Generative AI & LLM Apps Built to Survive Production

Most generative-AI demos never reach production. Mobizio builds the real thing — LLM apps, RAG pipelines, and AI agents on frontier and open-weight models that are evaluated, observable, cost-engineered, and safe by default. (Need predictive models or computer vision instead? See our Machine Learning & Data Science service.)

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Let's Break It Down

What Are Generative AI & LLM Apps?

Generative AI apps are products built on top of foundation models — frontier models from OpenAI, Anthropic, and Google, plus open-weight models like Llama and Mistral. It's distinct from traditional ML: instead of training models from scratch, you wire LLMs, retrieval, and tools into reliable user-facing systems. The hard part isn't the prompt — it's the engineering around it: evaluations, observability, guardrails, latency budgets, and inference economics.

Build LLM-powered features with OpenAI, Anthropic, Bedrock, and open-weight models like Llama and Mistral
Architect RAG pipelines with chunking strategy, hybrid search, and reranking — not naive vector lookup
Engineer multi-step AI agents using LangGraph, DSPy, and CrewAI with human-in-the-loop checkpoints
Instrument every prompt with LangSmith or Langfuse — golden datasets, regression tests, and drift alerts
Bring down inference cost through prompt caching, semantic caching, quantization, and vLLM serving
Harden against hallucination, jailbreaks, and PII leakage with input/output guardrails and audit trails

10+

Years building production software, since 2015

Eval-first

Every LLM system shipped with evals, observability & guardrails

End-to-end

From discovery and POC to production hardening and monitoring

What You Get

Everything You Need to Succeed

We don't just deliver code — we deliver outcomes. Here's what makes our approach different.

LLM Application Development

Production apps on frontier models from OpenAI, Anthropic, and Google, plus Amazon Bedrock — streaming, structured outputs, function calling, tool use, and prompt versioning. We treat prompts like code, with tests and review.

RAG & Vector Search Engineering

Retrieval pipelines on Pinecone, Weaviate, Qdrant, and pgvector. Smart chunking, hybrid BM25 + dense search, reranking, query rewriting — and an eval harness so you know retrieval quality is improving.

AI Agents & Multi-Step Workflows

LangGraph and DSPy agents with deterministic state machines, tool calls, fallback paths, and human-in-the-loop. We design for observability and graceful failure, not just a happy-path demo.

Evaluation & Observability

LangSmith, Langfuse, Braintrust, and Arize instrumentation from day one. Golden datasets, LLM-as-judge evals, regression gates in CI, and dashboards your product team actually uses.

Inference Cost & Latency Engineering

Prompt caching, semantic caching, model routing, batch inference, vLLM and Ollama self-hosting, and quantization. We measure dollars per request and p99 latency, then bring both down.

AI Safety, Guardrails & Compliance

Input filtering, output validation, jailbreak resistance, PII redaction, and full audit logs, aligned to NIST AI RMF principles. Secure-by-default practices that we can build toward SOC 2, HIPAA, and GDPR requirements.

Our Process

Our Methodology for Success

A battle-tested process built for speed, quality, and zero surprises.

Discovery & Eval Design

We pin down the real success metric — not just 'use AI'. Before any prompts, we build a golden eval set so progress is measurable from week one.

Architecture & POC

A working LLM/RAG/agent prototype in 2–3 weeks, with the right model, retrieval strategy, and orchestration framework chosen for your latency and cost budget.

Production Hardening

Observability, guardrails, error handling, rate-limit and cost ceilings, retry logic, and CI-gated eval regressions — the engineering most AI POCs skip.

Monitor, Optimize, Upgrade

Live monitoring of drift, cost, and quality. We swap in new models as they release, tune prompts against fresh eval data, and reduce token spend every quarter.