AI DEVELOPMENT
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Intelligent App
Architectures
We build custom web and mobile applications with integrated AI components, such as predictive analytics, intelligent search, and autonomous data handling.
Full-Stack AI
Integrated LLM endpoints and vector databases for smart data retrieval.
Scalable Ops
Cloud architectures designed for heavy AI processing and low latency.
AI-Powered Product, Built Fast
AI-Native Platform Launched in 8 Weeks
A fintech startup needed a semantic search platform over 10M+ financial documents. We architected a full-stack solution with a vector database, LLM query engine, and real-time dashboard — from design to deployment in 8 weeks. User retention was 40% above industry benchmark at launch.
8wk
Design to Launch
10M+
Documents Indexed
+40%
Retention vs. Benchmark
AI Development FAQ
Which AI models and frameworks do you use?
We are model-agnostic and choose the best tool for each use case — including OpenAI, Anthropic, Mistral, and open-source alternatives. On the infrastructure side we use Pinecone, Supabase, Vercel, and AWS depending on scale requirements.
Do you handle maintenance after launch?
Yes. We offer ongoing retainer arrangements covering model updates, performance monitoring, feature iterations, and infrastructure scaling as your user base grows.
Can you integrate AI into our existing app?
Absolutely. We frequently embed AI capabilities into existing products via API integrations, without requiring a full rebuild. We audit your stack first and recommend the lowest-friction path.
How We Built an AI-Powered
Internal Tool That Saved $180K/Year
A logistics company was using a 10-year-old internal system that required 3 FTEs to manually process shipment data, generate reports, and answer internal queries from ops teams.
We rebuilt their internal tool as a modern AI-native web app: LLM-powered natural language querying over their data, automated report generation, and an AI operations assistant embedded directly into their ops workflow.
The result: 2 of the 3 FTE roles were reassigned to higher-value work, report generation time dropped from 4 hours to 45 seconds, and the company saved an estimated $180K annually in operational overhead.
Discuss Your Project$180K
Annual Savings
45s
Report Generation
2 FTE
Roles Reassigned
14wk
Build Timeline
AI Development — FAQ
What tech stack do you build with?
We typically build with React or Next.js on the frontend, Node.js or Python on the backend, and integrate OpenAI, Anthropic, or open-source LLMs depending on your data privacy requirements.
Do you handle hosting and infrastructure?
Yes. We provision and manage cloud infrastructure on AWS, GCP, or Vercel. We can also deploy to your existing cloud accounts if preferred — you always own the infrastructure.
How do you handle data privacy for AI features?
We architect AI components so sensitive data never leaves your environment. We use private model deployments, encrypted vector stores, and self-hosted LLMs where compliance requires it.
What does post-launch support look like?
All builds include a 30-day post-launch support window. After that, we offer monthly retainer packages for ongoing feature development, model updates, and performance monitoring.