AI & Generative AI

Production AI, not demos.

We build AI systems that ship, scale and stay reliable — picking the right model and pattern for each problem, not the loudest one.

What we build with AI

Capabilities that earn their place

LLM Integrations

Conversational interfaces, copilots, content generation, summarization, classification.

RAG Pipelines

Ground AI in your documents, databases, support tickets, code — with citations.

AI Agents

Multi-step workflows that plan, act and complete tasks across your systems.

Structured Extraction

Turn unstructured text, PDFs, emails, calls into clean structured data.

Voice & Multimodal

Speech-to-text, text-to-speech, image/video understanding, real-time voice agents.

Custom Model Work

Fine-tuning, evaluation harnesses, distillation, domain-specific models.

Tools & platforms

The AI stack we work across

Foundation models

ClaudeGPTGeminiLlamaMistralDeepSeekQwen

AI infrastructure

LangChainLlamaIndexHugging FacevLLMOllama

Vector databases

PineconeWeaviateQdrantpgvectorChroma

Cloud AI

AWS BedrockAzure OpenAIGoogle Vertex AI

MLOps

MLflowWeights & BiasesLangSmithHelicone
How we engineer AI

Principles, not hype

01

Model-agnostic by default

We pick what fits, not what's trendy.

02

Cost-aware architectures

Prompt caching, batching, model routing by complexity.

03

Evaluation, always

We measure quality so it improves, not drifts.

04

Privacy-first

On-prem, regional, or isolated deployments when sensitivity demands.

05

Observability from day one

Every AI action logged, traceable, debuggable.

Typical engagements

Where teams usually start with us

Add a copilot to an existing SaaS product
Build a RAG system over our internal knowledge base
Automate customer support tier-1 with AI
Move our AI prototype to production

Got an AI idea?

Let's make it real — and make it production-grade.