16 scopes · ranked by best match
Set up the data pipelines, versioning, feature flow, and storage patterns required for repeatable ML and AI work. Finish with an ML-ready foundation running in your cloud.
VIEWS 30D 0Take an existing AI proof-of-concept into monitored production with reliability, evaluation, security, and cost controls. Finish state: the demo that impressed everyone, now boring, monitored, and live.
VIEWS 30D 0Create a working voice agent for one bounded call flow with speech, tools, state, and handoff behavior. Finish state: a demonstrable voice agent completing real calls on one flow, with an honest read on production readiness.
VIEWS 30D 0Route requests across models based on quality, latency, availability, and cost while tracking spend and outcomes. Finish state: the right model per request, spend visible per feature, quality protected by evaluation.
VIEWS 30D 0Create test sets, scoring, red-team checks, policy controls, and release gates for an LLM-powered feature. Finish state: AI quality and safety measured and gated, not vibes-checked.
VIEWS 30D 0Extract structured fields and classifications from documents with validation and exception handling. Finish state: documents becoming clean structured data automatically, with humans only on exceptions.
VIEWS 30D 0Build an agent that gathers, structures, and summarizes account and prospect information from approved sources and tools. Finish state: reps opening calls with a current, sourced account brief they did not write.
VIEWS 30D 0Implement an AI support agent with knowledge retrieval, tool actions, escalation rules, and conversation tracking. Finish state: a share of support conversations resolved well without a human, measured.
VIEWS 30D 0Embed a scoped AI assistant inside an existing product with context, permissions, and basic evaluation. Finish state: a useful copilot live for real users, doing a defined set of things well.
VIEWS 30D 0Deploy a permission-aware conversational interface with RAG ingestion, retrieval, and grounding over approved internal knowledge. Finish state: staff getting cited, permission-correct answers from your own content.
VIEWS 30D 0A production-ready clickable prototype in two weeks, built with AI-accelerated design tools.
VIEWS 30D 0Dedicated QA team leveraging AI agents to deliver up to 1000 automated tests so regressions stop reaching production.
VIEWS 30D 0An ongoing pod building out AI agents and workflow automations across your SDLC and operations: an automation backlog delivered agent by agent, behind enterprise governance, with ROI measured on every rollout.
VIEWS 30D 0One operational AI agent deployed into a live workflow with governance and guardrails: workflow selection, agent build and integration, enterprise controls, and a measured rollout with hours-saved reporting.
VIEWS 30D 0Structured engagement that audits a design team, upskills designers in AI workflows and builds embedded AI capability.
VIEWS 30D 0Facilitated 2-week workshops that align stakeholders, define goals, and produce an actionable design & development strategy.
VIEWS 30D 0