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 engineering
AI app development grounded in your data and workflows
Practical AI features, built by the engineers who also ship the app around them.
At a glance
The short version
What it is
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.
- What we build
- Customer support assistants
- Document extraction
- Internal knowledge search
- Drafting copilots
- How it works
- Customer or employee
- Search over your data
- AI model
- Guardrails
- 2 more
- Runs on
- Web app
- Admin
- Android
- iOS
- Cross-platform
- Built with
- and 4 more tools
- Who it's for
- Cost depends on
- Kind of AI component
- Condition of your data
- Accuracy bar
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 estimateStart with the task, then choose the model
A sound AI project starts with a narrow, checkable task: answer order-status questions, pull line items from supplier invoices, suggest what a repeat customer will reorder. We write it down with examples of good and bad output before picking a model.
That decides the architecture: a hosted model API, retrieval over your documents, or a small model trained on your data, which can be cheaper and more predictable. The same examples become the test set we measure quality against, before launch and after every change.
Use cases
What teams ask us to build
Customer support assistants
Order, policy and account answers on web chat or WhatsApp, with hand-over to an agent.
Document extraction
Invoices, purchase orders or KYC forms read into fields; uncertain values go to a person.
Internal knowledge search
Answers from SOPs, contracts and past tickets, filtered by each employee's access rights.
Drafting copilots
First drafts of replies, product descriptions or reports that staff edit and approve.
Forecasts and scores
Demand forecasts, lead scores and risk flags trained in Python on your history.
Indian-language voice and text
Speech and text in Hindi and other Indian languages, tested on real recordings since quality varies.
Safeguards included in every AI build
Evaluation before launch
Real examples with expected answers, scored automatically and by reviewers.
Human review where errors are costly
Review queues and escalation for anything touching money, health, legal standing or a customer's account.
Spending limits
Token budgets per user and feature, caching, smaller models for routine steps and usage alerts.
Privacy-aware data handling
Personal data redacted before prompts and excluded from training under provider terms; retention mapped to the DPDP Act, 2023.
Guardrails and fallbacks
Prompt-injection checks, validated outputs, out-of-scope refusals and a non-AI path when a model fails.
Solutions
AI solutions built on this service
Support and sales chatbots for web, app and WhatsApp that answer from your own content and hand off to your team.
Assistants that book, schedule, look up records and raise requests by voice or text, with confirmation before every action.
Automations for repetitive back-office work, with AI steps that read, classify and draft, and approvals where decisions matter.
Data extraction from invoices, KYC documents, statements and forms into your systems, with validation rules and human review for uncertain fields.
Search and question-answering over your documents, tickets and product data, with hybrid retrieval, citations and permission-aware results.
Features that generate catalogue copy, reports, summaries, translations and images, with brand controls and a review step before anything is published.
Product, content and offer recommendations built from your behavioural data, with business rules, cold-start handling and A/B testing.
Multi-tenant SaaS products with AI at their core, built with tenant isolation, usage metering, model routing, evaluation and subscription billing.
How it works
How an AI assistant answers from your documents
The model never answers from memory alone: it searches your approved content first, and a person steps in wherever a mistake would be costly.
- Customer or employee
Step 1: Someone asks a question
They type in plain words on web chat, WhatsApp or an internal tool, for example “Can I return a product bought in the sale?”, in English or Hinglish.
- Search over your data
Step 2: The system searches your documents
It finds the policy sections, product pages or order details the person is allowed to see, instead of relying on what the model happens to remember.
- AI model
Step 3: The model drafts an answer
A language model writes a short reply using only what was found and notes the source; if nothing relevant turns up, it says so rather than guessing.
- Guardrails
Step 4: Checks decide if a person should look
Rules flag drafts about refunds, money, health or anything low-confidence, and send them for review instead of straight to the user.
- Support team
Step 5: Staff review where needed
A team member approves, edits or rewrites the flagged draft; simple, well-sourced answers go out without waiting.
- Admin dashboard
Step 6: Answer delivered and logged
The reply, its sources and any edits are logged, so your team can track accuracy and cost and fill gaps in the knowledge base.
Platforms
Where your AI features can live
Inside your web application or your apps for Android and iPhone, with an admin dashboard where your team manages documents, reviews answers and watches costs.
- 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.
- 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.
Technology
The AI stack, and why each part is there
Model APIs, including OpenAI’s, draft and summarise text; Python handles data preparation and evaluation; PostgreSQL stores your content and its search index; Node.js, NestJS and Next.js run the application around the model; Redis caches repeat answers; hosting is built on AWS.
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
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
Web
Next.js
A modern web technology for fast, search-friendly websites, online stores and web applications that load quickly on mobile.
Used for
- Business websites
- Online stores
- Web apps
- Customer portals
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
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
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
Plain-English glossary
AI terms, in plain English
- Large language model (LLM)
- The AI engine that reads and writes text, of the kind behind popular chat assistants. On its own it can sound confident while being wrong, which is why we connect it to your documents and add checks.
- RAG (retrieval-augmented generation)
- Searching your own documents first and giving the model only those passages to answer from. Answers stay grounded in your policies and can show the source they came from.
- Hallucination
- When an AI model states something plausible that is not true. Grounding, citations and review reduce it, and we measure how often it still happens before and after launch.
- Evaluation set
- A collection of real questions paired with the answers your team considers correct. Every change to prompts or models is scored against it before it goes live.
- Tokens
- The small chunks of text that AI providers charge by; an English word is roughly one to two tokens. Shorter prompts, caching and smaller models for routine steps keep token use, and the monthly bill, in check.
- Human in the loop
- A step where a person approves or edits the AI’s output before it is sent or saved. We place it wherever a wrong answer would cost money, trust or safety.
Product preview
What an AI assistant looks like to users and to your team
Illustrative screens from a support assistant that answers from a retailer’s own policies, with the admin screens behind it.
Customer chat. Customers get short answers that quote your own policy, and requests like returns go to staff for approval instead of being decided by the AI. 
Admin: knowledge base. Your team controls what the assistant knows: upload, update or remove documents, and outdated policies are excluded from answers. 
Admin: review queue. Anything touching money, safety or a missing policy waits for a person, and each decision is logged to improve the assistant. 
Admin: quality and cost. A dashboard tracks answers, hand-offs, sourcing and model spend per answer, so you can see whether the assistant helps and what it costs.
Illustrative preview
Sample screens: names, prices and figures are examples, not client data.
Work
Illustrative sample project
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
Industries
Industries where these patterns apply
Cost drivers
What shapes the cost of an AI build
Kind of AI component
API integrations are usually smallest; RAG adds ingestion and search; custom ML adds training and hosting.
Condition of your data
Scanned PDFs, mixed languages or unlabelled history need cleaning first.
Accuracy bar
Financial, medical or legal stakes call for larger evaluation sets, review queues and audit trails.
Integrations
Connecting your CRM, ERP, helpdesk or WhatsApp Business Platform often outweighs the AI step.
Usage and hosting
Hosted models bill per token; self-hosting an open-weight model means GPU infrastructure instead.
Estimates are written from your scope, with the effort and assumptions behind each line item.
How pricing worksProcess
How an AI project moves from idea to production
Use-case and data review
Task, users, data sources and the cost of a wrong answer, and whether plain rules would do.
You getScoped use case
Evaluation set and baseline
Real examples and expected outputs agreed with your team; candidate approaches scored against them.
You getBaseline scores
Prototype on masked data
A few staff use it while we measure quality, response time and cost per request.
You getCost estimate
Production build
Backend, retrieval or model service, admin screens, review queues, audit logs and fallbacks.
You getStaging application
Supervised pilot
A limited rollout with outputs reviewed; each failure joins the evaluation set.
You getError analysis
Launch and monitoring
Quality, cost and latency dashboards; evaluations re-run before any prompt or model change.
You getMonitoring dashboard
More specific pages
Also available locally
AI app development in
FAQ
Frequently asked questions
Should we use a hosted model API or train our own model?
For most text tasks a hosted model API is quicker and cheaper to start with, and good prompts plus retrieval usually give sufficient quality. Training your own model suits a narrow, repetitive decision with plenty of labelled history, such as demand forecasting, or data that cannot leave your infrastructure. Fine-tuning sits in between. We compare the options on your evaluation set before recommending one.
How do you stop an AI assistant from making things up?
No one can remove that risk completely, so we reduce and contain it. Answers are grounded in retrieved documents with cited sources, the assistant says so when it finds nothing relevant, structured outputs are validated, and high-risk replies go to a review queue. We measure the error rate before launch and keep tracking it on live traffic.
Is our customer data safe if we send it to an AI provider?
It depends on the provider, the plan and the integration. We use API terms that exclude your data from model training, redact personal details the model does not need, prefer Indian cloud regions where offered, and can self-host an open-weight model for highly sensitive data. We map these choices to the DPDP Act, 2023; your legal adviser confirms the final position.
How long does it take to add an AI feature to an existing product?
A focused API integration typically takes a few weeks, including evaluation. A RAG assistant with an admin panel and access control typically takes one to three months, depending on data quality and integrations. Custom ML depends heavily on the data, which sometimes needs weeks of preparation first. We commit to a timeline after the use-case review, not before.
How do you keep monthly model costs predictable?
We measure cost per request during the prototype and design around it: shorter prompts, caching repeated answers, cheaper models for routine steps, batch processing for non-urgent jobs and per-user limits. A dashboard shows spend by feature, with alerts at thresholds you set. Provider prices change often, so we keep the model layer swappable.
Next step
Have a task you think AI could handle?
Send the workflow and a few real examples. We will suggest an approach, estimate it, and say so if AI is the wrong tool.