AI Systems We Have Put Into Production

The client sells AI sales agents that engage website visitors, answer product questions, and capture leads around the clock. Their early product handled one conversation well, but every new customer needed hand-tuning, and each agent lived inside the website widget alone. Growth meant a platform, not more manual setup.

• Rebuilt the agent layer as a multi-tenant runtime, so each customer gets an agent trained on their own brand voice, product catalog, and objection handling without bespoke engineering per account.
• Grounded agent answers in customer-supplied content with a retrieval layer, so agents cite the customer's real product and pricing information instead of improvising.
• Extended the agent beyond the website widget into the messaging channels their customers already use, with one conversation state shared across channels.
• Added lead capture and qualification handoff, so a conversation that shows buying intent becomes a structured, routed lead rather than a transcript someone has to read.
• Instrumented the whole path from first message to captured lead, so both the client and their customers can see which conversations produce revenue.

The client automates content for small and mid-sized businesses across social channels, blogs, and newsletters. Content generation was already working; the operation around it was not. Ideation, scheduling, publishing, and engagement each needed a person in the loop, which capped how many accounts one operator could run.

• Built an autonomous content pipeline that runs ideation, drafting, scheduling, and publishing as one continuous workflow instead of four supervised steps.
• Implemented a brand knowledge base per account, so generated content stays in the client's voice and inside their claims, and improves as the account adds material.
• Shipped an MCP server exposing the platform as tools an AI assistant can call directly, so an operator can plan and publish a campaign in conversation rather than through the dashboard.
• Automated multi-channel distribution across the major social networks plus blog and newsletter delivery, with per-channel formatting handled by the engine.
• Added a control center with predictive analytics, so operators supervise exceptions instead of approving every post.

The client is a full-service marketing agency for automotive and RV dealerships, running traditional, digital, and social campaigns for over a hundred accounts across the United States and Canada. Analysts were spending their week assembling per-dealer performance reports by hand, which left no time for the optimization the dealers were actually paying for.

• Consolidated spend, traffic, and lead data from the ad platforms and dealer systems into one pipeline, so every account reports off the same numbers.
• Automated per-dealer performance reporting, replacing the manual weekly assembly with generated reports that read like an analyst wrote them.
• Deployed a monitoring agent that watches campaign performance continuously and flags accounts that need attention, instead of surfacing problems at the end of a reporting cycle.
• Automated inbound lead routing and first-touch follow-up, so a lead from a dealer campaign gets a response in minutes rather than waiting for business hours.
• Built a retrieval assistant over the agency's own campaign history, so account teams can ask what worked for a comparable dealer instead of guessing.

The client builds an applicant tracking and recruiting CRM for staffing agencies and corporate HR teams. Keyword search was returning the wrong candidates from a database the customers had spent years filling, and recruiters were doing the screening, sequencing, and status-chasing that the platform was supposed to absorb.

• Replaced keyword matching with semantic search over parsed resumes, so a role returns candidates who fit the requirement rather than candidates who happened to use the same words.
• Built a structured parsing layer that turns inconsistent resume formats into comparable candidate records, including migrated data from the systems customers switched from.
• Automated the screening pass, so a new applicant arrives already ranked against the role with the gaps called out.
• Added agents that run candidate outreach sequences and interview scheduling, and chase the pipeline stages that recruiters were tracking manually.
• Surfaced recruiter and pipeline performance in role-based dashboards, so agency owners can see where a search is stalling while there is still time to fix it.