Hire AI & ML engineers
Custom AI solutions, LLM integrations, RAG pipelines, and intelligent automation built by senior engineers from Bangladesh. Production AI, not proof-of-concept demos.
Sound familiar?
- •Your team built an AI proof-of-concept that impressed stakeholders, but nobody knows how to turn it into a production service that handles real traffic and edge cases
- •You're paying $50K+/month for OpenAI API calls because nobody optimized prompts, implemented caching, or evaluated whether a smaller model would work just as well
- •RAG search returns irrelevant results because the chunking strategy is wrong, embeddings aren't tuned, and there's no reranking or hybrid search
- •Every AI feature request gets blocked because your web developers don't understand vector databases, prompt engineering, or model serving patterns
What our AI engineers deliver
Engineers who ship AI features to production - not just notebooks that work on sample data.
LLM integration & optimization
OpenAI, Anthropic, and open-source model integrations with proper prompt engineering, response caching, fallback chains, and cost monitoring. We optimize for quality and cost - not just 'it works with GPT-4'.
RAG pipeline engineering
Document ingestion, intelligent chunking, embedding generation, vector storage with Pinecone or Weaviate, hybrid search with BM25, and reranking. RAG systems that actually return relevant answers, not just similar text.
AI-powered chatbots & copilots
Conversational AI with memory, tool-use, and domain-specific knowledge. Customer support bots, internal knowledge assistants, and code copilots that understand your codebase and documentation.
AI workflow automation
Multi-step AI pipelines that extract data from documents, classify content, generate summaries, and trigger actions. Structured output parsing, validation, and human-in-the-loop approval flows.
AI safety & guardrails
Input validation, output filtering, PII detection, prompt injection prevention, and content moderation. Production AI systems need safety layers - we build them from day one.
MLOps & model deployment
Model versioning, A/B testing, performance monitoring, and automated retraining pipelines. Deploy models behind production APIs with proper latency tracking, error handling, and fallback strategies.
What teams build with us
AI-powered chatbots & copilots
Customer support agents that resolve tickets, internal knowledge assistants that answer employee questions, and domain-specific copilots that help users navigate complex workflows. Built with proper conversation memory and tool-use capabilities.
Document intelligence & search
Extract structured data from PDFs, contracts, and invoices. Semantic search across thousands of documents. Question-answering systems that cite their sources and handle ambiguous queries gracefully.
Content generation pipelines
Product descriptions, marketing copy, email personalization, and report generation. AI content systems with brand voice consistency, fact-checking, and human review workflows.
Recommendation engines
Product recommendations, content personalization, and user matching. Hybrid approaches combining collaborative filtering with LLM-powered understanding of user intent and item semantics.
The AI stack our engineers use
Production-tested tools for building AI-powered applications.
OpenAI / Anthropic
LLM providers
LangChain
LLM orchestration
Pinecone
Vector database
FastAPI
Model serving
Python
Core language
PyTorch
ML framework
Weaviate
Hybrid search
AWS SageMaker
MLOps
Frequently asked questions
Can you work with our existing AI models?
How do you handle AI costs?
Do you build custom models or just use APIs?
How do you ensure AI output quality?
Ready to build with AI?
Tell us about your AI project and we'll match you with senior ML engineers within a week.