Why AI projects fail
Most teams get stuck at the prototype. We build the production layer — reliability, cost control, and output quality — that turns a demo into a product.
What we build
Every AI engagement includes
LLM Integration Layer
OpenAI, Anthropic Claude, Google Gemini, or open-source models — wired into your product with proper rate limiting and error handling.
RAG & Knowledge Pipelines
Vector search with pgvector or Pinecone, document ingestion, chunking strategy, and retrieval tuning tailored to your content.
AI Chatbots & Assistants
Conversational interfaces with memory, multi-turn context, tool use, and escalation to human agents when needed.
Prompt Engineering & Evals
Structured prompts, few-shot examples, output schemas, and an automated eval suite to catch regressions before they hit users.
Content Automation Pipelines
Automated content generation, summarisation, classification, and tagging — integrated with your CMS or data platform.
AI Feature Observability
Token usage dashboards, latency tracking, output quality monitoring, and cost alerts — full visibility into your AI layer.
Tools we use
Models
Frameworks
Vector DBs
Backend