Document extraction
Invoices, purchase orders, KYC (know-your-customer) packs, delivery notes and forms. We pull the fields you need out of each file, score how sure the model is of each one, and send the unsure ones to a person.
AI automation and integration services in India
Add AI to the ERP, CRM or helpdesk you already run — without rebuilding it.
We add document extraction, ticket triage, classification and summaries to the software your team already works in, using your existing logins and audit trail. The model scores how sure it is of each item, and anything below the level you set goes to a person.
- Works inside the tools you already use
- Unsure items go to a person
- Can run on infrastructure you control
In brief
- What it is
- AI added to software you already run, such as an ERP, CRM, helpdesk or admin panel. It reads documents, sorts queues, tags records and summarizes what nobody has time to read.
- Why it matters
- Your team keeps the tools and data it relies on. The model takes the routine part, and a person still decides anything the model isn't sure about.
- What you get
- A working automation inside your existing system, a triage queue for the items that need a person, and a security review before launch.
Where it helps
The routine work AI can take off your team.
Inbox and ticket triage
We classify, prioritize, route and draft replies, so the queue nobody wants to own is handled consistently.
Classification and tagging
Products, transactions, support themes, CVs and content, sorted into your own categories. The uncertain ones go to a person.
Summaries at volume
Calls, meetings, threads and research. It helps most where there's so much of it that nobody reads any of it today.
Data cleaning and matching
Removing duplicates and linking records that describe the same thing (entity resolution), including two systems that spell the same customer four different ways.
Built into your stack
Each of these runs inside the system your team already uses, not in a separate app.
Deciding how much of a process to hand over? See AI automation. Adding a hosted model to software that's already running? See AI integration. Need AI that plans and runs longer, multi-step work in your systems? See AI agent development, or go back to all AI development.
Inside your stack
AI inside your systems, not one more tool beside them.
For most companies, the useful AI work is three or four jobs that take up someone's week. Think of reading invoices, sorting an inbox, or pulling five fields out of a PDF that arrives in four different layouts.
That work rarely needs a change to your product, let alone a new system. We build inside the software you already run.
What stays the same
- Your tools. Your team keeps working in the CRM, ERP, helpdesk or admin panel it already uses.
- Your logins and audit trail. We build with the ones you have, not a second set beside them.
- Your say on anything with consequences. In a helpdesk, the model can read the ticket, look up the order and draft the reply. A refund waits for a person to approve it.
What changes
Two things usually limit what we can build: how people sign in and what each of them may see, and where your data may be stored and processed. If the model is hosted by a provider, your software also depends on that provider. The provider decides when that model is retired and what each use costs. AI integration covers how to plan for a model's retirement and its usage costs.
Low-confidence items
Items below your threshold go to a person.
The model will get some cases wrong, so we plan for it from the start.
Every item the model handles carries a confidence score: how sure it is of its answer. You set the threshold. Items at or above it go straight through. Anything below it goes to a person on your team, with a reason code.
What that gives you
- A person on every unsure item. It reaches someone on your team with the reason attached, instead of going straight through.
- A clear view of weak spots. The triage queue shows you where the model struggles.
How we work
Measure first, pilot on real data, then build it in.
You see how much of the job the model can take on before anything goes live.
Measure the manual process
We look at the job as your team does it today: what the task is, roughly how many items it handles a week, and which system it runs in.
Agree where data may go
We check how people sign in, what each of them may see, and where your data may be stored and processed. For sensitive or regulated data, we set out the options and the trade-off. The decision stays with you.
Pilot on a real sample
We run the model on a real sample of your items. It shows how many clear your threshold, how many would go to a person, and how often the ones that clear it are still wrong.
Build it into your system
We add it to your CRM, ERP, helpdesk or admin panel, with a triage queue for everything below the threshold.
Security review before launch
Our own security team, not the people who built the system, reviews and hardens it before it goes live, and verifies each fix. See AI security.
An example
What happens when a helpdesk refund needs a person.
Actions with real consequences wait for a person to approve them. Here is how that works in one run with sample data.
The example run, step by step
The model reads the ticket, looks up the order, searches the knowledge base for the refund policy and drafts a reply. In this example, those four steps only read data, so they run on their own.
Issuing the refund would change data, so the run stops there and asks a person to approve or reject it. When someone approves, the log records that a person approved it, and when.
For longer, multi-step work that the AI plans itself, see AI agent development.
What a pilot can't promise. A pilot's error rate is a point-in-time result for the sample it ran on.
FAQ
Questions about AI automation and integration.
Scope, mistakes, your data, and how automation and integration differ. For anything else, ask us directly.
We let a model search documents you already hold (a retrieval layer), put one behind an existing form, or wire an assistant into your admin panel. We build it inside your stack, using your existing logins and audit trail. A hosted model then becomes a dependency, with a retirement date and a usage meter that its provider sets. AI integration covers how to plan for both.
It covers using AI to take repetitive work out of a process you already run: reading documents, triaging a queue, classifying records and summarizing volume nobody reads today. It rarely changes your product. How much of a process it can take on depends on your cases, so we measure the manual process first and then run a pilot on a real sample. AI automation explains the method.
Often both, because they answer different questions about the same project. Automation asks how much of a process a model can take on, and what happens to the cases it can't call. Integration asks what changes in your existing software once a hosted model becomes one of its dependencies. An invoice pipeline that feeds your ERP raises both questions.
It will, sometimes, so we design for it. Every extracted item gets a confidence score. Anything below the threshold you set goes to a person with a reason code, and that queue shows you where the model struggles. A pilot on a real sample tells you how many items land on each side before you rely on it.
Not necessarily. For sensitive or regulated data we can run open-weight models on infrastructure you control, or redact data before anything leaves your network. That costs more, and sometimes it's the only acceptable design. We set out the trade-off, and the decision stays with you.
Let's talk
Have a queue nobody wants to own?
Tell us what the task is, roughly how many items it handles a week, and which system it runs in. We reply within one working day.