Model selection
Hosted or open-source models compared on quality, cost, latency, and privacy.
AI & Agent Engineering
LLM Application Development
LLM application development is the engineering around the model: choosing it, prompting it, grounding it, testing it, and running it at a sustainable cost.
We treat prompts and model choices as code — versioned, evaluated, and monitored — so quality does not drift after launch.
- OpenAI
- Claude
- Gemini
- Llama
- Gemma
- Fine-tuning
- Guardrails
- Evals
CAPABILITIES
What we build
Prompt engineering
Structured prompts and tool schemas, versioned and regression-tested.
Fine-tuning
Applied only when it beats RAG and prompting on your own evaluation set.
Tool calling
Reliable function calling and structured output for downstream systems.
Guardrails
Input and output checks, policy filters, and safe fallbacks.
Monitoring and deployment
Tracing, quality scoring, and cost tracking in production.
TOOLS
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
LLM 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.