Artemis

Artemis is an AI layer purpose-built for executive decision-making: conversational and voice-driven, with cross-module business reasoning and long-term context, designed to replace the reflex of building another static dashboard every time leadership has a new question.

Traditional dashboards answer the questions they were built to answer, and nothing else. Every new leadership question — 'why did margin drop in the region with the new hires' — becomes a request to an analyst or a new dashboard ticket. Executives needed a way to ask ad hoc, cross-functional questions and get a governed, data-backed answer immediately, in the format that fits how they actually work — including by voice, in meetings.

• Answer ad hoc business questions across modules instead of only the metrics a dashboard was designed to show
• Support real-time voice interaction suitable for use during meetings, not just typed queries
• Maintain long-term context across sessions so the assistant remembers prior decisions and workspace history
• Automate recurring executive workflows (briefings, exception alerts) instead of requiring a person to remember to check a dashboard

Artemis combines a conversational reasoning layer, a real-time voice pipeline, and a long-term memory store scoped to each workspace. Business questions are decomposed into module-level queries, executed against governed data sources, and reassembled into a single coherent answer — with the same access controls a human user would have.

• Separated the voice transport layer from the reasoning layer, so the underlying AI reasoning can be reused across chat, voice, and automated briefings
• Built a long-term context store per workspace, distinct from a single conversation's memory, so the assistant retains relevant history across sessions without re-ingesting everything on every request
• Cross-module reasoning is implemented as orchestration over existing governed APIs rather than a direct data lake query, preserving the same permission boundaries as the rest of the platform
• Designed for graceful degradation — if voice fails, the same reasoning engine is still available over text

• Keeping voice latency low enough to feel conversational in a live meeting setting
• Deciding what belongs in long-term memory versus what should be re-fetched fresh from source systems, to avoid the assistant acting on stale context
• Orchestrating multi-step reasoning across modules while keeping each intermediate step auditable, which matters when the outputs inform business decisions
• Avoiding hallucinated figures by grounding every quantitative answer in a traceable query against real data

• Used OpenAI's Realtime API for the voice pipeline rather than building a custom speech stack, to focus engineering effort on business reasoning quality
• Adopted a retrieval-plus-verification pattern: the model proposes what data it needs, the system fetches it from governed sources, and the final answer is only generated once the underlying numbers are confirmed
• Built prompt and context management as a first-class engineering discipline (versioned, tested) rather than ad hoc string templates

• Designed the overall AI orchestration architecture and long-term memory model
• Built the voice pipeline integration and its fallback-to-text behavior
• Defined the grounding/verification pattern used to keep quantitative answers accurate

• Reduced time-to-answer for ad hoc executive questions from a multi-day analyst request to a live conversation
• Enabled voice-driven business queries during live meetings for the first time
• Long-term memory reduced repeated context-setting across sessions for recurring executive workflows

• Conversational AI in an executive context lives or dies on grounding — a fluent wrong answer is worse than a dashboard, so verification against source data had to be non-negotiable
• Voice interfaces need an equally strong text fallback; treating voice as the only path was a mistake caught early in testing