Custom AI software development
AIthatworksinproduction,notjustinapitchdeck
Most enterprises are sitting on a goldmine of unstructured data and repetitive processes that AI can transform. Smoking Chimney's AI engineers turn that potential into working software — faster, cheaper, and more reliably than building an in-house AI team.
What we build
EnterpriseAIcapabilitiesacrosseveryusecase
LLM Integration Services
Embed GPT-4o, Claude, Gemini, Mistral, and open-source LLMs into your products with robust prompt engineering, RAG pipelines, and context-aware response systems.
- RAG pipeline architecture
- Prompt engineering & fine-tuning
- Multi-model orchestration
- LLM cost optimisation
AI Agent Development
Design and deploy autonomous AI agents that plan, reason, and execute multi-step tasks — from research agents to code-generating copilots and workflow automation bots.
- Multi-agent frameworks (LangGraph, CrewAI)
- Tool-use & function calling
- Memory & state management
- Human-in-the-loop guardrails
Document AI & OCR
Transform unstructured documents — PDFs, scanned forms, contracts, invoices — into structured, actionable data using AI-powered extraction and classification.
- Intelligent document processing
- Multi-format OCR pipelines
- Entity extraction & NER
- Document classification
Generative AI Development
Build product features powered by generative AI — from AI-written content and image generation to synthetic data creation and creative tool suites.
- Text & image generation APIs
- Fine-tuned domain-specific models
- Synthetic data pipelines
- AI content moderation
AI Workflow Automation
Replace manual, repetitive knowledge-work with intelligent automation — from email triage and report generation to complex multi-system orchestration.
- Business process automation
- Intelligent data routing
- AI-triggered webhooks
- Human escalation workflows
ML & Predictive Analytics
Custom machine learning models — trained on your data — that forecast demand, detect anomalies, score risk, and surface insights your team can act on.
- Supervised & unsupervised learning
- Time-series forecasting
- Anomaly detection
- Model explainability (XAI)
Why Smoking Chimney
TheAIdevelopmentpartnerthatships,notjustdemos
Research-to-production AI
We bridge the gap between AI research and production engineering — our team includes ML engineers, MLOps specialists, and AI product designers who build systems that work in the real world, not just in notebooks.
AI architecture that scales
We design AI systems with observability, latency budgets, and cost models in mind from day one — so your AI features don't bankrupt you as you scale.
Responsible AI by default
Bias testing, hallucination mitigation, PII redaction, and output safety filters are standard in every AI system we ship — because irresponsible AI is a liability, not a feature.
Domain-specific fine-tuning
Off-the-shelf models aren't always enough. We fine-tune LLMs and vision models on your proprietary data to achieve accuracy levels that generic models can't match in your domain.
Our AI development process
FromrawdatatoproductionAIinweeks,notyears
AI opportunity assessment
We audit your existing processes, data assets, and tech stack to identify the highest-ROI AI use cases — and the ones that sound exciting but won't deliver value.
Data readiness & pipeline design
AI is only as good as its training data. We assess data quality, design collection and labelling pipelines, and build the vector stores and feature stores your AI models need.
Model selection & prototyping
We rapidly prototype with the best-fit model architectures — fine-tuning vs. RAG, open-source vs. API-based — and benchmark performance against your acceptance criteria.
Production engineering & MLOps
We productionise models with serving infrastructure, monitoring, A/B testing frameworks, and automated retraining pipelines so your AI gets smarter over time.
Integration & user experience
AI features are embedded into your product's UX with thoughtful human-in-the-loop design — ensuring AI augments your users rather than frustrating them.
Monitoring, evaluation & iteration
We set up LLM evaluation frameworks, output quality monitoring, and feedback loops so you always know how your AI is performing and when to improve it.
Common questions
YourAIdevelopmentquestions,honestlyanswered
We work across the full ecosystem — OpenAI (GPT-4o, o1), Anthropic (Claude), Google (Gemini), Meta (LLaMA), Mistral, and open-source models via Hugging Face. For frameworks, we use LangChain, LangGraph, CrewAI, LlamaIndex, and custom orchestration layers depending on requirements.
We use a combination of RAG (retrieval-augmented generation) to ground LLM outputs in your data, structured output parsing, output validation layers, confidence scoring, and human-review escalation for low-confidence responses.
Yes. We support fully on-premises AI deployments using open-source LLMs (LLaMA, Mistral, Phi) on your own cloud infrastructure — ensuring your proprietary data never touches a third-party API.
A focused AI feature (e.g., document extraction, Q&A bot) typically takes 4–8 weeks to production. Complex AI agent systems or full ML pipelines take 10–20 weeks depending on data readiness and integration complexity.
Yes. We offer ongoing MLOps retainers that include model performance monitoring, drift detection, periodic retraining on new data, and prompt tuning as your use cases evolve.
Explore what else we build
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Talk with our decision makers
The team AI-powered wonder elevates mundane tasks into the astounding.
Whether Hollywood-worthy stories to profound user experiences, we're the
first place to look for unprecedented changes in the digital realm.
hi@smokingchimneystudios.com
Professional, accessible, and secure.
+91 77219 12611
Dedicated, assist, and quick.
PCMC - Pune, India - 411033
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