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
AI virtual assistant development
AI virtual assistants that get tasks done, not just answer questions
Assistants that book, schedule, look up records and raise requests by voice or text, with confirmation before every action.
At a glance
The short version
- What you get
- In-app assistant
- Voice line
- Employee assistant
- Operations view
- How it works
- App, phone line or Teams
- Assistant
- Your backend
- Patient or employee
- 2 more
- Runs on
- Android
- iOS
- Cross-platform
- Web app
- Admin
- Built with
- and 3 more tools
- Who it's for
- Cost depends on
- Number of tools
- Voice versus text
- Languages and accents
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 estimateFrom answering to acting
The difference between a chatbot and a virtual assistant is what it may do. An assistant works out which system a request needs and performs the step, such as creating a booking, after the user confirms.
That power needs structure. The assistant gets a short list of well-described tools, each backed by an API with its own validation, so the model chooses actions but never writes to your database directly. Anything irreversible, financial or ambiguous waits for an explicit yes, or goes to a person.
Challenges
Where an assistant saves time
Staff hunt across several tools
CRM, ERP and spreadsheets, just to answer one customer query.
Phone lines stay busy with routine requests
Reschedules, balance checks and callbacks tie up reception and call-centre staff.
Internal policies are hard to find
HR and IT answer the same leave and password questions every week.
Field teams work on the move
Reps need hands-free, voice-first access on patchy networks.
What is included
Capabilities by surface
Platform: Android, iOS, web
In-app assistant
- Text and push-to-talk voice input
- Proposed actions shown as cards to confirm or edit
- Context from the current screen, such as the open order
Platform: Telephony
Voice line
- Speech recognition and synthesis in English, Hindi and selected regional languages
- Barge-in, and transfer to a live agent with a summary
Platform: Teams, Slack or web
Employee assistant
- HR and IT policy answers with citations
- Leave, payslip and ticket actions via HRMS and helpdesk APIs
Platform: Admin dashboard
Operations view
- Tool catalogue with per-role permissions
- Action log of who asked, what ran and what changed
How it works
How the assistant turns a request into a confirmed action
Example: a patient moving a clinic appointment by voice. The same pattern applies to leave requests, callbacks and account look-ups.
- App, phone line or Teams
Step 1: User asks by voice or text
“Move my Thursday appointment to next week, mornings only.” Speech is turned into text, and key details are read back.
- Assistant
Step 2: Assistant picks the right tool
From a short list of allowed actions, such as “find slots” or “reschedule”, it chooses one and fills in the details it heard.
- Your backend
Step 3: Permissions are checked
The action runs with the signed-in user’s own access, never a shared admin account, so nobody can do more through the assistant than in the app.
- Patient or employee
Step 4: User confirms the change
A card shows exactly what will change, for example Tuesday 10:30 instead of Thursday 5 pm. Nothing is written until the user says yes.
- Booking, CRM or HRMS API
Step 5: Your system of record is updated
Your existing software makes the change with its own validation, and a confirmation goes out by notification or WhatsApp.
- Admin dashboard
Step 6: Every action is logged
Who asked, which tool ran and what changed are recorded for disputes, audits and DPDP Act access requests.
Integrations
Systems an assistant can act on
Scheduling
Google Calendar and Microsoft 365
Slot look-ups and bookings with delegated access.
Voice
Exotel, Twilio or existing telephony
Calls routed to the assistant and on to agents.
Speech models
Whisper, hosted APIs or Indic options such as Bhashini and AI4Bharat models, tested on your audio.
HRMS
Keka, greytHR, Zoho People
Leave and policy data where the API exposes it.
CRM
Salesforce, Zoho CRM, LeadSquared
Account look-ups and activity logging.
Appointments
Booking or practice systems
Clinic, service-centre or site-visit slots.
Channel
WhatsApp Business Platform
Reschedules and template reminders.
Privacy & security
Guardrails for an assistant that can act
Confirmation before any change
Changes are summarised for approval; the model never initiates payments.
Permissions follow the user
Tool calls run with the signed-in user’s scope, never a shared superuser account.
Voice data handled deliberately
Callers are told when AI handles or records a call; audio retention is set separately.
Complete action audit trail
Requests, tool calls and results are logged for disputes and DPDP Act access requests.
Platforms
Where the assistant runs
Inside your apps for Android and iOS, often built from one cross-platform codebase, in a web app for staff, and in an admin dashboard for permissions and logs.
- Mobile
Android app
Apps for Android phones and tablets, tested on budget and mid-range devices and published on Google Play or privately.
- Mobile
iOS app
Apps for iPhone and iPad, built to Apple's guidelines and released through TestFlight and the App Store.
- Mobile
Cross-platform mobile app
One Flutter or React Native codebase for Android and iOS, with native modules where a feature needs them.
- 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 behind an assistant that acts, and why it matters
Models interpret the request, NestJS and Node.js services carry out only approved actions, Redis keeps each conversation’s context, and Flutter brings voice and text to both phone platforms.
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
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
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
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
Mobile
Flutter
Build Android and iOS apps from one shared codebase, so you launch on both platforms faster with a consistent experience.
Used for
- Mobile apps
- Delivery apps
- E-commerce apps
- Booking apps
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
Flutter and the related logo are trademarks of Google LLC. We are not endorsed by or affiliated with Google LLC.
Plain-English glossary
Assistant terms, in plain English
- Function calling
- The way a language model asks your software to do something, such as “find slots on 14 March”, instead of doing it itself. Your code decides whether the request is allowed and carries it out, so the model never touches your database.
- Speech recognition (speech-to-text)
- Software that turns spoken words into text the assistant can act on. Accuracy changes with accent, background noise and phone-line quality, so we test it on recordings of your real callers before choosing a provider.
- Barge-in
- Letting a caller interrupt the assistant mid-sentence, as they would a person. Without it, callers must sit through long prompts, one of the main reasons people dislike phone bots.
- Maker-checker rule
- A control where one person, or the assistant, prepares a transaction and a second authorised person approves it. Finance teams use it so that no single step can move money unchecked.
- Audit trail
- A record of each request, the tool the assistant used and the result. When a customer disputes a booking or asks what data you hold, you can show exactly what happened and when.
Product preview
What users and your operations team see
Illustrative screens, using an assistant for a multi-doctor clinic and its staff as the example.
Patient app. Patients ask in plain words, and the assistant proposes the change for them to confirm, so a misheard word never becomes a wrong booking. 
Patient app. When a request is vague, the assistant shows matching slots instead of guessing, and the patient taps the one that suits them. 
Employee assistant. Staff ask about leave or payslips; the assistant answers from policy with a citation and files the leave request only after they confirm. 
Admin dashboard. Operations staff decide which actions each role may use, see how often each one runs and can switch a tool off instantly.
Illustrative preview
Sample screens: names, prices and figures are examples, not client data.
Industries
Teams that use virtual assistants
Services
Engineering work involved
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.
Mobile app development
Native and cross-platform Android and iOS apps, from first release through regular store updates, built by our in-house designers and engineers.
Web application development
Browser-based products, customer portals, dashboards and internal tools, built on clean data models with secure roles and integrations.
API development
Secure, documented REST and GraphQL APIs, plus integrations that connect your apps to payment, logistics, GST, messaging and business systems.
Flutter development
Android and iOS apps from one Dart codebase, built by engineers who know when Flutter is the right call and when native code is needed.
Cost drivers
Cost drivers for assistants
Number of tools
Each action needs an API, validation, confirmation UI and failure handling; writes cost more than reads.
Voice versus text
Telephony, speech recognition, latency tuning and accent testing add work.
Languages and accents
Each language needs its own test recordings and phrasing review.
State of your APIs
Systems without usable APIs need a middleware or sync layer first.
Estimates are written from your scope, with the effort and assumptions behind each line item.
How pricing worksWork
Sample projects
Illustrative projects that show how we plan and build this kind of product. They are samples, not client work.
AI solutions
Illustrative sampleAI customer support assistant for a D2C brand
An illustrative AI assistant that answers order, return and product questions from a D2C brand's own policies and order data, with sources and a human hand-off.
- E-commerce
- AI assistant
Runs on
- Website
- Admin
Built with
- Next.js
- Python
- OpenAI APIs
- LLM integrations
- PostgreSQL
- +1 more
FAQ
Frequently asked questions
How is a virtual assistant different from a chatbot?
A chatbot mainly answers questions; an assistant also acts in your systems for the user. That needs well-defined tools backed by APIs, authentication, per-action permissions, confirmation steps and an audit log. It keeps context across steps: find a slot, check the doctor’s availability, book, send a reminder. Many products start as a chatbot and add actions once the answers are reliable.
Can the assistant understand Hindi and regional languages by voice?
Hindi and Indian English work reasonably well with current speech models; other Indian languages vary with accent, audio quality and background noise. We collect sample audio from your real users, measure error rates and compare providers and open models before choosing. In noisy places such as shop floors, the assistant reads key details back, so a misheard word does not become a wrong booking.
Can it make payments or approve transactions?
Not on its own. The assistant can prepare the action, such as filling a reimbursement claim or requesting a payment link from your backend, and the user or an approver confirms it through a normal authenticated step. The busywork is automated, while the financial decision and controls such as maker-checker rules stay with people.
What does it need from our existing software?
APIs, mostly. Each action needs a way to read or write the relevant record with proper authentication. Many CRMs, HRMS tools, calendars and helpdesks already offer this; older or in-house systems may need a small API layer, which we can build. We also need a clear list of who may do what, because the assistant inherits those permissions rather than having broad access of its own.
Next step
List the tasks you want handled
Tell us the ten requests people make most often. We will map which can be automated safely.