What we build
LLM applications
Assistants, copilots and document workflows wired to your data and systems.
RAG systems
Grounded answers with citations, permissions and automatic reindexing.
AI agents
Autonomous agents that plan, call tools and complete multi-step tasks.
Content & analysis workflows
Summarization, extraction, classification and generation at scale.
Our approach
- Pick the right model and technique — prompting, RAG or fine-tuning
- Ground outputs in your data with citations and guardrails
- Instrument evaluation, cost and quality from the first PoC
- Add human-in-the-loop controls for sensitive actions
- Deploy on AWS with observability and continuous improvement
Evidence in production
Frequently asked questions
What is generative AI development?
Building applications on large language models that generate text, answers, summaries or actions — grounded in your data and engineered for production, not just demos.
What kind of applications do you build?
AI assistants, document analysis, RAG knowledge systems, AI agents with tool access, and content generation workflows integrated with your systems.
How do you keep outputs accurate?
We ground outputs in your data with RAG and citations, add guardrails, and run evaluation suites that measure quality on real business cases before and after every change.
Can you integrate with our existing stack?
Yes. We connect to CRM, ERP, databases, APIs, WhatsApp and marketplaces through APIs and the Model Context Protocol (MCP).
How fast can we go to production?
We start with a focused proof of concept on the riskiest part, then iterate to an MVP with guardrails and observability — typically weeks, not quarters.
Related
Build a generative AI product that works
Tell us the outcome you need. We'll propose the model, architecture and a PoC that de-risks the build.
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