Backend
Python
A programming language for AI features, data processing and automation: the engine behind document reading, reports and smart search.
Used for
- AI features
- Document reading
- Reports and analytics
- Task automation
AI workflow automation
AI workflow automation for the repetitive steps, with people on the decisions
Automations for repetitive back-office work, with AI steps that read, classify and draft, and approvals where decisions matter.
At a glance
The short version
- What you get
- Triggers and intake
- AI and rule steps
- Approvals and exceptions
- Monitoring
- How it works
- Shared inbox
- AI step
- Workflow engine
- Operations staff
- 2 more
- Runs on
- Web app
- Admin
- Built with
- and 3 more tools
- Who it's for
- Cost depends on
- Number of exceptions
- Systems without APIs
- Document variety
How to start
Tell us what you need in your own words. You talk to the developers who would build it and get a written, line-item estimate.
Get a project estimateAutomation your operations team can audit
The workflows worth automating first are high-volume, rule-heavy and dull: keying purchase orders into the ERP, triaging a shared inbox, chasing missing KYC documents. We start by writing down today’s process, exceptions included.
Then we split the work. Code handles what is certain: records, totals, API calls. A language model handles reading and writing, such as classifying an email or drafting a reply. Anything above a value threshold or below a confidence threshold stops for a person to approve.
Challenges
Signs a process is ready for automation
Data is retyped between systems
Orders from email or WhatsApp are keyed into Tally or an ERP by hand.
Shared inboxes need constant sorting
Someone spends each morning forwarding enquiries, invoices and complaints.
Follow-ups depend on memory
Document, payment and renewal reminders go out when someone remembers.
Exceptions hide in spreadsheets
Mismatched invoices and failed deliveries sit in sheets nobody owns.
What is included
What a workflow is made of
Platform: Background services
Triggers and intake
- New email, WhatsApp message, form or file upload
- Schedules and webhooks from CRM, ERP, payment or courier systems
- Duplicate detection so nothing runs twice
Platform: Workflow engine
AI and rule steps
- Classify intent, urgency and department
- Extract fields into a validated schema
- Draft replies and summaries for review
Platform: Web app
Approvals and exceptions
- Approval inbox with the source document alongside
- Value and confidence thresholds per workflow
Platform: Admin dashboard
Monitoring
- Run history with each step’s input and output
- Failure alerts and model cost per run
- Pause a workflow without a deployment
How it works
How an emailed purchase order reaches your ERP
One real example. Invoice capture, lead routing and KYC follow-ups use the same pattern of AI reading, code checking and people approving.
- Shared inbox
Step 1: An email with a PO arrives
A buyer mails a PDF purchase order to your orders inbox. The workflow picks it up within minutes and checks it has not been processed before.
- AI step
Step 2: AI reads and classifies it
The model recognises a purchase order and pulls out buyer, items, quantities and rates into a fixed format.
- Workflow engine
Step 3: Rules check the numbers
Code confirms the customer exists, SKUs match your item master, totals add up and the GSTIN format is valid.
- Operations staff
Step 4: Big or unclear orders wait for approval
Orders above your value limit, or with fields the AI was unsure of, appear in an approval inbox beside the original PDF.
- ERP or Tally
Step 5: The sales order is created
Approved data is posted through the system’s own interface, and the run ID is stored so anyone can trace the order back to the email.
- Reply step
Step 6: The buyer gets a reply
An acknowledgement with the order number goes back to the buyer. If any step fails, the team is alerted instead of the email quietly sitting there.
Integrations
Systems workflows commonly touch
Email
Gmail and Microsoft 365
Shared inboxes via Gmail API and Microsoft Graph.
Messaging
WhatsApp Business Platform
Incoming orders and template reminders.
Accounting
Tally, Zoho Books and ERPs
Vouchers and sales orders; Tally via its XML interface.
CRM
Zoho CRM, HubSpot, LeadSquared
Lead routing and stage updates.
Leads
IndiaMART lead feeds
Buyer enquiries pulled into qualification flows.
Spreadsheets
Google Sheets and Excel
Often the interim system while you migrate.
Orchestration
n8n, Temporal or a custom queue
n8n for simple flows; code where testing matters.
Privacy & security
Controls built into every workflow
Least-privilege service accounts
Credentials limited to the mailboxes, folders and API scopes a workflow needs.
Secrets kept out of code
Keys live in a secrets manager and are rotated.
Minimal data to the model
Only the fields a step needs reach a model, with PAN or account numbers masked.
Replayable, retained logs
Run logs support audits and DPDP Act requests, with retention agreed per workflow.
Platforms
Where your team manages automations
Workflows run quietly as background services; people use a web app for approvals and an admin dashboard to monitor, pause and retry runs.
- Web
Web application
Browser-based applications with logins, roles and workflows, such as customer portals, SaaS products and internal tools.
- Back office
Admin dashboard
Back-office panels for operations, support and finance teams: orders, users, content, reports and permissions.
Technology
Technology that runs your workflows, and why it matters
Python and Node.js services do the exact work, Redis-backed queues make retries safe, PostgreSQL keeps the run history, and language models are called only where text needs reading or writing.
Backend
Node.js
Runs the server side of apps: fast, scalable back ends that power your app, website and integrations.
Used for
- App back ends
- Live order tracking
- Chat and notifications
- Payment processing
Backend
NestJS
A structured way to build back ends on Node.js, so large business systems stay organised, testable and easy to hand over.
Used for
- Business app back ends
- SaaS platforms
- Marketplaces
- Admin panel back ends
Adds AI features such as chat assistants, document summaries and smart search to your product, built on OpenAI models with human review.
Used for
- AI chatbots
- Document summaries
- Smart search
- Drafting replies
Connects your product to AI language models from several providers, so each task uses a suitable model and you can switch later.
Used for
- AI assistants
- Answers from documents
- Workflow automation
- Ticket triage
Data
PostgreSQL
A reliable database for the records your business runs on: orders, payments, bookings and stock, kept accurate and easy to report on.
Used for
- Orders and customers
- Payments and ledgers
- Stock and inventory
- Bookings
Data
Redis
Keeps frequently used data in fast memory, so apps stay quick on busy days and live features like order tracking feel instant.
Used for
- Faster apps
- Live order status
- Shopping carts
- Job queues and alerts
Cloud & DevOps
Docker
Packages your software so it runs the same way on every laptop and server, which makes releases predictable and moving hosts easier.
Used for
- Reliable releases
- Same setup everywhere
- Faster onboarding
- Easy scaling
Cloud & DevOps
AWS
Cloud hosting for your app, website and data, with data centres in India and room to grow when traffic rises.
Used for
- App hosting
- File and photo storage
- Backups
- Busy sale days
Plain-English glossary
Automation terms, in plain English
- Webhook
- A notification one system pushes to another the instant an event happens, such as “payment received” or “shipment delivered”. Workflows start from these, so nobody has to watch a portal and copy updates across.
- Confidence threshold
- A cut-off score below which the AI’s reading of an email or document is not trusted. Items under it stop for a person, so the automation handles the clear cases and your staff handle the doubtful ones.
- Shadow mode
- A trial period in which the automation runs alongside your staff but changes nothing. Its results are compared with theirs, and it is only allowed to act once the error rate is acceptable to you.
- RPA (robotic process automation)
- Software that clicks and types through screens the way a person would. It breaks when a screen layout changes, so we use it only for systems that offer no other way in.
- Orchestrator
- The engine that runs each workflow step in order, retries failures and records every run. Low-code tools suit simple flows; a coded orchestrator suits high volume, complex rules and strict audit needs.
Product preview
Screens your operations team works in
Illustrative screens, using an order-intake and invoice automation for a distributor as the example.
Approval inbox. Only the exceptions reach people. Each item says why it stopped, such as a value above your limit or a field the AI was unsure of. 
Approver on the go. Managers can approve a flagged order from their phone, with the checks that passed and the one that needs a decision listed plainly. 
Admin dashboard. Every run is listed with its trigger and outcome; failed steps can be retried, and any workflow can be paused without a deployment. 
Admin dashboard. Volumes, exception rate and AI spend per workflow show what the automation is doing, and which process to automate next.
Illustrative preview
Sample screens: names, prices and figures are examples, not client data.
Industries
Operations-heavy industries we automate for
Services
Services that power automation
AI app development
AI features and AI-first apps built on model APIs, retrieval over your own data, workflow automation or custom ML, with evaluation and human review.
Custom software development
Software shaped around how your business runs, from approvals and inventory to billing and reports, replacing spreadsheets and disconnected tools.
API development
Secure, documented REST and GraphQL APIs, plus integrations that connect your apps to payment, logistics, GST, messaging and business systems.
Admin panel development
Back-office dashboards built around your team's daily tasks: order queues, approvals, catalogue management, payouts, reports and audit logs.
Web application development
Browser-based products, customer portals, dashboards and internal tools, built on clean data models with secure roles and integrations.
Cost drivers
What drives effort in automation
Number of exceptions
Each exception type needs its own rule, test and approval route.
Systems without APIs
File imports or browser automation are slower to build and more fragile.
Document variety
Ten supplier formats take longer than one; scans add OCR work.
Volume and timing
Near-real-time processing needs queues and monitoring a nightly batch does not.
Approval design
Multi-level approvals with escalation add interface and rules work.
Estimates are written from your scope, with the effort and assumptions behind each line item.
How pricing worksFAQ
Frequently asked questions
Should we use Zapier, n8n or custom code?
It depends on volume, complexity and testing needs. Zapier or Make suit light flows owned by a business team. Self-hosted n8n handles moderate flows and keeps data on your server. Custom code with a queue or an orchestrator such as Temporal fits complex rules, high volume or strict audit needs, because it can be version-controlled and unit-tested. We often combine them.
How is AI automation different from traditional RPA?
Classic RPA replays clicks and keystrokes on screens. It suits stable, structured tasks but breaks when a layout changes, and it cannot read unstructured text. We use API integrations for mechanical steps and language models only where text needs reading or writing, such as classifying an email. Screen automation stays a fallback for systems with no other access.
How do you prevent an automation from making costly mistakes?
By limiting what it can do alone. Items above a value, below a confidence score or matching a risk rule stop for approval. Extracted data is checked: totals match, the GSTIN format is valid, the customer exists. New workflows run in shadow mode first, with a person comparing results against their own work before the automation is allowed to act.
How long before a workflow is live?
One well-understood workflow with two or three integrations typically reaches production in four to eight weeks, including a shadow-mode period. Many document formats, legacy systems without APIs or multi-level approvals take longer. We suggest starting with one process, measuring time saved and error rates for a few weeks, then choosing the next one on that evidence.
What are the running costs after launch?
Three things: hosting for the workflow engine and database, model usage billed per request, and maintenance when connected systems change their APIs. Model cost depends on how many items pass through AI steps and how long the texts are, so we call models only where needed and use smaller ones for simple classification. The dashboard shows the actual cost per run.
Next step
Walk us through one process
Show us real examples, awkward ones included, and your weekly volume. We will propose what to automate first.