I help teams turn messy information into reliable tools: multilingual narrative monitoring, document intelligence, audience-adaptive content pipelines, and speech analytics for real-world workflows.
I take on a small number of engagements at a time to stay hands-on through delivery.
MIT-trained engineer with applied AI experience from expert-systems era through modern LLM workflows
10+ years shipping systems in high-pressure environments with complex stakeholders
Clear documentation, reproducible evaluation, and responsible deployment practices
Track themes, framing shifts, and emergent narratives across languages and channels—so teams can understand what's moving and respond with clarity.
Turn internal docs into a searchable, citation-grounded assistant: policy, research, contracts, SOPs, knowledge bases—built with evaluation and guardrails.
Transform complex information into clearer formats for different audiences—readability levels, structured summaries, localization, tone guides—with quality checks.
Real-time or batch transcription with sentiment/tone signals and actionable summaries for coaching, customer experience, or internal review workflows.
LLM test harnesses, retrieval evaluation, red-teaming, prompt/version control, and "what good looks like" metrics—so AI stays dependable.
Pick the model that fits your timeline:
Define the right use cases, data readiness, architecture, success metrics, risks, and a build plan your team can execute.
A working demo in your environment: a narrow scope that proves value, with evaluation, documentation, and a path to production.
Production implementation with monitoring, tests, handoff docs, and training—built to survive contact with reality.
Supported a public-facing communications environment with multilingual narrative tracking and trend analysis, helping teams understand how messaging themes evolved across channels.
Designed an approach to transform complex news/information into formats accessible to different audiences while preserving fidelity and sourcing.
Built an early prototype for streaming speech processing with live sentiment/tone signals and summaries for feedback and coaching workflows.
I don't do political microtargeting, covert persuasion, or surveillance deployments. If your use case needs careful boundaries, we'll define them up front.
Define "done" — success metrics, failure modes, and constraints
Build the smallest valuable slice — prove value fast
Instrument + evaluate — quality, latency, cost, error analysis
Harden + hand off — docs, tests, monitoring, training