AI Application Development
Build production-grade AI agents, RAG systems, and semantic tools that integrate safely with your data infrastructure.
AI that works in a demo, but fails in production
Most AI features look magic in a Figma mockup or command-line script, but crumble when exposed to real-world edge cases. Latency spikes, prompt regressions, and runaway token bills often kill AI initiatives before they can deliver return on investment.
- Unoptimized contexts that run up massive OpenAI, Anthropic, or Gemini API bills.
- Hallucinations that break system schemas or surface incorrect calculations to users.
- RAG pipelines that retrieve irrelevant document chunks, producing poor quality outputs.
AI engineered with verification guardrails
We don't build toys. We build reliable, self-correcting AI systems that use semantic caching to lower cost, and structured output schemas to prevent failures. Our engineering process treats prompts as code, running evaluations against target test sets.
- Rigid evaluation pipelines to measure prompt output quality quantitatively.
- Semantic query caching reducing recurring API costs by 30% to 50%.
- Advanced hybrid-search indexing (pgvector, Pinecone, or Qdrant) for high-precision RAG.
Designed for direct business impact
Sub-200ms Caching
Serve frequent user queries instantly from local vector databases, avoiding the latency of third-party API roundtrips.
Cost Management
Compress prompts, optimize context windows, and dynamically route tasks to cost-efficient open-source or mini models.
Rigid Guardrails
Wrap completions in strict parser schemas to guarantee that returned responses conform 100% to your database types.
How we ship your software
Feasibility & Cost Estimation
We analyze your proprietary data sources, identify target models, and calculate expected API costs before writing code.
Prompt Engineering & RAG Design
We write system prompts, set up secure data parsing pipelines (PDFs, DBs), and configure semantic search vector indexers.
Prompt Engineering & RAG Design
We write system prompts, set up secure data parsing pipelines (PDFs, DBs), and configure semantic search vector indexers.
Integration & Caching Setup
We integrate the AI pathways into your App Router server actions, deploy caching layers, and implement model fallback logic.
Continuous Evaluation
We run bulk tests to verify prompt performance and latency benchmarks under high concurrent user simulations.
Continuous Evaluation
We run bulk tests to verify prompt performance and latency benchmarks under high concurrent user simulations.
Frequently Asked Questions
Ready to construct your vision?
Get in touch for an honest consultation about your systems architecture, timelines, and budgets.
Build your AI product