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
Media & technology
Software engineering for AI and machine learning companies
Product engineering for companies building AI products: the apps, pipelines, evaluation and infrastructure around your models.
At a glance
The short version
- What we build
- How it works
- Your app or API
- Pre-processing
- Model gateway
- Guardrails
- 2 more
- Runs on
- Web app
- Admin
- Cross-platform
- Built with
- and 3 more tools
- Who it's for
AI companies are strong on models and stretched on the product around them. We build that product layer so your ML team stays on the model.
- Cost depends on
- Hosted or self-hosted models
- Evaluation depth
- Compliance needs
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 demo to dependable product
A notebook that answers well on twenty examples is not yet a product. Customers need a fast interface, an API with keys and limits, predictable costs, and answers that do not quietly degrade between model versions. Enterprise buyers ask where their data goes.
We build this layer around third-party, open-weight or your own trained models.
Challenges
Why AI products stall
No evaluation discipline
Without test sets, every prompt or model change is a gamble.
Inference costs
Token and GPU spend outpaces revenue without caching and limits.
Latency and outages
Slow responses and provider downtime need streaming and fallbacks.
Enterprise data concerns
PII redaction, residency and no-training terms come up in reviews.
Solutions
Related AI solutions
Multi-tenant SaaS products with AI at their core, built with tenant isolation, usage metering, model routing, evaluation and subscription billing.
Features that generate catalogue copy, reports, summaries, translations and images, with brand controls and a review step before anything is published.
Search and question-answering over your documents, tickets and product data, with hybrid retrieval, citations and permission-aware results.
Data extraction from invoices, KYC documents, statements and forms into your systems, with validation rules and human review for uncertain fields.
Automations for repetitive back-office work, with AI steps that read, classify and draft, and approvals where decisions matter.
The product layer we build
Model gateway
One API over several providers, with fallbacks.
Evaluation harness
Test sets, automated scoring and human review.
Annotation tools
Labelling and review workflows for training data.
Usage billing
Credits or per-call pricing from metered usage.
Human review queues
Low-confidence outputs routed to reviewers.
How it works
How a request travels through an AI product
Every answer passes through checks your customers never see, and those checks are most of the engineering.
- Your app or API
Step 1: A user sends a request
Someone asks a question or uploads a document. The request carries their account or API key, so rate limits and billing apply from the start.
- Pre-processing
Step 2: Personal data is redacted
Details such as phone numbers or PAN are masked where the customer's policy requires it, before anything reaches a model.
- Model gateway
Step 3: The gateway picks a model
The gateway routes the request to the right model, your own or a provider's, and falls back to another if the first is slow or down.
- Guardrails
Step 4: The answer is checked
Outputs are checked against rules and a confidence score. Low-confidence or sensitive answers go to a human review queue instead of straight to the user.
- Usage billing
Step 5: Usage is metered
Tokens, pages or calls are recorded against the customer's credits or plan, so cost and revenue stay visible for every account.
- Evaluation harness
Step 6: Evaluation guards each release
Before any prompt or model change ships, it runs against your test sets; a drop in scores holds the release until someone reviews it.
Platforms
Interfaces for AI products
- 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
Cross-platform mobile app
One Flutter or React Native codebase for Android and iOS, with native modules where a feature needs them.
Technology
AI and data stack
Python runs models, pipelines and evaluation, LLM integrations put third-party and open-weight models behind one gateway, and Next.js delivers the interface your customers use.
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
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
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
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
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 product terms, in plain English
- Token
- The small chunk of text, often part of a word, that AI models read and write and that providers bill by. Hindi and other Indian scripts often need more tokens per word than English, which changes cost estimates.
- Evaluation set
- A fixed collection of realistic inputs with expected answers or grading rules, agreed with your domain experts. Running every change against it shows whether quality improved or quietly got worse.
- Retrieval-augmented generation (RAG)
- The system first finds relevant passages in your own documents, then asks the model to answer from those passages. Answers can cite their sources, and updating the documents does not require retraining a model.
- Open-weight model
- An AI model whose trained weights are published, so it can run on your own servers or cloud account. It gives more control over data and cost at scale, in exchange for managing GPUs and upgrades yourself.
- PII redaction
- Automatically masking personally identifiable information, such as names, phone numbers or Aadhaar numbers, before text is stored or sent to a model. Enterprise buyers often ask about it in security reviews.
Product preview
The product your users see, and the consoles behind it
Illustrative screens for a document-question product; the same layers apply to other AI features.
Your product (web app). Users ask questions about their own documents and get answers with citations; uncertain answers are flagged for review rather than stated confidently. 
Review queue. Low-confidence or policy-sensitive outputs go to a trained reviewer who corrects them, and corrections can be added to the evaluation set. 
Evaluation console. Before release, every change is scored against your test sets, including Hindi and Hinglish cases; a regression holds the release until someone signs it off.
Illustrative preview
Sample screens: names, prices and figures are examples, not client data.
Cost drivers
Cost drivers for AI product engineering
Hosted or self-hosted models
Self-hosting adds GPU infrastructure, serving and monitoring.
Evaluation depth
Domain-specific test sets and human grading take expert time.
Compliance needs
Redaction, audit logs and regional hosting add scope.
Estimates are written from your scope, with the effort and assumptions behind each line item.
How pricing worksServices
Services for this industry
The design and engineering services our in-house team combines on projects in this sector.
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.
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.
SaaS development
Multi-tenant SaaS products with subscription billing, team roles, onboarding and usage analytics, from first MVP to paying customers.
UI/UX design
Product design for web and mobile apps: research, user flows, wireframes, prototypes and design systems that engineers can build from.
Locations
AI & machine learning by city
Cities where this sector is a significant part of the local economy. We work remotely with teams across India and meet in person where practical.
Karnataka
Bangalore
FAQ
Frequently asked questions
Do you train our models or build the product around them?
Mostly the product around them: interfaces, APIs, pipelines, evaluation, deployment and billing. We integrate third-party models, build retrieval-augmented systems and fine-tune smaller task-specific models where the data justifies it. We do not pre-train foundation models. If the model is your core IP, we build to your ML team’s interface.
How do you evaluate an LLM feature before release?
We build a test set of realistic inputs with expected outputs or grading criteria, agreed with your domain experts. Every prompt, model or retrieval change runs against it, scored by rules, a judge model with spot checks, and human reviewers for sensitive cases. It will not make outputs perfect, but it stops silent regressions.
Can AI products handle Hindi and code-mixed Hinglish input?
Often, but test rather than assume. Model quality varies across Indian languages, scripts and transliterated text. We add Hindi, regional-language and Hinglish examples to your evaluation set, compare models on them and handle script detection and transliteration. For voice products, speech recognition on Indian accents is a separate test.
Next step
Put your model in front of paying customers
Share your model setup and users; we will scope the product layer.