AI Product Development & Applied AI
Build your AI product with a senior team. Get a plan, timeline, and a working prototype in weeks.
We build AI-native products that behave predictably and scale. Not another chatbot. Real systems that combine orchestration, retrieval, guardrails, evals, and human review loops. Our team blends product design, engineering, and applied AI so the output feels intentional, reliable, and fast.
What we deliver
We’re here to make great products with great people.
Why this matters for your team
How we approach it
Engagement models
Working inside your roadmap.
Owning a full product or subsystem.
Architecture, performance & system stabilization.
Why teams choose us
We build AI that works inside your workflows, not chatbots that spit out disconnected blurbs. Our focus is on simplifying the actual tasks your team deals with every day. That means structuring messy data, reducing manual steps, and producing outputs that feel reliable instead of random. The intelligence sits inside the workflow, not on top of it, so the product feels clearer and easier to use.
What makes this powerful is the combination of tailored workflows, meaningful data, and UX that makes the AI feel invisible. When the system understands the job and the context, it becomes hard to replace and genuinely useful. Instead of one-off features, you get a tool built around your process, your information, and the way your team works. That’s where AI creates real value.
Problems we solve
- → Many teams start without a clear AI strategy, which leads to features that behave inconsistently and don’t hold up under real use.
- → Data is usually unstructured or incomplete, so internal tools end up relying on manual work instead of automation.
- → Prototypes also move slowly and often never reach a stable, production-ready state.
FAQ
- Python, Node, TypeScript
- LangChain, LlamaIndex, Instructor
- OpenAI, Anthropic, local models, fine-tuned LLMs
- Vector DBs like Pinecone, Weaviate, Qdrant
- Retrieval pipelines, structured outputs, eval frameworks
- AWS, GCP, Azure
- React, Next.js, React Native
It combines retrieval, structured outputs, evals, decision logic, and fallbacks instead of a single prompt or model call. The system is predictable and built for scale.
Most teams see a working slice in 3 to 6 weeks depending on complexity, integrations, and data quality.
Yes. Many clients start with no AI roadmap. We run a short discovery sprint to find the highest-value jobs and automation opportunities.
We slot in easily. Shared standups, shared repo, clear ownership. No overhead.
We build with audit logs, deterministic steps, PII handling rules, and model isolation. You get transparency and traceability.
We rescope quickly. Unused budget rolls forward and we update artifacts so the project stays aligned.
Yes. Most clients join our monthly care plan for monitoring, updates, data cleanup, and small improvements.