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⚡ AI THAT ACTUALLY WORKS

Not Another ChatGPTWrapper.Real AI. Real Results.

Anyone can paste an OpenAI API key and call it AI. We do not do that. We design intelligence systems — trained on your data, fine-tuned for your use case, and integrated into your product so deeply that AI is not a feature — it IS the product.

6
Live AI-powered products
3
AI systems in production
2016
Building since
Real Data
No synthetic benchmarks
YOUR DATA
User behavior
Business data
External APIs
CLEANING
Data Cleaning &
Transformation
AI MODEL
YOUR PRODUCT
Recommendation
Automation
Intelligence
INPUT
PROCESSING
INFERENCE
OUTPUT

Most 'AI Features' Are
Just API Calls.

Here is the difference between adding a ChatGPT button and actually building AI.

API Wrapper
Someone pastes an OpenAI key, adds a text input, and calls the response 'AI-powered'.
  • Same output for all users
  • No learning from your data
  • No integration with your system
  • Breaks when OpenAI changes pricing
  • Gives wrong answers confidently
This is what 80% of 'AI products' are.
Basic Integration
Connecting AI APIs thoughtfully — with proper context injection, structured prompting, and output validation.
  • Context-aware responses
  • Validated and structured output
  • ~ Somewhat personalized
  • ~ Limited learning capability
  • ~ Still dependent on external models
Good for most products getting started.
Real AI System
AI that is trained on your specific data, fine-tuned for your use case, integrated with your entire system, and improves with every user interaction.
  • Trained on your actual data
  • Learns from every interaction
  • Integrated with your entire system
  • Improves measurably over time
  • Custom models for your domain
This is what we build.
We have built real AI systems into AskBLR, NextGirl, and MNCJob. These are not demo apps — they serve real users daily. That experience is what we bring to client work. We will also tell you honestly when a basic API integration is all you actually need.

Seven AI Capabilities.
One Technical Partner.

Every capability below is something we have already built — in our own products or for clients. No theoretical promises.

01
CAP 01

AI Recommendation Systems

Recommendation engines that learn from each user's behavior and get smarter with every interaction. Product recommendations, content suggestions, job matching, financial product guidance. Built to improve measurably over time — not static rule-based suggestions.

E-commerceJob platformsContent
→ NextGirl (fashion), MNCJob (jobs)
02
CAP 02

Natural Language Search & Query

Search that understands what users MEAN — not just what they type. Semantic search, intent detection, multilingual support, and context-aware result ranking. We built this for AskBLR in Kannada and English.

City searchProduct searchKnowledge base
→ AskBLR (Bengaluru city AI)
03
CAP 03

Contextual AI Chatbots

Chatbots that know your business, your products, your FAQs, and your policies — built with RAG (Retrieval Augmented Generation) so answers are grounded in YOUR data. Not hallucinating from general knowledge. Answers that are accurate, not approximate.

Customer supportSales qualificationOnboarding
→ AskBLR (hyperlocal Q&A)
04
CAP 04

Intelligent Classification & Tagging

Automated content classification, sentiment analysis, spam detection, lead scoring, and document categorization. Feed in unstructured data — get back structured, actionable output. Connected to N8N workflows for full automation.

Lead scoringContent moderationDocument processing
→ FreeBill (invoice processing)
05
CAP 05

Predictive Analytics & Scoring

Machine learning models that predict future outcomes from historical patterns. Churn prediction, revenue forecasting, demand planning, user lifetime value scoring. Trained on your data — not generic industry models that don't reflect your reality.

Churn predictionRevenue forecastDemand planning
→ FinCalc (financial modeling)
06
CAP 06

Computer Vision & Image AI

AI that understands images — product visual search, automated image tagging, visual similarity matching, quality inspection, and OCR for document processing. Fashion visual search, medical image tagging, and document digitization.

Visual searchImage taggingOCR / Document AI
→ NextGirl (visual fashion search)
07
CAP 07

RAG Systems & Custom LLM Integration

Retrieval Augmented Generation — connecting Large Language Models to your private knowledge base, documents, and database. Ask questions of your own data in natural language. Internal knowledge management, AI-powered documentation, and smart search over proprietary information.

Knowledge base AIDocument Q&AInternal tools
→ AskBLR (local knowledge engine)
OUR PROCESS

AI Integration

Done Properly.

AI implementation fails 90% of the time because teams skip the first three steps. Here is how we do it correctly.

1

Define the AI Problem Precisely

Day 1-3

Before touching any model, we spend time defining exactly what decision the AI needs to make. Most AI projects fail because the problem was defined vaguely. 'Add AI' is not a problem definition. 'Predict which users will churn in the next 30 days' is.

Crisp AI problem statement
Success metric definition
'Build vs buy' recommendation
2

Audit Your Existing Data

Week 1

No data — no AI. We audit what you have, identify gaps, and design a data collection strategy if needed. We also evaluate data quality — bad data produces bad AI regardless of how sophisticated the model is.

Data quality assessment report
Data collection gap analysis
Privacy and compliance review
3

Select the Right Model Architecture

Week 1-2

GPT-4 is not the answer to every problem. Sometimes a fine-tuned small model outperforms a large general model for your specific use case — at 10% of the cost. We select the architecture that fits the problem, your budget, and your performance requirements.

Model comparison analysis
Cost vs performance tradeoff report
Architecture decision document
4

Build, Train and Evaluate

Week 2-5

Model development with rigorous evaluation. We do not ship an AI feature until it passes defined accuracy thresholds on held-out test data. Every model has a baseline to beat and a minimum performance bar to cross before production deployment.

Trained model with evaluation metrics
Baseline comparison report
Edge case analysis documentation
5

Integrate Into Your Product

Week 5-7

Model plus product integration — API layer, real-time inference endpoints, caching strategy for latency, fallback behavior when model confidence is low, and monitoring setup for ongoing performance tracking.

Production API endpoints
Latency optimization
Monitoring and alerting setup
6

Monitor, Retrain, Improve

Ongoing

AI is not 'set and forget'. Model performance drifts as data patterns change. We set up monitoring dashboards, define retraining triggers, and establish ongoing improvement cycles so your AI gets better over time — not worse.

Model performance dashboard
Drift detection monitoring
Quarterly retraining schedule

Models and Tools We Work With.

No Buzzword Bingo.

Every tool below is something we have actually used in production — not just something we listed to look impressive.

LANGUAGE MODELS
OpenAI GPT-4oOpenAI GPT-4-turboAnthropic ClaudeGoogle GeminiLlama 3 (self-hosted)Mistral
ML FRAMEWORKS & TOOLS
LangChainLlamaIndexHuggingFaceScikit-learnTensorFlowPyTorch (basic)Transformers
VECTOR DATABASES & SEARCH
PineconeChromaDBWeaviatepgvector (PostgreSQL)ElasticsearchQdrant
DEPLOYMENT & MONITORING
ReplicateModalAWS SageMaker (basic)Weights & BiasesLangSmithSelf-hosted inferenceAPI rate management
What We Do NOT Do

We do not build autonomous AGI or self-learning systems that operate without human oversight.

We do not train foundation models from scratch. We work with existing models and fine-tune them.

We do not make accuracy guarantees. We set targets, measure them rigorously, and tell you honestly if a model cannot meet your requirements.

We do not build AI for surveillance, manipulation, behavioral profiling of unknowing users, or any system designed to deceive.

AI We Have Actually Built.

Running Today.

Not mockups. Not demos. These AI systems serve real users in production today.

AskBLR

AI CITY INTELLIGENCE
AI FEATURES INSIDE
Natural language city search in Kannada + English
Location-aware query understanding
Intent detection for local queries
HOW IT WORKS
User types a local question → NLP model detects intent → location resolver adds context → ranked results returned
askblr.com → Live AI product

NextGirl

AI FASHION COMMERCE
AI FEATURES INSIDE
Visual product similarity matching
Style preference learning system
Personalized catalog recommendation
HOW IT WORKS
User views product → embedding generated → vector similarity search → related products ranked by visual + behavioral similarity
nextgirl.in → Live AI commerce

MNCJob

AI RECRUITMENT PLATFORM
AI FEATURES INSIDE
Job-candidate compatibility scoring
Resume parsing and skill extraction
Employer intent matching
HOW IT WORKS
Resume uploaded → skill entities extracted → job requirements vectorized → compatibility score calculated and ranked
mncjob.in → Live AI matching

Want this for your product?

Discuss Your AI Build →

Three Ways to Bring
AI Into Your Product.

FASTEST

Add AI to Existing Product

You have a working product and want to add AI capabilities — recommendations, search, chatbot, or automation. We assess what's possible with your current data and implement the highest-impact AI feature first.

Timeline: 4-8 weeks
AI capability audit of your product
Highest-impact feature identification
Model build and integration
Performance measurement setup
Audit My Product →
MOST POPULAR

Build an AI-First Product

You have an idea for a product where AI is the core value proposition. We scope the AI architecture, data requirements, and model strategy from day one — not bolted on later.

Timeline: 10-16 weeks
AI architecture from day one
Data collection strategy included
Custom model training
Full product build + AI integration
Scope AI Product →
START HERE

AI Readiness Consultation

Not sure if AI is right for your product or how to approach it? A structured 3-day consultation to assess your data, your use cases, and give you an honest AI roadmap — without committing to a full build.

Timeline: 3 days
Data readiness assessment
AI use case prioritization
Build vs buy analysis
Honest investment estimate
Book Consultation →
⟶ The conversation is free. The honesty is guaranteed.

What AI ProblemAre You Trying to Solve?

Describe it to us. We will tell you honestly whether AI is the right tool, what it will cost, and how long it will realistically take. No hype. No overselling. Real answers.