AI Products

Not 'AI that looks smart' — AI that actually gets used

What matters is whether LLMs and agents make a real dent in your product or operations. We build AI features that hold up in production, not just in demos.

01

Overview

Calling a model is easy. Making it into a feature people trust is something else. We design prompts, retrieval-augmented generation, agent orchestration and evaluation pipelines — end to end.

02

Tech Stack

AreaTools
ModelsOpenAI · Anthropic Claude · Gemini · Open-source LLMs
FrameworkLangChain · LlamaIndex · Vercel AI SDK
Vector / Datapgvector · Pinecone · Weaviate · Supabase
OpsPrompt evals · LangSmith · Guardrails · Logging & monitoring

03

Our Approach

  1. 01

    Define the use case

    We don't start from 'what can AI do' — we start from 'what problem could AI solve better'.

  2. 02

    Data & RAG design

    We design the data ingestion, preprocessing and retrieval strategy that makes answers trustworthy.

  3. 03

    Build the agent

    Tool use, multi-step reasoning and human-in-the-loop — all wrapped in a reliable agent.

  4. 04

    Evaluate & operate

    Automated eval sets and production logs let us measure quality and keep improving it.

04

Case Studies

  • Marketing AI

    Hoost.ai

    LLM-driven marketing automation — ad copy, target segments and A/B testing, all automated.

  • Education

    TenX

    Multimodal problem recognition with Socratic, step-by-step hint generation.

  • RAG

    Internal knowledge bot

    RAG-based assistant trained on internal docs — reduces repetitive questions and speeds up onboarding.

Want to talk it through?

Tell us what you're building. We'll help design the right structure together — even a small idea is a good place to start.

Let's Talk