Large Language Models & AI APIs

AI Integration

We wire OpenAI, Anthropic Claude, Google Gemini, and open-source LLMs directly into your product — from a simple API call to full RAG pipelines, embeddings, and vector search at production scale.

LLM-Powered Features, Built to Ship

We don't just call an API — we architect the full AI layer: prompt engineering, context management, retrieval-augmented generation (RAG), streaming responses, and cost-optimized model routing. The result is a reliable AI feature your users will actually trust.

  • OpenAI / Claude / Gemini API — model selection, prompt design, and token optimization
  • RAG Pipelines — ingest your docs, embed them, retrieve relevant chunks, generate grounded answers
  • Vector Databases — Pinecone, Weaviate, pgvector — designed for semantic search at scale
  • Streaming & Real-Time UX — token-by-token streaming with WebSockets or SSE for fluid AI interfaces
  • Fine-Tuning & Custom Models — domain-adapted models for specialized tasks or brand voice
Get a Quote
rag-pipeline.ts
// 1. Embed user query
const queryVec = await embedText(userQuery);
// 2. Retrieve top-k chunks
const chunks = await vectorDB.query(queryVec, { topK: 5 });
// 3. Stream grounded response
const stream = await openai.chat.create({
model: 'gpt-4o',
stream: true,
messages: buildPrompt(chunks, userQuery)
});
→ streamed to client via SSE

Models We Work With

We're model-agnostic — we pick the right engine for your use case and budget.

OpenAI GPT-4o / o1

The gold standard for reasoning, code generation, and multimodal tasks. We handle fine-tuning, Assistants API, and function calling.

Anthropic Claude

Exceptionally long context windows and instruction-following. Our go-to for document analysis, legal summaries, and complex multi-step reasoning.

Google Gemini

Native multimodality and deep Google ecosystem integration. Ideal for apps that live inside Workspace or need vision + text tasks in one call.

Open-Source LLMs

Llama 3, Mistral, Qwen — self-hosted on your infrastructure for maximum privacy, no per-token costs, and full control over the model.

Embeddings & Vector Search

OpenAI text-embedding-3, Cohere, or local sentence-transformers — paired with Pinecone, Weaviate, or pgvector for semantic retrieval.

Vision & Multimodal

GPT-4o Vision, Gemini Vision, and DALL-E 3 for document parsing, image analysis, receipt extraction, and generative content workflows.

What We Build With AI

Common AI features we've shipped for product teams and operators.

Semantic Search & Q&A

Let users ask natural-language questions over your knowledge base, documentation, or product catalog. Powered by RAG — answers grounded in your own data, not hallucinations.

Document Processing & Extraction

Ingest PDFs, contracts, invoices, or intake forms and extract structured data at scale. Used in healthcare, legal, finance, and real estate workflows.

AI Content Generation

Generate product descriptions, marketing copy, email drafts, or report summaries — at scale, on-brand, with human-review workflows built in.

AI-Assisted Developer Tools

Code review bots, PR summarizers, SQL generation from natural language, and internal copilots that live inside your existing toolchain.

Sentiment & Classification

Classify support tickets, tag CRM notes, route leads by intent, or score NPS responses — replacing brittle rule-based systems with context-aware models.

Guardrails & Evals

Production-grade AI needs more than a prompt. We build moderation layers, output validation, eval harnesses, and hallucination detection so your AI stays safe.

Our AI Tech Stack

We pick tools based on your requirements — not hype. Our stack is battle-tested across multiple production AI deployments.

Orchestration

  • LangChain / LangGraph
  • LlamaIndex
  • Vercel AI SDK
  • Custom pipelines

Vector Stores

  • Pinecone
  • Weaviate
  • pgvector
  • Chroma (local)

Infrastructure

  • AWS / GCP / Azure
  • Vercel Edge Functions
  • Docker + GPU hosts
  • Modal / Replicate

Monitoring

  • LangSmith
  • Helicone
  • Braintrust Evals
  • Custom dashboards
RAG Pipeline Metrics
Answer Accuracy (eval set)94.2%
Avg Latency (streaming first token)380ms
Hallucination Rate1.3%
Cost vs baseline (unoptimized)-61%
Eval runs on every deployment

Ready to add AI to your product?

Book a free 30-minute scoping call. We'll assess your use case, recommend the right model, and give you a clear build plan.

Book a Free Strategy Call