Two practices. One rope.
Two coordinated practices delivered under one engagement: AI adoption, team workflow integration, forward-deployed engineering, and AI safety and governance on the strategy side; agentic workflows, embeddings and chunking, document AI, and model tuning on the applied ML side.
Strategy & Org
AI adoption consulting
Bring the whole team to base camp: operating model, training, and the rituals that make AI stick across product, ops, and leadership.
Organizational structuring for AI
Design teams, roles, and reporting lines that match the work AI actually creates: platform, applied, evaluation, and governance.
Team workflow integration
Embed AI into the workflows your teams already live in: IT ticketing and triage, recruiter pipelines, customer support, and back-office operations.
Forward-deployed engineering
Embed alpage engineers into your team to build, ship, and transfer ownership of AI features alongside your people. We code with you, then leave the codebase yours.
AI safety and governance
Risk frameworks, model governance, and evaluation harnesses that tell you, before users do, whether a release is shipping a regression or crossing a policy line.
Applied ML
Agentic workflows
Multi-step agents with tool use, planning, and parallel execution: the orchestration patterns that turn LLMs into production systems.
Embeddings & chunking strategies
Embedding models, chunking strategies, and vector database management, tuned to your corpus: the retrieval layer that decides what context a model actually sees.
Document AI
Pull structure and signal out of PDFs, scans, and messy real-world documents, and feed them into downstream search and agents.
Model tuning
Fine-tune, distill, and align open and closed models against the metrics that actually matter for your product.
Applied machine learning
Classification, ranking, and computational methods like affinity signaling: the quantitative pipelines that turn raw behavior into product signal.