Practical AI features built for your software application.
We design and build AI integrations, vector database search indexes, and automation agents for custom client software.
Problems We Neutralize
Generic wrappers with no context
Standard AI prompts with no context return generic answers. For projects requiring domain context, we build Retrieval-Augmented Generation (RAG) pipelines that connect your internal data.
Slow inference blocking the user thread
AI generation takes seconds. If run synchronously, it freezes the page. Where real-time output is needed, we implement streaming server connections to display tokens as generated.
High API overhead and token waste
Poor token pruning drains budget. On client builds, we implement context payload compression and input pruning before API requests.
Target Deliverables
Architecture specifications covering:
- Retrieval-Augmented Generation (RAG) — database search indexing built to feed domain context into prompt runs.
- Vector Embeddings Storage — database configuration (such as pgvector) to enable semantic search.
- Streaming Server Responses — Server-Sent Events (SSE) integration to stream model responses live to the browser.
- Data-Cleaning Automation — custom background scripts to preprocess documents and text before prompt delivery.
Technology Choices & Rationale
| Technology | Architectural Rationale |
|---|---|
| pgvector | Enables vector comparison queries directly inside PostgreSQL database architectures. |
| Vercel AI SDK | Provides clean interface utilities to stream and coordinate model payloads. |
| OpenAI / Anthropic | Connects hosted inference APIs from OpenAI and Anthropic for structured data parsing. |
Frequently Asked Questions
What is RAG (Retrieval-Augmented Generation)?
RAG is a technique where we search your internal databases or documents for relevant information first, and then inject that specific context into the prompt, grounding the AI output in your data.
How do you protect data privacy during prompt queries?
We configure pipelines to exclude sensitive PII data before token submission, and we suggest enterprise APIs that guarantee data is not used for model training.
Ready to build?
Contact us to discuss your project requirements, API architecture, and database integrations.