Engineering

AI Engineering

We design, build, and deploy production-ready AI agents, semantic retrieval pipelines (RAG), and custom LLMs tailored to your business data.

45% Avg. reduction in user search latency
10M+ Vector database tokens managed

Functional AI that drives business value

Too many companies build AI prototypes that never survive deployment. We focus on production-ready AI engineering. That means building applications with robust caching, cost optimization, rate limiting, and exact testing evaluations so your agents perform reliably at scale.

We work with modern vector databases (Pinecone, pgvector), cloud APIs (OpenAI, Anthropic, Gemini), and custom open-source models (Llama, Mistral) to deliver optimal performance/cost ratios.

Semantic Search & RAG

Connect unstructured documents, PDFs, and databases directly to conversational LLMs with exact relevance matching.

Custom Agents

Autonomous agentic workflows that perform multi-step tasks, schedule calendars, write database records, or query inventory.

Fine-Tuning

Train smaller models on custom customer support logs or industry codebases to lower API costs by up to 80%.

Evaluation Pipelines

Implement automatic unit-testing frameworks for prompts and outputs to prevent regression or hallucinations.

AI tools we build with

We remain vendor-agnostic, choosing the exact model, database, and pipeline combination that matches your latency, cost, and security guidelines.

Models

GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, Llama 3.1, Mistral Large.

Vector DBs

Pinecone, pgvector, Qdrant, Milvus, Weaviate.

Frameworks

LangChain, LlamaIndex, LangGraph, DSPy, Hugging Face.

Infrastructure

GCP Vertex AI, AWS Bedrock, RunPod, LangSmith, Braintrust.

AI Readiness Blueprint

We guide your organization through a structured approach to transition AI from research spikes to production operations.

Phase 1: Data Audit & Curation +

We audit your existing text, PDF, and SQL logs, mapping out the security policies and scrubbing sensitive PII data before indexing.

Phase 2: RAG Pipeline Design +

We create optimal embedding chunks, set up vector databases, and implement semantic search to fetch relevant context under 80ms.

Phase 3: Testing & Cost Optimizations +

We set up automatic prompt evaluations to ensure accuracy, and integrate Redis caching to prevent repeat API calls, reducing costs by up to 60%.

Featured Success

How Understood launched their AI companion app

vector software inc designed and built a production-ready conversational chat assistant for Understood.org, helping parents of neurodivergent children retrieve trusted advice instantly from millions of records.

Read Understood Case Study