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#AIFXAI

AI Integration

Ship AI features that actually fit your product, not a bolted-on chat widget.

model: custom scope & deployment · contact for quote

~/telemetry/spec
Running
surface
product_embedded_flows
retrieval_engine
hybrid_rag
inference_gate
route·cache·stream
validation
zod_structured_output
Node health0%
○ waitingmeasuring...

~/scope

6 capability modules
01#RAG

Retrieval-augmented generation

Answer over your own data with grounded sources, citations, and fresh content, no hallucinations on stale docs.

  • Chunking
  • Semantic search
  • Hybrid rerank
  • Freshness
02#VECTORS

Vector search

Semantic search across documents, products, or support tickets, set up, indexed, and tuned for relevance.

  • Pinecone
  • pgvector
  • Re-ranking
  • Evaluation
03#PRODUCT

Product workflow AI

Drop AI into existing user flows, summarization, classification, extraction, drafting, and delegating to a human at the right moments.

  • Structured output
  • Guardrails
  • Human hand-off
04#BUDGET

Cost-aware architecture

Caching, streaming, model routing, and rate limits tuned so the AI feature doesn't become a line-item surprise.

  • Caching
  • Prompt optimization
  • Model routing
05#LLMOPS

LLM Ops

Version-controlled prompts, evals before deploys, and output-quality monitoring that keeps the feature honest.

  • Prompt versioning
  • Evals
  • Guardrails
  • Dashboards
06#SCHEMA

Structured output handling

Typed, schema-validated model responses, not free-form JSON you have to trust, wired straight into your backend.

  • Zod schemas
  • Tool calling
  • Retries
  • Validation

~/stack

all runtimes production-validated

FOUNDATION

Next.jsPythonTypeScript

REASONING

OpenAIClaudeEmbeddings

FRAMEWORK

RAG pipelinesPrompt templatesStreaming

STATE CONTROL

PineconepgvectorRedis cache

BACKEND CORE

tRPCAuthRate limiting

~/process

4-stage execution pipeline
  1. 01SCOPE

    What should the AI do, and where does it hand off to a human?

    output:Use-case map
  2. 02ARCHITECT

    Choose model, retrieval, caching, and streaming strategy.

    output:AI architecture spec
  3. 03EXECUTE

    Prototype fast, then productionize: errors, streams, costs.

    output:Working AI feature
  4. 04HARDEN

    Evals, monitoring, and output-quality tracking in production.

    output:Quality dashboard

~/work-case-study

linked to live /work routes
~/work/gpt-guiderAI learning companion

GPT Guider

Every lesson plan and answer streams through a RAG pipeline over the curriculum, with layered prompts tuned per user level.

100%Answer grounding
<250msStream start
-62%Cost / session
RAGOpenAIStreamingpgvector
~/action
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Years shipping
0+
Products launched
0K+
Users reached
0%
Uptime mindset