Generative AI applications
Standalone AI products taken from concept to launch.
AI & Agent Engineering
Generative AI Development Company
Generative AI development means building products that create — text, images, audio, code, and structured data — on top of foundation models.
We turn that capability into product features people rely on: copilots inside your app, assistants for your customers, and content pipelines that keep a human in control.
- OpenAI
- Claude
- Gemini
- Stable Diffusion
- Whisper
- Copilots
- Multimodal
CAPABILITIES
What we build
AI copilots
In-product assistants that understand the user's context and data.
Multimodal AI
Text, image, speech, and vision combined in one experience.
Content generation
Brand-safe generation pipelines with review and approval steps.
AI integrations
Generative features added to the systems your team already uses.
Enterprise generative AI
Private deployments, governance, and usage controls.
TOOLS
The AI stack we work in
Chosen per use case, not by default.
-
OpenAI
-
Claude AI
-
Gemini
-
Llama
-
Gemma
-
LangChain
-
LangGraph
-
CrewAI
-
AutoGen
-
Hugging Face
-
Pinecone
-
PgVector
-
Chroma
-
Whisper
-
Stable Diffusion
-
PyTorch
-
TensorFlow
OUR APPROACH
Our AI-native process
Seven steps, one team. Intelligence is scoped at the start and evaluated at the end.
-
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.
-
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.
-
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.
-
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.
-
05
Mobile Development
Native and cross-platform apps for iOS and Android, built against the same APIs and design system as the web product.
-
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.
-
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
Generative AI Development — 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.