Without grounding in company-specific content and permission controls, AI assistants produce confident but unreliable answers. Users stop trusting them, adoption drops, and the tool becomes another failed experiment. Three patterns that signal the problem:
WHAT WE DO
Assessing AI maturity across data, systems, processes, people, security, governance, integrations, analytics maturity, and current AI adoption. The output is a factual baseline connected to actual business use cases—not a generic maturity score that leaves you with theory.
Helping choose the right approach for the business scenario: conversational assistant, enterprise search, document Q&A, sales enablement, field service support, employee self-service, or natural language analytics over combined document and tabular data.
Starting with a focused MVP: selected content sources, target users, test questions, evaluation criteria, feedback loop, and monitoring plan. After the pilot proves value, the solution expands to more content, users, workflows, languages, and integrations.
Designing role-based access, document-level security trimming, moderation, prompt governance, audit logs, human review, and escalation paths. The assistant should not expose data a user cannot access in the source system.
Connecting assistants to CRM, DXP, CMS, ERP, ticketing, Microsoft Teams, portals, automation tools, and custom business applications. AI should help users where work already happens, not in a separate interface.
