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We integrate LLMs, RAG pipelines, and agentic workflows into your existing product — from proof-of-concept to production. Senior AI engineers, dedicated to your codebase.
Connect your product to GPT-4, Claude, Gemini, Llama, or Mistral. We handle authentication, prompt engineering, context management, and cost optimization.
Retrieval-Augmented Generation over your proprietary data — document ingestion, vector embeddings, semantic search, and grounded responses with citations.
Multi-step AI agents that reason, plan, and take actions. Tool use, function calling, and orchestrated pipelines using LangChain, LangGraph, or custom frameworks.
Train domain-specific models on your data. Classification, extraction, summarization, and generation tasks tailored to your industry and terminology.
Smart recommendations, intelligent search, auto-categorization, sentiment analysis, and content generation — embedded directly into your product.
Production ML infrastructure — model versioning, A/B testing, drift detection, latency monitoring, and cost tracking. AI in production, not just in demos.
Automated loan assessment and fraud detection using custom ML models trained on transaction history.
Extract structured data from unstructured clinical notes using fine-tuned LLMs with HIPAA-compliant architecture.
Real-time product recommendations and dynamic pricing using collaborative filtering and LLM-powered search.
In-app AI assistant that understands your product's data model and generates contextual insights for users.
Clause extraction, risk flagging, and contract comparison using RAG over proprietary legal databases.
Personalized learning paths and AI tutoring using student performance data and instructional content RAG.
We audit your data, infrastructure, and use cases to define the highest-ROI AI integration opportunities.
We choose the right models, vector stores, and orchestration frameworks for your specific requirements and budget.
A working proof-of-concept in 1–2 weeks. Real data, real latency, real cost — validated before full build.
Full integration into your product with auth, rate limiting, cost controls, monitoring, and error handling.
Continuous benchmarking against quality, latency, and cost metrics. We tune until production is predictable.
Model monitoring, prompt versioning, drift alerting, and continuous improvement — managed by your pod.
We don't build demos. Every integration is designed for scale, observability, and cost control from day one.
We don't push you toward any one model. We benchmark GPT-4, Claude, Gemini, and open-source against your specific task.
Your AI engineers are part of your dedicated pod — they know your architecture and ship within your deployment pipeline.
We define success metrics upfront — accuracy, latency, cost per call — and report against them. No vague AI claims.
Start with a 1-week AI readiness assessment. We'll map your highest-ROI integration opportunities, define the architecture, and prototype in the same sprint.