AI Engineering meets Software Craft

We design, build, and scale LLM‑powered products, MLOps pipelines, and modern web apps - from first prototype to production.

Start a project Explore services
  • LLM Apps
  • RAG & Agents
  • MLOps
  • Cloud & APIs
Abstract header art for Team Placebo

We build reliable AI, not hype

Team Placebo is an AI engineering and software development company that helps organizations transform their data into versatile tools with real-world impact. We bridge research and production through rigorous engineering, thoughtful design, and close collaboration with our clients. With a background in web development, game design, and platform engineering, we focus on accuracy, performance, and building products that excel in interaction design and usability.

Capabilities

LLM Applications

Chat, copilots, and agentic workflows with guardrails, evals, and analytics. Retrieval‑augmented generation (RAG), tools/functions, and multi‑step planners.

  • Model selection & prompt engineering
  • RAG on vector / hybrid search
  • Safety, evals, and observability

Data & MLOps

From notebooks to production: CI/CD for models, feature stores, offline/online evaluation, and automated deployment.

  • Tracking: experiments & datasets
  • Serving: REST, gRPC, and batch
  • Monitoring: drift & quality

Web & Platform

Modern web apps and backends that scale. APIs, integrations, and secure authentication with clean, maintainable code.

  • SPA/SSR front‑ends
  • Python/TypeScript backends
  • Cloud: AWS, Azure, GCP

Services

AI Product Discovery

Opportunity mapping, quick feasibility spikes, and a de‑risked roadmap with ROI and compliance considerations.

LLM Integration & RAG

Grounded generation over your data with hybrid search, chunking strategies, and latency/quality tuning.

MLOps Foundations

Data versioning, experiment tracking, model registry, automated testing, canary deploys, and monitoring.

Full‑Stack Development

Design‑to‑deploy delivery of user‑facing products and APIs using Python, TypeScript, and cloud‑native services.

Performance & Cost Tuning

Token, latency, and throughput optimization; caching and batching; on‑prem or GPU‑efficient inference.

Security & Compliance

Threat modeling, PII handling, and least‑privilege architecture aligned with SOC2/ISO practices.

Recent Work

Abstract art for Team Placebo

RAG Query System over 14K Inventory Assets Records

Confidential (Healthcare) • 2025

Built a searchable RAG layer on top of ~14,000 assets to support triage and planning with natural-language queries.

  • Problem: Operators needed quick answers across many systems (models, owners, lifecycle, location).
  • Approach: ETL + normalization → embeddings → metadata-filtered vector search; hybrid keyword + dense retrieval; caching.
  • Tech: ChromaDB, Python, FastAPI, scheduled ingests, OpenAI/OSS embeddings.
  • Impact: Unified view of inventory and faster decision-making for upgrades, incidents, and audits.
Python TypeScript Vector Search AWS / Azure / GCP Auth & Security

Contact

We partner with product teams to ship LLM-powered features, grounded search, and the MLOps glue that keeps them reliable. Whether you’re exploring an idea, hardening a prototype, or scaling a production workload, we’d love to hear about it.

To help us respond quickly, include:

  • Goals & success metrics.
  • Who the users are and where AI fits in their workflow.
  • Your current stack & constraints (cloud, languages, auth, data residency).
  • Data sources to ground the model (formats, size, freshness).
  • Security/compliance needs (PHI/PII, SOC 2/ISO, HIPAA).
  • Rough timeline & budget range.
  • Links to docs, mockups, or repos (if available).

Need an NDA first? Happy to send ours or sign yours. We usually reply within one business day.

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