AI & Agentic Systems

Support response lagged the industry standard, and most of what arrived was the same set of questions asked in different words, spread across general, technical, and financial categories that each needed a different person to answer.

A support chatbot with a connected FAQ and a machine learning model trained on the company's own prior conversations rather than generic support data, so it answers in the company's terms and recognizes how its customers actually phrase things. Language detection routes inquiries correctly. Answer evaluation scores a response before it reaches a customer, so low-confidence cases go to a person instead of guessing. The repeat questions answer themselves, and the support team gets the ones that need judgment.

A publicly traded producer and exporter of agricultural products had a customer support problem that was becoming an employee problem: staff answered inquiries from memory and search while the accurate answers sat in company data nobody could query conversationally.

An AI system built on agentic retrieval-augmented generation over the company's existing data. Employees get faster, more accurate responses grounded in material the company already owns, which was the point: better answers for customers and an easier job for the people giving them.

A SaaS company's documentation lived in every format across internal and external systems, and its customers couldn't find answers without opening a support ticket. Self-help wasn't possible, so the support queue carried questions the documents already answered.

Built inside Microsoft Teams on agentic retrieval-augmented generation, the system processes every document automatically as it arrives. Users ask questions and get instant answers drawn from company data, administrators watch the key metrics from a dashboard, and support handles the requests that actually need a person.

Business development research meant assembling public company data, industry trends, and competitive signals by hand for every prospect worth pursuing.

A custom AI research agent that automates collection and analysis across web sources, business databases, and market signals, generating the competitive picture a seller walks in with, on demand instead of on request.

Quote verification, licensing questions, order setup, and CRM updates each took minutes, and the minutes never stopped.

AI-powered automation workflows handling quote verification against pricing, promotions, and margins, licensing and compliance question resolution, basic order setup, metric dashboards, and CRM record updates, with the exceptions routed to people.

Support engineers and business development teams serving global manufacturers spent their hours on research: technical documentation in one place, internal knowledge in another, prospect data somewhere else, all of it partially current. 

A retrieval-augmented AI assistant that continuously ingests technical documentation, best practices, historical projects, and market signals, returning instant, cited, context-aware answers and prospect profiles, with escalation flags and strict data-security and brand-compliance controls. It updates incrementally as documentation changes, so answers reflect current material rather than a snapshot. 

Content drafting, meeting summaries, project tracking, quotes, licensing, CRM updates. None of it is difficult. All of it is constant, and in a high-volume B2B operation it adds up to entire roles dedicated to administration.

An AI platform built on large language models and workflow orchestration: branded content generation, parsing of emails and tickets for pending actions, project tracking with reminders and timelines, automated quote verification and CRM updates, all surfaced through centralized dashboards with escalation alerts.