AI INTEGRATION

AI Integration Services

Gemini API · Stable Diffusion · ControlNet · LLM Apps · RAG Systems

We embed AI into real products — not demo projects. From multimodal crop diagnosis (Gemini Vision + Flutter) to AI interior renders (Stable Diffusion + ControlNet) to intelligent document analysis — we have shipped AI integrations that are live in production and used daily by real users.

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What We've Shipped

  • ✓ FasalVision — Gemini multimodal farming AI, 9 languages
  • ✓ Proptifi — Stable Diffusion + ControlNet interior redesign
  • ✓ TradeGuardian — AI trading signal pipeline
  • ✓ Custom chatbots — RAG over client documents
  • ✓ Image generation APIs — production-grade GPU pipelines

What We Build

Every integration we've shipped is in production. No toy demos.

Gemini API Integration

We integrate Google's Gemini API into your web or mobile app — text generation, multimodal (image + text), function calling, and streaming responses. We've shipped Gemini-powered features into Flutter apps used by farmers across 9 countries. Preferred over GPT-4 for cost efficiency at Indian user volumes.

Use cases: content generation, document Q&A, image analysis, multilingual AI assistants.

Stable Diffusion + ControlNet

We deploy and customise Stable Diffusion pipelines — inpainting, img2img, ControlNet for structure-preserving generation, custom LoRA fine-tuning, and ESRGAN upscaling. We've shipped a production system generating interior design renders at under ₹2 per image for Proptifi.

Use cases: property visualisation, fashion try-on, product staging, architectural renders.

RAG Systems (Retrieval-Augmented Generation)

We build document intelligence products — upload a PDF, query it with natural language, get precise answers with source citations. Built on LangChain or LlamaIndex, with vector databases (Chroma, Pinecone, pgvector), and your choice of LLM backend (Gemini, Claude, or local Mistral for data-sensitive clients).

Use cases: legal document Q&A, policy chatbots, internal knowledge bases.

LLM-powered Web & Mobile Features

We embed LLM features into existing Laravel or Flutter apps — AI-drafted content, smart search (semantic rather than keyword), form auto-fill from uploaded documents, and automated report generation. We specialise in the engineering work, not just API calls: streaming responses, cost optimisation, rate-limit handling, fallback logic.

AI Signal & Analytics Pipelines

We build data pipelines that transform raw inputs (market data, sensor readings, user behaviour events) into AI-generated insights surfaced in your dashboard. TradeGuardian's real-time trading signal engine is an example — raw tick data in, actionable buy/sell signals out, displayed live via WebSockets.

GPU Inference Infrastructure

Running image generation or large models at production scale requires more than an API call. We design cost-optimised GPU inference pipelines — spot instances (RunPod, Lambda Labs), batching, queue workers, and automatic scale-down. We've reduced image generation costs by 80% versus naive per-request GPU allocation for clients who came to us after their cloud bill exploded.

Our AI Technology Stack

Language Models
Gemini 1.5 Flash / ProPrimary LLM (cost + multimodal)
Claude Haiku / SonnetLong-context document tasks
Mistral 7B / 8x7BOn-premise, data-sensitive projects
LangChain / LlamaIndexRAG orchestration
Pinecone / pgvectorVector database
Image Generation
Stable Diffusion XLBase image generation
ControlNetStructure-preserving generation
ComfyUIPipeline orchestration
ESRGANAI upscaling to print quality
RunPod / Lambda LabsCost-optimised GPU inference

See AI Integration in Action

FasalVision, Proptifi, and TradeGuardian are all live products we built — not concepts. View them in the portfolio to see what production AI integration actually looks like.

View Portfolio Proptifi Case Study FasalVision Case Study

AI Integration — Common Questions

It depends on the task. For text generation and document Q&A at Indian user volumes, Gemini 1.5 Flash gives the best cost-per-token. For image generation with structure control, Stable Diffusion + ControlNet. For long legal or financial documents, Claude's 200K context window is worth the cost. We'll recommend the right model after understanding your use case — book a free consultation.

Yes — most of our AI work is adding AI features to existing applications, not starting fresh. We audit your codebase, identify where AI adds genuine value (not where it's technically interesting but doesn't help users), and integrate it with minimal disruption to your existing architecture.

A focused AI feature addition (e.g., Gemini-powered chat in an existing app) typically takes 2–4 weeks. A full AI product (image generation pipeline, RAG system) is 8–16 weeks. API costs at early-stage volume are usually negligible — Gemini charges fractions of a rupee per query. Contact us with your use case for a fixed-price estimate.

Google's Gemini API (when accessed via API key, not Google AI Studio) does not use your queries to train models by default. For clients with strict data requirements, we deploy local models (Mistral, LLaMA) on your own infrastructure — no data leaves your servers. We design the architecture to match your compliance posture.