AI use cases we have shipped, and the shape each one takes.

Three custom production systems built on your Databricks Lakehouse. Secure Document Intelligence ingests sensitive unstructured data (contracts, clinical records, compliance filings) into Delta Lake with Unity Catalog PII scrubbing at the ingestion layer. Conversational Analytics uses Databricks Genie Spaces mapped to your business vocabulary so executives ask questions in English and get SQL-grounded answers. Multi-Agent Workforce Automation builds compound agent systems on Mosaic AI Agent Framework for processes that single-prompt AI cannot handle. Governed by Unity Catalog and traced with MLflow. Industry playbooks for financial services, healthcare and life sciences, insurance, legal, retail and CPG, and manufacturing.

Two intelligence layers on top of the Snowflake you already run. Document Intelligence ingests Confluence, Jira, PDFs, CRM, and email into Snowflake for semantic search and cited Q&A using Cortex Search. Natural Language Analytics translates English questions into schema-aware SQL with written explanations using Cortex Analyst. Everything runs on Snowflake Cortex. No migration. No new vendor. Industry playbooks for financial services, healthcare, retail, SaaS, manufacturing, professional services, and media.

For a startup building a product that lets small and mid-sized businesses run operations through one chat interface, we built the layer underneath the conversation. An agent registry maps each request to the right capability; skill-based sub-agents carry the work through the connected tools; a custom router sequences them and recognises when a step needs a person; persistent memory carries context across sessions and channels; and a persona layer lets a team define an assistant once and publish it company-wide. Every action is logged and replayable, and anything irreversible waits for approval in the same chat.

Internal AI tools that live where your team already works. Slack is the interface and the approval layer: people ask plain-English questions about the business data in Airtable, Google Sheets, CRMs, and internal databases, get sourced answers with deep links back to the records, and approve or reject anything the system proposes before it acts. Every query and decision is logged to an audit trail and an admin page that tracks accuracy and daily LLM cost. Read-only by default, with write actions added only behind explicit approval flows. Our Slack Data Copilot for Airtable and Google Sheets is the shipped example.

A single autonomous system that runs an entire real estate agency end-to-end. It maintains a centralised CRM with a per-customer knowledge base, runs AI-assisted outbound across calls, SMS, WhatsApp, and Instagram, and moves leads through the right sequence without manual triage. It markets properties across channels, generates presentations and brochures on demand, and keeps listings, outreach, and the CRM in sync, so agents spend their time closing, not coordinating.

A layered, sub-agent-driven engine that takes natural-language intent and turns it into the right sequence of skills, queries, and tool calls. It connects to your databases, data warehouses, spreadsheets, and Parquet files, routes work to the right specialist agent, and gives the user back a clean, trustworthy answer instead of a raw data dump. Built for teams that have data but not enough analysts.

A complete marketing-operations engine, shipped for the US-based agency Robomarketer. The system writes social posts, designs ad creatives, builds ad-sets and campaigns, and reports back on ROI, CPC, and predictive performance, automatically. Humans stay in the loop through Slack approve/reject commands, every approval and rejection trains the orchestration layer, and continuous competitor research means agency clients get briefed with data, not vibes. Built on AWS with commercial-model integrations and a feedback loop.

An AI-powered creative evaluation platform that replaces manual ad review for large consumer brands. Posters, banners and packaging are reviewed against the brand's own visual standards, covering brand presence, layout and clarity, human attention, and message and call to action. Deep learning models trained on AWS SageMaker AI detect and measure what is on the canvas; a generative model on Amazon Bedrock, orchestrated by our agent framework, judges each check, writes the reasoning and a recommendation, and assembles a readiness score and shareable report in seconds instead of days. Thresholds adjust by product line and market, and accuracy is measured against the brand's own reviewers.

A custom Claude MCP connector that turns Meta Ads and Google Ads into a question-and-answer experience inside Claude. 18 tools today: weekly reports in about 30 seconds, creative-fatigue detection with AI-drafted replacement copy, wasted-spend audits with copy-paste-ready negative-keyword lists, daily anomaly scans correlated with your account change history, and any free-form question your dashboards can’t answer. Read-only by design, in your account currency. Built as the reference for the same pattern over any system with an API.

Forecast load from historical transaction patterns and scale GPU/CPU fleets ahead of demand. Run lean by default, react before spikes hit, and surface utilisation, cost, and projected load in a single observability layer. On a recent regulated-enterprise workload the same engine cut monthly compute cost ~40% while keeping 100% of inference inside the client’s infrastructure.

Lexxy is a daily AI companion that learns the user’s habits, lifestyle, and language over time. It journals with them, surfaces patterns they would miss on their own, and powers a Soundroom that records meetings and reflects back on tone, decisions, and personal credibility, turning every conversation into a memory the user actually owns. Built for individuals who want an AI that grows with them, not a chatbot that resets.