AI Agents
Agents are a workloadon the platform, not the platform.
Once your data is connected and governed, agents become the highest-leverage layer: procurement, service, sales, IT, and finance assistants that take real work off your team, with human oversight and full audit trails.
Agents are where the hype lives, and where most projects fail, because teams reach for them before the foundation exists. An agent acting on fragmented, ungoverned data is a liability, not an asset.
We treat agents as the final stage of a maturity path: assess, build the data foundation, automate, then add agents as another governed workload on the platform. That sequence is what makes them reliable enough to put in front of a customer or the books.
Agents we build
Each one runs on your connected data, with guardrails and a human in the loop.
Procurement agent
Drafts purchase requests, checks them against policy and budget, and routes approvals, with a human signing off before anything is committed.
Customer service agent
Answers from your own knowledge base and order data, resolves routine tickets, and escalates the rest with full context attached.
Sales assistant
Preps account briefings, drafts follow-ups, and keeps the CRM current using the connected data foundation, not a disconnected copy.
IT support agent
Triages requests, runs known runbooks, and resolves common issues, escalating anything outside its guardrails to your team.
Finance analyst
Pulls from the governed reporting layer to answer variance questions, flag anomalies, and assemble the numbers behind a board deck.
Portfolio management assistant
Tracks initiatives, surfaces status and risk across projects, and keeps executives current on where technology investment is going.
Build the foundation first
If you already have a connected data foundation, we can move straight to agents. If not, start with an assessment and the data layer, then add agents as a workload that keeps paying off.