70%lower content production costs
- 5×faster content production
- 20+blogs a month, up from 4
inputRising content costs, slow production cycles and a heavy reliance on freelancers and agencies made a steady publishing schedule hard to keep.
[00] agents that do the work, with you in control
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.
- Pilot on your data first
- Fixed-scope quotes
- NDA from day one
client ratings
Independent review platforms
[01] tools the agent can use
Fifteen services. Five toolsets an AI system can call on your behalf.
Read it left to right: an AI system reads your data, sees, predicts, reasons and writes, then acts in your systems. Each node is one of the 15 services we build. Open one to see how it works.
toolset.01 · read
Data & BI
03 toolstoolset.02 · see
Computer vision
02 toolstoolset.03 · predict
Machine learning
03 toolstoolset.04 · reason & write
Generative AI & LLMs
04 tools[02] results
Results, annotated. Every figure is taken from a published case study.
Three recent projects: the problem, what we built, and what changed once it went live.
All case studies35%higher conversion rate
- $68 → $92average order value
- 22% → 41%customer retention
- 71% → 49%cart abandonment
inputHigh traffic but low conversion: generic recommendations, poor product discovery, high cart abandonment and few repeat purchases.
AI Recommendation Engine Increased E-commerce Conversion Rate by 35%
3×increase in customer engagement
- 50%lower support costs
- 70%of repetitive queries automated
- Secondsto respond, down from 12–24 hours
inputSupport replies took 12 to 24 hours, repetitive questions tied up agents, support costs were high and customer engagement was low.
AI Chatbot Increased Customer Engagement by 3× and Reduced Support Costs by 50%
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.
I had an amazing experience with him. He finished the work before the deadline and sent more than one demo for me to test and give him feedback. Professional programmer and wonderful work. I would definitely recommend him.
[04] inputs
What your AI can read and see. Six kinds of input, one structured output.
Most of the work that reaches a business arrives as something messy: a photo, a scan, a voice note, a chat. We build the vision, speech and language models that read it, and the plumbing that puts the result where it belongs.
AI & ML developmentread.01 · documents
Documents & invoices
Invoices, purchase orders, forms, IDs and contracts, read field by field, even from a phone photo or a scan.
→ goes toERP · accounting · document store
read.02 · photos & video
Photos & video
Products, parts, shelves, vehicles and damage, detected and boxed in photos or frames from a video feed.
→ goes toinspection log · claims · stock
read.03 · speech & calls
Speech & calls
Voice notes and recorded calls transcribed, with the caller, the reason and the follow-up pulled out.
→ goes tohelpdesk · CRM · call log
read.04 · chat & email
Chat & email
WhatsApp messages, website chats and email threads, sorted by intent with names, dates and quantities extracted.
→ goes toCRM · order desk · support queue
read.05 · web data
Web data
Prices, listings, tenders and reviews collected from public websites and cleaned into a consistent table.
→ goes todatabase · BI dashboard
read.06 · structured data
Structured data
Spreadsheets, ERP exports and sensor logs checked for outliers, gaps and trends, then used to forecast and score.
→ goes toreports · forecasts · alerts
[05] grounded decisions
Every decision, traced to its source. The agent shows where each word came from.
When the agent runs Docs.read, it reads the invoice itself and ties every word of its decision to the part of the page it came from. A person checks that trace and approves, or sends it back, before anything is paid.
AI agent developmenttrace.01 · read
It reads the page itself
Docs.read marks the supplier, date, PO number, line items and total before the agent decides anything.
trace.02 · cite
Every word points back
Each word of the decision is linked to the region it relied on, so the reasoning can be checked.
trace.03 · approve
A person signs off
The run holds at the approval step. Nothing is paid or posted until someone has checked the trace.
Illustration: the agent runs Docs.read on an invoice, marks the vendor, date, PO number, line items and total, then writes its decision word by word (“Hold: PO number and total do not match the order.”), with lines showing which part of the invoice each word relied on.
How we work
Five steps. A working pilot on your own data comes before the full build.
Every project runs in the same order. You get a plan with milestones after discovery, a fixed-scope quote, and an NDA before you share anything confidential.
Every project starts with a free, no-obligation consultation.
run.logappsinai / project
$ appsinai run --pilot-first
Step 1: Discover
We map the problem, the data you have and the systems involved, and agree what success looks like.
You get: problem, data and systems mapped · success criteria agreed
Step 2: Prove it
A pilot on your own data, measured against an agreed baseline, before you commit to a full build.
You get: pilot on your data · compared with the agreed baseline
- gatego / no-go on the results before “Build & integrate”
Step 3: Build & integrate
We build the production system and connect it to the tools your team already uses.
You get: production system · connected to your existing tools
Step 4: Launch & train
We roll it out, hand over documentation and train the people who will use it every day.
You get: rollout · documentation · team training
Step 5: Monitor & improve
We watch accuracy, cost and usage after launch, and keep improving the system over time.
You get: accuracy, cost and usage watched after launch
↺ results feed the next improvement cycle$
[07] why appsinai
Built for daily use, not for demos.
- 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.
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[09] faq
Questions buyers ask.
Anything else? Email sales@appsin.ai or call +91-9025555584.
01How much does an AI project cost?
Most projects are quoted at a one-time cost for an agreed scope. The price depends on the problem, the state of your data, the number of systems we connect to and whether we train a model or build on an existing LLM. Larger programmes can run with a dedicated team billed monthly. Model usage and hosting are paid to the provider from your own account, and we estimate them before you commit. We give you a range on the first call and a fixed quote after discovery. We do not work for equity.
02How long does it take?
A focused pilot, such as a chatbot on your documents or automating one process, usually takes a few weeks. Custom models, larger datasets and integrations with several systems take longer. After discovery you get a plan with milestones, and we recommend proving the approach on real data with a pilot before a full rollout.
03What data do we need to get started?
It depends on the job. An LLM assistant that answers from your documents needs those documents, not a training dataset. A predictive or computer vision model needs historical examples, ideally labelled. We start with a short data audit to check what you have and whether it is good enough. If it is not, we can help collect, clean and label it, and we will tell you honestly if the data cannot support the result you want.
04Which AI models and LLMs do you use?
We work with OpenAI, Anthropic Claude, Google Gemini and open-weight models such as Meta Llama and Mistral, plus PyTorch, TensorFlow and scikit-learn for custom machine learning. We pick the model for each task by testing candidates on an evaluation set of your real examples and comparing accuracy, cost, speed and data residency. We design the system so the model can be swapped later without a rebuild.
05Is our data secure? Will you sign an NDA?
Yes. We sign an NDA before you share any data. Where possible, data stays in your own cloud account, and we use business API tiers that do not train on your data, or open-weight models hosted privately when data must not leave your environment. Access is limited by role, data is encrypted in transit and at rest, and personal data can be masked before it reaches a model.
06Who owns the code and the models?
You do. On completion and final payment, the code, prompts, trained or fine-tuned models and documentation we build for you are handed over, as set out in the contract. Third-party and open-source models, APIs and libraries stay under their own licences.
07Will AI work with the systems we already use?
Yes. We connect AI to your CRM, ERP, helpdesk, databases, website and messaging channels such as WhatsApp through their APIs. Where a system has no API, robotic process automation can work through its screens. The aim is for your team to keep using the tools they know, with AI doing the repetitive work inside them.
08What happens after launch?
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. Support, maintenance and improvements are covered by an agreed support plan.
09Which countries do you work with?
Our clients are in India, the GCC (including the UAE, Kuwait, Qatar and Saudi Arabia) and the USA, and we work with them remotely from our office in Coimbatore.