# AP Studio — AI Technology

> AP Studio ships production-grade AI integrations: RAG pipelines, autonomous agents, fine-tuned models, and domain-specific copilots. We treat AI as infrastructure — instrumented, evaluated, and shipped for measurable outcomes rather than demos.

Source page: https://byappi.com/ai-technology
Contact: journey@byappi.com

## What we build

- Retrieval-Augmented Generation (RAG) systems over private knowledge.
- Autonomous and tool-using agents (planning, multi-step, code execution).
- Domain-specific copilots embedded in client products.
- Fine-tuned and instruction-tuned models on proprietary data.
- LLM evaluation harnesses (offline + online) and guardrails.
- Voice agents (real-time transcription + LLM + TTS).
- Document understanding pipelines (OCR, layout-aware parsing, extraction).
- AI-driven internal tools and workflow automation.

## Stack

- Foundation models: OpenAI (GPT-5/4o), Anthropic (Claude), Google (Gemini), Meta (Llama), Mistral.
- Orchestration: LangGraph, LangChain, LlamaIndex, custom Python/TypeScript runtimes.
- Vector / hybrid search: Pinecone, Qdrant, Weaviate, pgvector, Elasticsearch, Vespa.
- Fine-tuning: LoRA / QLoRA, PEFT, Unsloth, Axolotl, OpenAI / Anthropic / Vertex managed FT.
- Inference / serving: vLLM, TGI, Modal, Replicate, Bedrock, Vertex AI.
- Evals: Ragas, DeepEval, Braintrust, custom golden sets, LLM-as-judge.
- Observability: Langfuse, Helicone, Arize, OpenTelemetry.
- Infra: AWS, GCP, Azure, on-prem GPU.
- Privacy: PII redaction, EU residency, BAA-eligible deployments.

## Engineering principles

- Eval-driven development: every change gated by an offline eval delta.
- Cost + latency budgets per route (P50 / P95 enforced).
- Hybrid retrieval (BM25 + dense + reranker) over single-vector RAG.
- Tool-use over prompt-stuffing for actions.
- Guardrails: input/output validators, policy filters, structured outputs (JSON schema, function calling).
- Deterministic fallbacks for every model call.
- Caching at the prompt + semantic + retrieval layers.

## Engagement model

- Discovery: 1–2 weeks (data audit, problem framing, eval design).
- Prototype: 2–4 weeks to first production-grade slice.
- Hardening: evaluation harness, observability, cost optimization.
- Operate: ongoing model upgrades, retrieval tuning, regression tracking.

## RAG vs fine-tuning vs agents

- RAG — when knowledge changes frequently or is large.
- Fine-tuning — when style, format, or domain syntax must be enforced.
- Agents — when the task requires multi-step reasoning or tool use.
- We routinely combine all three; default architecture is hybrid retrieval + structured-output LLM + tool-using agent layer.

## Related

- [Website Development](https://byappi.com/llms-website-developemnt.txt)
- [Mobile App Development](https://byappi.com/llms-app-development.txt)
- [Marketing — SEO & GEO](https://byappi.com/llms-marketing.txt)
- [Studio overview](https://byappi.com/llms.txt)