Enterprise AI Solutions

Enterprise AI Solutions Built for Modern Businesses

We develop production-ready AI systems that automate document processing, conversational intelligence, speech technologies, and multilingual communication.

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AI Document Intelligence Platform

DocParser

An enterprise document processing platform capable of automatically extracting structured information from receipts, invoices, and bank statements — turning unstructured paperwork into clean, validated, structured data at scale.

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TechDexAI — AI-powered financial document processing platform
OCR
Document Classification
Key-Value Extraction
Table Extraction
Line Item Detection
JSON Output
REST APIs
Multi-language Support
Processing Flow

Upload

Classification

OCR

Extraction

Validation

Structured JSON

Enterprise Conversational AI Platform

ConversaAI

An intelligent AI platform for natural language communication and multilingual interactions — combining conversational agents, translation, and speech technologies into a single enterprise-ready pipeline.

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Conversa AI — enterprise conversational AI voice platform
AI Chat
Translation
Speech-to-Text
Text-to-Speech
Voice Conversations
AI Agents
Knowledge Base Search
API Integration
Workflow

User

AI Agent

LLM

Translation

Speech Processing

Response

AI Case Studies

Real problems, production outcomes

Click a case study to see the full breakdown of problem, architecture, and impact.

Problem Statement

Manual back-office teams spent over 30 hours a week keying data from receipts, invoices, and bank statements.

Business Challenges

High error rates, slow month-end close, and dozens of inconsistent document formats and languages.

Solution

An end-to-end OCR, classification, and extraction pipeline with a human-in-the-loop validation UI.

Architecture

Client → API Gateway → Upload Service → RabbitMQ → AI Worker → OCR Engine → Classifier → Extraction Models → Database → API Response.

Technologies Used

FastAPI, PyTorch, PostgreSQL, Docker, RabbitMQ.

AI Models

PaddleOCR for text detection/recognition, a fine-tuned document classifier, and a layout-aware key-value extraction model.

Results

94% straight-through processing rate with no manual intervention required.

Performance

Average 2.3 seconds per document, roughly 40 documents processed per minute per worker.

Business Impact

70% reduction in manual data-entry hours and a significantly faster month-end close.

Problem Statement

A small support team could not serve customers across 12 languages without significant hiring.

Business Challenges

Language coverage, response latency, and accurate escalation routing to human agents.

Solution

A conversational agent orchestrating an LLM, translation, and speech pipeline behind one API.

Architecture

Client → API Gateway → Conversation Agent → LLM → Translation Engine → Speech Engine → Response.

Technologies Used

LangChain, vLLM, Node.js.

AI Models

An instruction-tuned LLM, NLLB for translation, Whisper for speech-to-text, XTTS for text-to-speech.

Results

12 languages supported at launch, with seamless escalation to human agents when needed.

Performance

Sub-second median response latency across all supported languages.

Business Impact

35% reduction in first-response time and 24/7 coverage without additional headcount.

Technology Stack

Built on a modern, proven stack

Frontend

React.js
TypeScript
Tailwind CSS

Backend

FastAPI
Node.js
Python

AI

Hugging Face
LangChain
PyTorch
Transformers
OpenAI
vLLM

OCR

PaddleOCR
DocTR
LightOnOCR

Database

PostgreSQL
Redis

Infrastructure

Docker
RabbitMQ
AWS
Nginx
GitHub Actions

Architecture

How each platform is engineered

DocParser Architecture

Client

API Gateway

Upload Service

RabbitMQ

AI Worker

OCR Engine

Document Classifier

Extraction Models

Database

API Response

ConversaAI Architecture

Client

API Gateway

Conversation Agent

LLM

Translation Engine

Speech Engine

Response

AI Agents

The agents behind the platforms

Document Classification Agent
PurposeIdentify document type before routing.
InputUploaded document image/PDF.
OutputDocument type label + confidence score.
ResponsibilitiesPre-processing, type detection, routing.
AI ModelsFine-tuned vision classifier.
OCR Agent
PurposeConvert document images into raw text.
InputClassified document image.
OutputRaw text with bounding boxes.
ResponsibilitiesText detection and recognition.
AI ModelsPaddleOCR, DocTR.
Extraction Agent
PurposePull structured fields from raw text.
InputOCR text + layout metadata.
OutputKey-value pairs and line items.
ResponsibilitiesField mapping, table parsing.
AI ModelsLayout-aware extraction model.
Validation Agent
PurposeVerify extracted data before delivery.
InputExtracted structured data.
OutputValidated JSON, flagged exceptions.
ResponsibilitiesBusiness-rule checks, confidence review.
AI ModelsRule engine + anomaly scoring.
Chat Agent
PurposeHold natural-language conversations.
InputUser message + conversation history.
OutputGenerated response.
ResponsibilitiesIntent handling, context management.
AI ModelsGPT, Qwen, Llama, Phi.
Translation Agent
PurposeTranslate between languages in real time.
InputSource text + target language.
OutputTranslated text.
ResponsibilitiesLanguage detection, translation.
AI ModelsNLLB, MarianMT.
Speech Agent
PurposeConvert between speech and text.
InputAudio stream or text.
OutputTranscript or synthesized audio.
ResponsibilitiesSpeech-to-text, text-to-speech.
AI ModelsWhisper, XTTS.
Workflow Orchestrator
PurposeCoordinate agents across a pipeline.
InputIncoming request/event.
OutputCompleted multi-step workflow result.
ResponsibilitiesTask sequencing, retries, monitoring.
AI ModelsRule-based + LLM-assisted routing.

AI Models

The models powering every agent

OCR

PaddleOCR
PurposeGeneral-purpose text detection and recognition.
Model TypeOCR (detection + recognition).
StrengthsFast, multilingual, lightweight.
Use CasesReceipts, invoices, scanned forms.
LightOnOCR
PurposeHigh-throughput document OCR.
Model TypeOCR (transformer-based).
StrengthsHigh accuracy on dense layouts.
Use CasesBank statements, complex tables.
DocTR
PurposeDocument text recognition pipeline.
Model TypeOCR (deep learning).
StrengthsOpen-source, easily fine-tuned.
Use CasesCustom document types.

LLM

GPT
PurposeGeneral-purpose reasoning and chat.
Model TypeLarge language model.
StrengthsBroad knowledge, strong reasoning.
Use CasesComplex conversations, agents.
Qwen
PurposeMultilingual instruction following.
Model TypeLarge language model.
StrengthsStrong multilingual performance.
Use CasesCross-language support chat.
Llama
PurposeSelf-hostable general reasoning.
Model TypeLarge language model.
StrengthsOpen weights, cost efficient.
Use CasesOn-prem/private deployments.
Phi
PurposeLightweight, fast inference reasoning.
Model TypeSmall language model.
StrengthsLow latency, low cost.
Use CasesHigh-volume simple tasks.

Speech

Whisper
PurposeSpeech-to-text transcription.
Model TypeSpeech recognition model.
StrengthsRobust across accents/languages.
Use CasesVoice conversations, transcripts.
XTTS
PurposeNatural text-to-speech synthesis.
Model TypeSpeech synthesis model.
StrengthsNatural, multilingual voices.
Use CasesVoice responses, IVR.

Translation

NLLB
PurposeTranslation across 200+ languages.
Model TypeNeural machine translation.
StrengthsBroad language coverage, including low-resource.
Use CasesGlobal multilingual support.
MarianMT
PurposeFast, focused-pair translation.
Model TypeNeural machine translation.
StrengthsLightweight, low latency.
Use CasesReal-time chat translation.

Metrics Dashboard

Performance at a glance

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Documents Processed

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AI Accuracy

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Processing Speed

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Languages Supported

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API Requests

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AI Models Running

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Active Clients

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