Appsin.Ai company overview · 2026

[00] applications in artificial intelligence · coimbatore, india

AI that does real work for your business.

We build generative AI assistants, AI agents, chatbots, machine learning models and data platforms on your own data, and connect them to the systems your team already uses.

clients across the Middle East UAEKuwaitQatarSaudi ArabiaBahrainOmanEgyptJordan

projects also in India and USA

results.log

cs 0185%less manual work

cs 0240%lower operational costs

cs 033×increase in customer engagement

cs 0435%higher conversion rate

cs 0570%lower content production costs

5 published case studies · appsin.ai/case-studies/

[00] contents

What this deck covers. Fourteen sections in nineteen slides, then an appendix.

01about appsinai03 02benefits of using ai04 03what we do05 04what we build from06 05results07 06what we built11 07ai in mobile apps12 08ai in automotive13 09how we work14

10how we build and ship15 11security and your data16 12where we work17 13what clients say18 14next step19

appendix · for reference

A1ai system maintenance20 A2technologies21 A3why appsinai22

the number on the right is the slide each section starts on · click a row to go there · the page number in any footer comes back here

01

[01] about appsinai

Who we are. What we are here to do.

AppsInAi is an AI development company in Coimbatore, India. We build machine learning models, generative AI and LLM applications, AI chatbots and agents, computer vision, recommendation engines and process automation, together with the data work underneath them: business intelligence, analytics, data annotation and data collection.

Our clients are across the Middle East, and we also take on projects in India and the USA. Most projects start with one clear problem, such as a manual process, a busy support queue or data nobody has time to analyse, and a pilot that proves the approach on real data before it is rolled out more widely.

We value honest scoping, measurable results and systems that keep working after launch. If AI is not the right answer to a problem, or your data is not ready yet, we tell you before you spend money on it.

mission

To help businesses put AI to practical use: systems that save time, reduce errors and support better decisions, built on their own data and processes.

vision

To be the AI partner growing businesses trust to take an idea from pilot to production, in India, the Middle East and beyond.

based in: Coimbatore, India
registered: 2018
serves: India · UAE · Kuwait · Qatar · Saudi Arabia · Bahrain · Oman · Egypt · Jordan · USA
builds: 11 AI services · 4 data services
delivery: Cloud, on-premises or hybrid
hours: Mon–Fri, 09:30–17:00 IST

02

[02] benefits of using ai

What AI does for you. Figures from our case studies.

01

Less manual workRoutine work, data entry and document handling done by the system; people review the exceptions.

85% less manual work

02

Lower operating costsFewer hours on routine work and routine support.

40% lower operational costs

03

Answers around the clockAssistants on your website, WhatsApp and apps reply in seconds, in Arabic and English.

Seconds to respond, down from 12–24 hours

04

Fewer errorsExtraction checked against evaluation sets, with human review where a mistake would be costly.

90% fewer data errors

05

Decisions on fresh dataReal-time dashboards, forecasts and alerts from the data you already have.

3× faster data processing

06

More revenue from the same trafficRecommendations personalised from browsing, search and purchase history.

35% higher conversion rate

07

Customers who come backRelevant products and content for each customer, every visit.

22% → 41% customer retention

08

Content at scaleDrafting, ideation and SEO in the workflow; editors review every draft.

5× faster content production

03

[03] what we do

Fifteen services. Eleven AI, four data.

ai services · 11

01

AI & ML DevelopmentCustom AI built on your own data

02

Generative AI DevelopmentContent, search and copilots on GenAI

03

AI Agent DevelopmentAgents that plan and act in your tools

04

AI Chatbot DevelopmentWebsite and WhatsApp assistants

05

ChatGPT & OpenAI IntegrationGPT models inside your product

06

LLM DevelopmentPrivate LLMs and RAG on your documents

07

Model Training & Fine-TuningModels tuned to your domain

08

Machine Learning DevelopmentForecasting, scoring and prediction

09

Computer VisionObject recognition and visual inspection

10

Recommendation EnginesPersonalised products and content

11

Robotic Process AutomationBots for repetitive back-office work

data services · 4

12

Business IntelligenceDashboards your managers will use

13

Data Analytics & InsightsAnswers from the data you already have

14

Data Annotation & LabellingTraining data for AI models

15

Web Scraping & Data CollectionClean, structured data from the web

the data work underneath every AI system: collected, labelled, analysed and reported

04

[04] what we build from

Six kinds of input. One system that reads them all.

01 · documents

Documents & invoices

Invoices, purchase orders, forms, IDs and contracts, read field by field, even from a phone photo or a scan.

→ ERP · accounting · document store

02 · photos & video

Photos & video

Products, parts, shelves, vehicles and damage, detected and boxed in photos or frames from a video feed.

→ inspection log · claims · stock

03 · speech & calls

Speech & calls

Voice notes and recorded calls transcribed, with the caller, the reason and the follow-up pulled out.

→ helpdesk · CRM · call log

04 · chat & email

Chat & email

WhatsApp messages, website chats and email threads, sorted by intent with names, dates and quantities extracted.

→ CRM · order desk · support queue

05 · web data

Web data

Prices, listings, tenders and reviews collected from public websites and cleaned into a consistent table.

→ database · BI dashboard

06 · structured data

Structured data

Spreadsheets, ERP exports and sensor logs checked for outliers, gaps and trends, then used to forecast and score.

→ reports · forecasts · alerts

05

[05] results

Five case studies. Figures as published on appsin.ai.

cs 01 · delivered in 12 days

85%

less manual work

for finance, e-commerce and enterprise operations teams

3× faster data processing90% fewer data errors40% lower operational costs

ai & ml development · robotic process automation

cs 02

40%

lower operational costs

for an enterprise with complex operational workflows

60% improvement in process efficiency50% less manual workload

robotic process automation · ai & ml development

cs 03

3×

increase in customer engagement

for a SaaS company

50% lower support costs70% of repetitive queries automatedSeconds to respond, down from 12–24 hours

ai chatbot development · machine learning development

cs 04 · results within six months

35%

higher conversion rate

for a fashion and lifestyle e-commerce brand

$68 → $92 average order value22% → 41% customer retention71% → 49% cart abandonment

recommendation engines · machine learning development

cs 05

70%

lower content production costs

for a B2B software company

5× faster content production20+ blogs a month, up from 4

generative ai development · llm development

figures are the before-and-after measures reported in each case study · full write-ups at appsin.ai/case-studies/

[05] results · cs 01 · finance, e-commerce and enterprise operations

Manual data processing bottlenecks solved with AI/ML in 12 days

client

Rapidly scaling enterprise, finance, e-commerce and enterprise operations.

challenge

Over 80% of processes were manual, reports arrived 24 to 48 hours late and human errors and duplicate records undermined decisions.

solution

Machine learning and NLP for data extraction and classification, workflow automation, real-time dashboards and integration with the existing ERP and CRM.

services

AI & ML DevelopmentRobotic Process AutomationBusiness IntelligenceData Analytics & Insights

built with

PythonTensorFlowscikit-learnNLPAWSAzureREST APIsPower BI

85%less manual work

before → after · delivered in 12 days

3× faster data processing

1×

3×

90% fewer data errors

before

−90%

40% lower operational costs

before

−40%

[05] results · cs 03 · saas

AI chatbot increased customer engagement by 3× and reduced support costs by 50%

client

Mid-sized company, saas.

challenge

Support replies took 12 to 24 hours, repetitive questions tied up agents, support costs were high and customer engagement was low.

solution

An NLP chatbot on the website, WhatsApp and mobile app, connected to the CRM and helpdesk, answering routine questions around the clock.

services

AI Chatbot DevelopmentMachine Learning Development

built with

NLPMachine learningCRM integrationHelpdesk integrationWhatsAppAnalytics dashboard

3×increase in customer engagement

before → after

50% lower support costs

before

−50%

70% of repetitive queries automated

70%

Seconds to respond, down from 12–24 hours

12–24 hours

seconds

[05] results · cs 04 · fashion and lifestyle e-commerce

AI recommendation engine increased e-commerce conversion rate by 35%

client

Mid-sized company, multiple regions, fashion and lifestyle e-commerce.

challenge

High traffic but low conversion: generic recommendations, poor product discovery, high cart abandonment and few repeat purchases.

solution

A custom machine learning recommendation engine that personalises products in real time from browsing, search, wishlist and purchase history.

services

Recommendation EnginesMachine Learning DevelopmentData Analytics & Insights

built with

PythonTensorFlowMachine learningRecommendation algorithmsBig data analyticsCloud infrastructureAPI integration

35%higher conversion rate

before → after · results within six months

$68 → $92 average order value

$68

$92

22% → 41% customer retention

22%

41%

71% → 49% cart abandonment

71%

49%

06

[06] what we built

Three AI products we built. Detail in the AI projects deck.

01 · sales and marketing crm

PulseAi CRM

A self-hosted sales and marketing CRM with a Python AI layer for drafting emails, scoring and enriching leads, and summarising deals.

AI email drafting from intent and toneAI reply drafting from a logged thread

02 · invoice processing agent

Invoice Agent

Invoice automation that turns incoming emails into validated business transactions through OCR, AI extraction and workflow integration.

Email inbox monitoring: Gmail, Outlook, IMAPInvoice detection and extraction from attachments

03 · url-shortening saas, live

HTTTP.IN

Shorten URLs, generate QR codes, create bio pages and track every click with real-time analytics, with an AI layer on top.

AI phishing and malware detection on every new linkSmart alias suggestions from the destination page

View all AI projects →View all AI projects →the screens, ai features, architecture and stack of each product

07

[07] ai in mobile apps

AI inside the apps you already have. Added by a team that has built ten of them.

01 · ai chatbot

An assistant inside the app

A chatbot in Arabic and English that answers from your documents and books, orders or reports without leaving the app.

02 · computer vision

The camera as input

Parts, damage, products, IDs and invoices recognised and read from a phone photo, with the fields filled in for the user.

03 · recommendations

A home screen for each user

Products, offers and content ranked for each customer from what they browse, search for and buy.

04 · speech & calls

Voice in and out

Voice notes, calls and commands transcribed and understood, with the reason and the follow-up pulled out.

05 · machine learning

Predictions that become notifications

Forecasts and scores drive push notifications: a service that is due, a lead going cold, a reading out of range.

06 · agents & rpa

The back office behind the app

The forms, bookings and requests an app collects, processed automatically: approvals, invoices, reports and follow-ups.

apps we built · 10

Car Sales App · Taxi User App · Mercedes-Benz App

We built these apps for car dealers, a telecom, offices and taxi operators, in Arabic and English. Today we add AI to apps like these.

View all Mobile App Projects →View all Mobile App Projects →all 10 apps, with their screens and features

08

[08] ai in automotive

AI in automotive. Built into AutoTech360, our automotive software brand, or custom on your data.

already inside the products

01

Document reading in the DMSAn assistant in AutoTech360 DMS reads documents into forms, so staff check the details instead of typing them in.

02

Reply drafts and summaries in the CRMAutoTech360 CRM drafts WhatsApp replies in the customer's language and adds AI summaries to its reports, so consultants answer faster and managers read less.

03

Receipt reading and a fleet assistantAutoTech360 Fleet reads fuel receipts and photos into entries for someone to confirm, raises recommendations unasked and answers questions about your own fleet.

custom, on your own data

04

Demand and stock forecastingCustom models use your sales history, regional demand and seasons to forecast which models and parts will sell, and flag slow-moving stock early.

05

Lead scoringScore enquiries by source, vehicle interest and response, so sales teams call the buyers most likely to act first.

06

Chat assistantsAssistants on your website or WhatsApp answer common questions about stock, prices and bookings, then hand the conversation to a person with the details already captured.

custom, continued

07

Photo-based inspectionComputer vision flags visible damage in vehicle photos to make check-in and return inspections faster and more consistent. Models are tested on your own photos before go-live.

08

Predictive maintenance from telematicsFor fleets with telematics data, models trained on fault codes and repair history flag vehicles likely to need attention, on top of the scheduled service reminders in AutoTech360 Fleet.

7 products · 4 ready-made apps

DMSCRMPrice StandRentalFleetCar WashRoadsideTaxi Booking AppCar Service AppSpare Parts Sales AppNew & Certified Car Sales App

View all Automotive Projects →View all Automotive Projects →every autotech360 product and ready-made app, with screens

09

[09] how we work

From first call to launch, and beyond. Proof before the full build.

$ appsinai run --project yours # NDA before any data · fixed quote after discovery

01 $ discover

Discover

We learn how the work gets done today, look at the data you have and pick the problems where AI can save the most time or money.

→ you get Use-case shortlistData reviewFixed quote

done

02 $ prove-it

Prove it

A proof of concept runs on your own data, measured against a baseline we agree up front, so you see real results before committing to a full build.

→ you get Working pilotEvaluation resultsGo / no-go

done

gate · go / no-go on the results before “Build & integrate”

03 $ build-integrate

Build & integrate

We build the production system, connect it to your CRM, ERP, helpdesk or apps, and add guardrails, logging and human review where they matter.

→ you get Production systemIntegrationsTested release

done

04 $ launch-train

Launch & train

Your team learns the system on real cases, then we roll it out in stages and stay close through the first weeks.

→ you get Team trainingStaged rollout

done

05 $ monitor-improve

Monitor & improve

We track accuracy, cost and usage after launch, retrain or update models as your data changes and add new use cases as you grow.

→ you get MonitoringRetrainingSupport plan

ongoing

proof of concept 3–6 weeks · small build 6–12 weeks · medium build 3–6 months · enterprise build 6 months +, in phases

10

[10] how we build and ship

How we build and ship. The same discipline on every project, from a bug fix to a new model.

how a change ships · every time

01

ChangeA new idea, a bug, a wrong answer found in review, or a better model.

02

UpdateThe code, the prompts or the model change on a branch, and another developer reviews the pull request.

03

TestUnit, integration and end-to-end tests run, and the evaluation set runs against the agreed baseline, so accuracy, cost and speed are checked before anything ships.

04

ReleaseGitHub Actions or Jenkins deploys to your cloud account, the rollout is staged, and monitoring picks up the next change.

mlops & deployment

MLflowDockerKubernetesFastAPIJenkinsGitHub ActionsCeleryRedis

a model swap is a configuration change and a re-run of the evaluation set, not a rebuild

on every project

01

Jira-based project controlTask estimation and progress tracking you can follow throughout the project.

02

Coding standardsSOLID and clean-code principles.

03

Code reviewsCarried out by other developers before anything is merged.

04

Version controlGit with pull-request flows.

05

Thorough testingUnit, integration and end-to-end tests.

06

Automated testsFor the most important parts of the business logic, and an evaluation set for every model.

07

DevOps supportA modern, scalable architecture and infrastructure.

11

[11] security and your data

Security and your data. Where it lives, who can see it, what the model gets.

01

An NDA before any data

We sign an NDA before you share any data. Named engineers, role-based permissions and logged access, revoked at the end of the engagement.

02

In your account, in your country

Data stays in your own cloud account where possible. Models, vector databases and applications can run in a UAE or Saudi cloud region, or on your own servers.

03

Models that do not train on your data

Business API tiers that do not train on your data, Azure OpenAI in your chosen region, or open-weight models hosted privately when data must not leave your environment.

04

Only what the task needs

Personal and sensitive fields are removed or masked before a prompt is built, so the model only sees what the task needs, and they are masked in logs too.

05

Encrypted, kept only as long as you say

Data is encrypted in transit and at rest, kept no longer than the retention period you set, and returned or deleted at the end of the project if you prefer.

06

Your data protection law

We review your data classification and your obligations under local law with you before the build, such as the PDPL in Saudi Arabia and Oman.

where it can run

Your own cloud account: AWS, Azure or Google CloudA cloud region in the UAE or Saudi ArabiaYour own serversOpen-weight models such as Llama and Mistral

12

[12] where we work

Clients across the Middle East. Working from Coimbatore, in your hours.

Our clients are across the Middle East, and we also take on projects in India and the USA. Projects run remotely from our office in Coimbatore, with calls in your working day.

clients in

UAEKuwaitQatarSaudi ArabiaBahrainOmanEgyptJordan

projects also in

IndiaUSA

assistants that read and reply in arabic and english

website in arabic · appsin.ai/ar/

office hours, in local time

Coimbatore, India: Mon–Fri, 09:30–17:00 IST
UAE · Oman: Mon–Fri, 08:00–15:30
Saudi Arabia · Kuwait · Qatar · Bahrain · Jordan: Mon–Fri, 07:00–14:30
Egypt: Mon–Fri, 06:00–13:30 (07:00–14:30 in summer)

Monday to Thursday overlap with the Gulf working week, with Sunday calls by arrangement. Invoicing in USD, or another currency by agreement.

13

[13] what clients say

In their own words. From clients in Kuwait and Qatar.

“Appsin.Ai has delivered on time and within budget no matter the modifications within the scope and scale of the project. The team is hard-working, knowledgeable and expert. The overall experience has been overwhelmingly superb and they always help the customer meet goals. They provide great value for the money.”

Mandy Scarfe · Kuwait

“I had an amazing experience with the team. They finished the work before the deadline and sent more than one demo for me to test and give them feedback. Professional programmers and wonderful work. I would definitely recommend them.”

Anas Karaki · Qatar

4.7 / 5google reviews ↗5.0 / 5clutch reviews ↗4.6 / 5justdial reviews ↗

apps we built for

Al Mulla & Behbehani Motors Company, Kuwait · Zajil International Telecom, Kuwait · a Mercedes-Benz dealer

[14] next step

Let’s talk about your project.

Book a free 30-minute call. You get a range on the first call and a fixed quote after discovery, and if AI is not the right answer to your problem we say so before you spend money on it.

Book a free call calendly.com/appsin-ai/30min

phone & whatsapp: +91-9025555584
email: sales@appsin.ai
web: appsin.ai

office · mon–fri, 09:30–17:00 ist

AppsInAi Private Limited
Housing Unit, Sf No. 393, CMS School Backside, 3rd Street, Nehru Nagar, Ganapathy,
Coimbatore 641006, Tamil Nadu, India

cin U72200TZ2018PTC030580

A1

[A1] ai system maintenance

After launch. An AI system needs looking after.

AI systems need looking after. We monitor accuracy, usage and cost, review the cases where the model got things wrong, update prompts and retrain models as your data changes, and move to newer models when they are better value.

01

Keep accuracy up

Your data changes after launch: new products, new customers, new ways of asking. Models are retrained or updated as it changes, so answers stay right.

02

Fix what the model gets wrong

The cases where the model got things wrong are reviewed, and prompts, retrieval and training data are updated.

03

Keep costs in check

Usage and cost are monitored, with usage limits and alerts, so there are no surprises on the provider's bill.

04

Move to better models

New models arrive every few months. Switching is a configuration change and a re-run of the evaluation set, not a rebuild.

05

Keep the guardrails

Limits on what the model can say or do, and human review where a mistake would be costly, stay in place after launch.

06

Add new use cases

Monitoring shows where the next saving is, and new use cases are added as you grow.

monitoring · retraining · support plan · support, maintenance and improvements are covered by an agreed support plan.

A2

[A2] technologies

Models, frameworks and clouds. Chosen per task.

01

Language models

OpenAI GPT models · Anthropic Claude · Google Gemini · Meta Llama · Mistral · Azure OpenAI (Microsoft Foundry) · Amazon Bedrock · Ollama

02

Machine learning frameworks

Python · PyTorch · TensorFlow · Keras · scikit-learn · Hugging Face · XGBoost · Sentence-Transformers

03

LLM application frameworks

LangChain · LangGraph · LlamaIndex · CrewAI · Model Context Protocol (MCP) · LangSmith · Pydantic

04

Vector databases

Pinecone · pgvector (PostgreSQL) · Weaviate · Chroma

05

Cloud platforms

AWS · Amazon SageMaker · Microsoft Azure · Azure Machine Learning · Google Cloud · Vertex AI · On-premises servers

06

Data & BI

Power BI · Tableau · Looker · Looker Studio · Metabase · Apache Superset · Snowflake · BigQuery · Amazon Redshift · Azure Synapse Analytics · Databricks · PostgreSQL · MySQL · MongoDB

07

Automation & RPA

UiPath · Power Automate · n8n · Automation Anywhere · Blue Prism

08

MLOps & deployment

MLflow · Docker · Kubernetes · FastAPI · Jenkins · GitHub Actions · Celery · Redis

09

Computer vision

OpenCV · YOLO · Tesseract OCR · MediaPipe · AWS Textract

10

Data collection & labelling

Scrapy · Playwright · Beautiful Soup · Label Studio · CVAT

chosen by testing accuracy, cost, speed and data residency on your own examples · deployed into your own cloud account and region, or on your own servers for open-weight models · prompts, retrieval and business logic stay separate from the model, so it can be swapped without a rebuild

A3

[A3] why appsinai

What you can count on. Six commitments, on every project.

01

AI engineering expertise

Our team works across machine learning, large language models, computer vision and data engineering, so one partner can scope, build and run the whole system, not just the model in the middle of it.

02

Practical, production AI

We build for daily use, not demos: evaluation sets to measure accuracy, guardrails on what the model can say or do, and human review wherever a mistake would be costly.

03

Custom to your data

Models are trained, fine-tuned or grounded with retrieval (RAG) on your own documents, records and processes, so answers reflect your business rather than the public internet.

04

Works with your systems

We connect AI to the CRM, ERP, helpdesk, databases and messaging channels you already use through their APIs, so your teams do not have to change how they work.

05

Fixed-scope delivery

Projects are quoted at a one-time cost for an agreed scope, or run by a dedicated team. You get a plan, milestones and a working pilot before full rollout.

06

Support and monitoring

After launch we monitor accuracy, cost and usage, update or retrain models as your data changes, and fix issues under an agreed support plan.