AI Engineering
AI-powered product engineering using OpenAI, Anthropic, LangChain, and custom ML pipelines. LLM integrations, intelligent automation systems, and AI-native SaaS products for the next wave of software.
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LLM integration with OpenAI GPT-4, Anthropic Claude, and Gemini
RAG (Retrieval-Augmented Generation) pipeline architecture
Vector database setup with Pinecone, Weaviate, or pgvector
AI agent systems with LangChain and LangGraph
Prompt engineering and LLM output optimization
Fine-tuning pipelines for domain-specific models
AI feature integration into existing SaaS products
Responsible AI implementation with guardrails and monitoring
1
Discovery & Architecture
Deep-dive into your requirements, user flows, and technical constraints to define the right architecture.
2
Design System & Prototyping
High-fidelity prototypes validated against your goals before a single line of production code is written.
3
Iterative Engineering
Bi-weekly sprint cadence with real deployments, demos, and continuous feedback loops.
4
Quality & Performance
Automated testing, performance audits, security reviews, and accessibility passes on every release.
5
Launch & Handoff
Production deployment, documentation, knowledge transfer, and post-launch monitoring support.
AI-Powered SaaS Features
Add intelligent automation, content generation, or data analysis features to existing SaaS products.
Internal AI Tools
Build AI-powered internal tools for customer support, sales, operations, or content workflows.
AI-Native Product Builds
Build new software products where AI is the core differentiator — intelligent agents, automation platforms, or AI copilots.
Document Intelligence Platforms
Build systems that extract, analyze, and act on unstructured document data using LLMs and OCR.
OpenAI GPT-4o / Anthropic Claude
LangChain / LangGraph
Pinecone / pgvector
Python / FastAPI
Next.js frontend
PostgreSQL
AWS Bedrock
Whisper / ElevenLabs