
Assesses Inquiry
💲 Quote Estimate
📅 Appointment Booking
AI Generates Estimate or Confirms Appointment
✉️
Email Confirmation
Sent to Customer
🏪
Email Confirmation
Sent to Shop

Live on Shop Website

joetestshop.com
AI ChatBot live on shop website
🔧

Answer Repair Questions

Provides automotive guidance and troubleshooting advice

💰

Generate Real-Time Quotes

Retrieves shop-configured pricing for accurate estimates

📅

Schedule Appointments

Books service visits and sends confirmation emails automatically

Personalized Experience

Each shop has its own AI assistant with custom branding

ChatBot: From Diagnosis to Booking

Complete customer journey handled by AI

Step 1: Diagnose
ChatBot diagnosing vehicle issue

AI identifies potential issues and offers troubleshooting advice

Step 2: Quote
ChatBot generating real-time quote

Real-time pricing retrieved from shop configuration

Step 3: Book
ChatBot booking appointment

Appointment scheduled with automatic email confirmation

AI Phone Bot: Voice-Powered Customer Service

Automated voice interactions using real-time AI

Phone Bot — Technical Architecture
Phone Bot Technical Architecture

How It Works

  1. Customer calls the shop number
  2. Call redirected to AI via Twilio
  3. AI processes speech in real-time
  4. Generates contextual voice responses
  5. Provides quotes & books appointments
  6. Sends email confirmations

Key Capabilities

  • Natural voice conversations
  • 24/7 availability
  • Real-time quote retrieval
  • Appointment scheduling
  • Call-to-shop transfer option
  • Emergency call redirect

Phone Bot: Real Voice Conversation

Actual conversation transcript between customer and AI

Live Call Transcript — AI Voice Response
Phone Bot real voice conversation transcript

Reduced Missed Calls

Every call is answered 24/7

Higher Booking Rate

Seamless quote-to-booking flow

Easy Integration & Configuration

Shop owners configure AI features through a simple admin panel

AI Program Toggle & Widget Script
AI program activation and configuration panel
Phone Bot Configuration
Phone bot configuration settings panel
One-Click Activation: Toggle AI chat and phone bot on/off instantly
Embeddable Widget: Simple script tag to add chatbot to any website
Custom Personality: Configure AI assistant name, opening message, and behavior
Call Routing: Set up redirect numbers for towing and emergencies
PROJECT 03

HR Bot

Enterprise HR  |  Microsoft Teams Bot & MCP Server

The Challenge

HR teams rely on manual workflows for leave management, policy lookups, and approval routing. Employees must navigate web portals, send emails, and wait for manual responses — creating friction and delays.

The Solution

An AI-powered Microsoft Teams bot that handles leave requests, policy Q&A, medical certificate processing, and approval workflows — all through natural language. Also serves as an MCP server accessible from Claude Desktop and other AI clients.

4AI Capability Areas
38MCP Tools
11Adaptive Card UIs
2Access Channels

AI Capabilities Overview

Four AI pillars integrated into the HR workflow

Conversational AI (GPT + Teams)
  • Natural language in Thai & English
  • Intent classification to structured JSON
  • Multi-step workflow orchestration
  • MCP tool calling by the LLM
RAG — Policy Q&A Engine
  • Company docs chunked & embedded
  • Cosine similarity vector search (top-5)
  • Grounded answers with source citations
  • Covers 14+ policy topic areas
Computer Vision (Medical Certs)
  • PDF/image upload and conversion
  • GPT Vision extracts structured data
  • Hospital, diagnosis, dates, doctor
  • Pre-fills sick leave form for review
MCP Server (38 Tools)
  • Dual access: Teams + any MCP client
  • Per-request auth token injection
  • Leave, holidays, employees, approvals
  • Claude Desktop & Cursor compatible

Architecture: Teams Bot + MCP Server

Dual-access AI assistant with enterprise SSO authentication

Microsoft Teams (SSO via Azure AD)User Interface — Natural Language + Adaptive Cards
HR Bot (Node.js / TypeScript)LLM Planner | Intent Resolver | Card Renderer | Proactive Notifications
MCP Client Layer + RAG Engine + Vision PipelinePer-request connections with user auth token injection
HR Center MCP Server (.NET 9.0)38 MCP Tools — Streamable HTTP Transport
Direct MCP Access

Claude Desktop

Cursor / VS Code

Custom AI Agents

HR Center API (.NET 7.0) + SQL ServerAzure App Service — REST API — Production Database

Leave Request Workflow — End to End

From natural language request to manager approval, all within Teams

01 Request
Employee Submits Leave
AI-generated leave request form in Microsoft Teams

Employee types natural language, AI generates form

02 Submitted
Pending Confirmation
Leave submitted with pending status and document upload options

Leave created with pending status, options to upload docs

03 Notify Manager
Approval Card Pushed to Manager
Proactive approval card pushed to manager's Teams chat

Proactive approval card pushed to manager's chat

04 Approved
One-Click Approval & Notification
One-click approval card with employee notification

One-click approval, employee notified of the result

Smart Error Handling & Rejection Flow

Contextual error messages and complete rejection notification chain

Leave Rejected — Employee Notification

Leave rejection notification sent to employee in Teams Contextual error message for invalid leave request

Intelligent Error Classification

Duplicate Leave Request

Same date detected

Leave Limit Exceeded

Quota check

Session Expired

Auth token refresh

Access Denied

Role-based control

RAG-Powered Policy Q&A & Leave Balance

Grounded answers from company documents with source citations

Policy Q&A — RAG-Powered Answers
Policy Q&A slide 1 Policy Q&A slide 2 Policy Q&A slide 3 Policy Q&A slide 4 Policy Q&A slide 5

Alternative Access: Claude Desktop via MCP

Same MCP server, different interface — showcasing MCP protocol versatility

Leave Request via Claude Desktop
Leave request submitted via Claude Desktop using the HR MCP server
Leave Quota Query via Claude
Leave quota and balance query via Claude Desktop MCP client

The same 38 MCP tools that power the Teams bot are accessible from Claude Desktop, Cursor, or any MCP-compatible AI client — one API surface, multiple interfaces.

HR Bot — Technology Stack

Bot Framework
Microsoft Teams AI SDK + BotBuilder
Language / Runtime
TypeScript 5.7 / Node.js v24
LLM & Embeddings
Azure OpenAI GPT + text-embedding-3-small
MCP Integration
ModelContextProtocol SDK — Streamable HTTP
Backend / API
.NET 9.0 MCP Server + .NET 7.0 API + SQL Server
Auth & Hosting
Azure AD SSO (OBO flow) + Azure App Service + GitHub Actions CI/CD
PROJECT 04

RuleSentry

Regulatory Privacy  |  Local-First Data Detection & Compliance

The Challenge

Organizations using AI assistants, LLM APIs, and cloud services face an invisible risk: sensitive data flows into third-party systems before anyone verifies what is being sent. Compliance must happen before sharing.

The Solution

A local-first rule evaluation engine that detects, classifies, and transforms sensitive data before it leaves the device. Combines deterministic detection rules, a 4-level jurisdictional hierarchy, policy and category dimensions and an AI-assisted rule builder.

191+Built-In Detection Rules
11+Policy Packs
RustPerformant Local-First Engine

Pre-Cloud Detection — Policy and Rule Configuration

Local data redaction before cloud providers

RULESENTRY CLIENT v1.0 — Configuration Builder
RuleSentry Policy and Rule Configuration

How It Works

The Configuration Tab lets you easily define your configuration using Policy packs to target your compliance concerns — including overrides and jurisdiction-based filtering.

Key Features

  • Draft, Activate and define your configuration
  • Custom Policy and Rule definitions
  • Override or selectively disable rules
  • Jurisdiction based filtering
  • Policy and Category dimension structuring

Multi-Dimensional Classification

A policy doesn't contain rules — it describes filters across axes

government.ssn_format
severity: critical  ·  effect: block
This single rule is classified across 3 independent dimensions
🌍

Regions

Hierarchical jurisdiction tree

GLOBAL
AMERICAS
US ← this rule
US-CA
US-TX
🏷️

Categories

9 detection categories

Financial Government Healthcare Personal Credentials Network
🔖

Profiles

Orthogonal compliance tags

HIPAA CCPA GDPR PCI-DSS

Industry: Healthcare · Financial

Compose into policies
US PII Protectionregion: US + profiles: CCPA, HIPAA
Financial Data Policycategory: Financial + profile: PCI-DSS
APAC Data Privacyregion: APAC + profile: PDPA-SG, PDPA-TH

Define once, classify across dimensions, filter freely — 191+ rules · 11 policies · 37 jurisdictions · 18 profiles · 9 categories

Why Multi-Dimensional Classification?

Define once, classify across dimensions, filter freely

Regions — "Where does this apply?"

Hierarchical jurisdiction tree: GLOBAL → AMERICAS → US → US-CA. Bidirectional — a US policy auto-includes state rules, a state policy inherits federal rules. No manual wiring.

Categories — "What data is detected?"

9 detection categories: financial, healthcare, government IDs, credentials, network, personal, contact, digital identity, NER. Slice your rule library by data domain.

Profiles — "Why does this rule exist?"

Loose compliance tags across 3 axes: 13 frameworks (HIPAA, GDPR, CCPA, PCI-DSS...), 4 industry contexts, 1 environment. Compose freely.

Automatic Policy Assembly

Policies describe filters, not rule lists. New rules matching those dimensions are included automatically — no manual maintenance.

Zero Duplication

191 rules power 11 policies without a single duplicated definition. APAC and US policies share global rules and diverge on jurisdiction-specific ones.

Instant Expansion

"What fires if we expand to the EU?" — switch the region filter. Adding a jurisdiction means tagging existing rules, not building a new policy from scratch.

191 rules · 11 policies · 37 jurisdictions · 18 profiles · 9 categories · 6 evaluation types · 16 validators

Every match is deterministic and fully auditable — no LLM inference, no probabilistic scoring. Each evaluation produces identical output for identical input.

The Problem with Traditional Compliance Tools

Why flat rule lists and monolithic policies break down at scale

Traditional Approach

Flat rule lists

You manually pick which rules apply to each deployment. When regulations change or you expand to a new market, you duplicate rules and hope nothing drifts.

Monolithic policies

You get HIPAA or GDPR — not both, tailored to your geography. You're maintaining a custom bundle that no one else can reuse.

No jurisdiction awareness

Country-level tagging misses multi-level regulation. US federal rules should automatically apply alongside California-specific ones.

RuleSentry's Dimensional Model

Dimensional filters replace lists

Policies describe what they want — region, category, severity, profile — and the engine resolves the right rules. Add a new rule, tag it, and every matching policy picks it up.

Composable, not monolithic

A bank composes financial-industry + pci-dss + glba + sox. A hospital composes healthcare-industry + hipaa + gdpr. Same rule catalog, zero duplication.

4-level jurisdiction hierarchy

GLOBAL → region → country → state. Bidirectional. Expanding to the EU is a filter change, not a rebuild.

Deterministic Tracing, Deduplication and Execution Flow

Full audit trail with smart policy and rule resolution based on priority and overrides

Last Execution — Execution Order
RuleSentry Deterministic Tracing and Execution Flow

How It Works

Compliance-audit support with smart Policy and Rule resolution based on Priority and Overrides.

Key Features

  • Visualize and track your compliance flows
  • Clone and modify for fine-tuning
  • Pinned versioning and flows for auditing

AI Assisted, Schema-Compliant Definitions

Describe your compliance needs in natural language — AI generates schema-valid rules instantly

AI BUILDER — Policy & Rule Generator
AI Builder — describe compliance needs in natural language to generate rules

How It Works

  1. 1. Describe your compliance need in plain language
  2. 2. AI researches applicable regulations and standards
  3. 3. Generates schema-compliant Rules, Policies, Profiles, and Categories
  4. 4. Validates output against RuleSentry JSON Schema 2020-12
  5. 5. Import and test locally — no data shared externally

Business Impact

  • Skip manual JSON schema authoring entirely
  • Schema compliance guaranteed by built-in validation
  • Aligned to your jurisdiction and industry context
  • Ready to test immediately on your local device
AI BUILDER — Schema-Compliant Output Preview
AI Builder — schema-compliant rule and policy output ready to import

Using AI to Build Definitions

Use AI to build schema-compliant Policies, Rules, Profiles and Categories for specific jurisdictions.

Key Benefits

  • Eliminates manual coding of your business’ needs
  • Ensures compliance with the RuleSentry schema
  • Immediately test locally – with no shared data
  • Reference Profiles and Categories for alignment

AI provides research and the right structure for your needs

AI Builder — structured research output aligned to RuleSentry schema dimensions

As Broad or Granular as Your Needs with Selective Validation

Many patterns may match but fail validation.

EVALUATE — Singapore-Only · ACTIVE
RuleSentry Selective Validation — NRIC evaluate screen

Context

Apply as broadly or narrowly as your needs.

Singapore defines precisely two rules — only one (NRIC) is caught here, twice.

Key Features

  • Configuration-based scope to reduce false positives
  • Post-match validators for many known patterns (see one rejected for the failed NRIC checksum)

RuleSentry — Technology Stack

Evaluation Engine
Rust (locally) and WASM for the web
Desktop App
Tauri 2 + React 19 + SQLite + TanStack Query
Browser Extension
Chrome MV3 + WASM (wasm-pack)
NER Inference
ONNX Runtime (ort crate) — distilbert-NER, on-device
AI Builder
Anthropic Claude API / Ollama (local) — JSON Schema 2020-12 validated
Schema & Data
200+ rules · 11 policies · 30+ jurisdictions · 18 Profiles · 9 Categories

Why Partner with Mycos Technologies for AI Integration

Proven Production Experience

Our AI solutions are not prototypes — they are deployed in production systems serving real users across multiple industries.

End-to-End Implementation

From architecture design to deployment, we handle the full lifecycle: data pipelines, AI models, security, and user interfaces.

Enterprise-Grade Security

Built-in PII protection, access control, multi-tenant data isolation, and compliance auditing from day one.

Multi-Channel AI

We deliver AI across channels your customers use: web chatbots, voice phone bots, document processing, and intelligent search.

Cloud-Native Architecture

Scalable, cost-efficient solutions built on Azure cloud services with serverless orchestration and on-demand provisioning.

Seamless Integration

We integrate AI into your existing systems without disruption. No migration needed — we meet your data where it lives.

How We Can Help You

AI integration services tailored to your business needs

1

AI-Powered Search & Knowledge Base

Transform your existing documents and data into an intelligent, searchable knowledge base with conversational AI.

2

Conversational AI (Chat & Voice)

Deploy AI assistants on your website and phone lines to handle customer inquiries, generate quotes, and book appointments 24/7.

3

Document Processing & Compliance

Automate document intake with OCR, PII detection, redaction, and compliance validation built into your workflow.

4

Workflow Automation with AI

Let AI analyze incoming requests, extract action items, generate summaries, and create structured task lists for your team.

5

AI Strategy & Consulting

We assess your existing products and processes to identify high-impact opportunities for AI integration and transformation.

6

Custom AI Solutions

From RAG pipelines to real-time voice AI, we build custom AI solutions that integrate with your existing technology stack.

Thank You

Let's Transform Your Products with AI

Contact us to discuss how AI can enhance your enterprise solutions

Get in Touch