LLM feature integration
OpenAI, Claude, Gemini, Llama, and Gemma — chosen per use case, not by default.
AI Development Company
AI & Agent Engineering
We build AI that ships. Not slideware, not endless POCs — production LLM features, multi-agent systems, and RAG pipelines running inside real products with real users.
Our AI engineers work inside the same teams as our designers and full-stack developers, which is why our AI features actually make it into the product instead of dying in a demo.
- Agents
- CrewAI
- LangGraph
- AutoGen
- LangChain
- RAG
- LLM
- OpenAI
- Claude
- Gemini
- Llama
- Pinecone
- PgVector
- Hugging Face
CAPABILITIES
What we build
Multi-agent systems
Agentic workflows built with CrewAI, AutoGen, and LangGraph, with tool calling and memory.
Retrieval-augmented generation
RAG over your documents and data with LangChain, Pinecone, PgVector, and Chroma.
Speech and vision
Whisper and Stable Diffusion where the product needs to hear, see, or generate.
Evaluation and guardrails
Model evaluation, prompt regression testing, and hallucination checks before release.
Cost and latency optimization
Model routing, caching, and monitoring so AI stays inside your unit economics.
AI SERVICES
Every part of the Intelligence Layer
Go deeper into each AI service.
AI Agent Development
Single and multi-agent systems with tool calling, memory, and human approval steps, built on CrewAI, LangGraph, and AutoGen.
Learn moreRAG Development
RAG pipelines over your documents and data: ingestion, embeddings, vector search, re-ranking, and answer evaluation.
Learn moreGenerative AI Development
Copilots, assistants, and multimodal features for text, image, and voice, integrated into your product.
Learn moreLLM Development
Model selection, prompt engineering, fine-tuning, guardrails, and monitoring for LLM-powered products.
Learn moreAI Automation
AI workflows for support, sales, documents, and operations — with human approval where it matters.
Learn moreAI Chatbot Development
Chatbots grounded in your data and connected to your systems, with clean handoff to your team.
Learn moreTOOLS
The AI stack we work in
Chosen per use case, not by default.
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OpenAI
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Claude AI
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Gemini
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Llama
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Gemma
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LangChain
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LangGraph
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CrewAI
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AutoGen
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Hugging Face
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Pinecone
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PgVector
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Chroma
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Whisper
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Stable Diffusion
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PyTorch
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TensorFlow
OUR APPROACH
Our AI-native process
Seven steps, one team. Intelligence is scoped at the start and evaluated at the end.
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01
Discovery & AI Opportunity Mapping
We start every engagement by mapping where intelligence belongs in your product: which workflows an agent can own, where an LLM adds real value, and where plain software is the right answer. You get an honest AI scope — including what not to build.
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02
UI/UX Design
Research, flows, wireframes, and high-fidelity interfaces, validated with users before development begins. For AI features, we design the states that matter: streaming, uncertainty, and human-in-the-loop control.
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03
Intelligence Engineering
Model selection, prompt and agent architecture, RAG pipeline design, and evaluation harnesses — built with LangChain, LangGraph, CrewAI, and the vector store that fits your data.
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04
Web Development
Frontend and backend engineering on React, Next.js, Node, and Python, delivered in short sprints with working software at the end of each one.
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05
Mobile Development
Native and cross-platform apps for iOS and Android, built against the same APIs and design system as the web product.
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06
Cloud & Deployment
Infrastructure as code, CI/CD, and monitored releases on AWS, Azure, or GCP — including GPU/inference infrastructure and model-cost monitoring where AI workloads demand it.
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07
Testing & AI Evals
Automated test suites cover every release. AI features get their own evaluation suites — regression tests for prompts, hallucination checks, and output-quality scoring — so intelligence ships with the same rigor as code.
FAQ
AI & Agent Engineering — common questions
We already have a dev team — can you add just the AI layer?
Yes. Our AI & Agent Engineering team regularly integrates with in-house teams, delivering the Intelligence Layer while your team owns the product.
Which model providers do you work with?
OpenAI, Anthropic (Claude), and Google (Gemini), plus open-source models such as Llama and Gemma when privacy, cost, or latency favors self-hosting. We recommend per use case.
How do you keep our data safe?
Private deployments, no-training API agreements, on-prem vector stores, and compliance-aware architecture for regulated industries.
How do you test AI features?
Each AI feature gets an evaluation suite: prompt regression tests, hallucination checks, and output-quality scoring that run before every release.
Ready to build AI-native?
Tell us what you're building — we'll show you where AI belongs in it.