Solutions
Custom AI Agents
AI that does the work, so your team does more of what matters.
An AI agent handles the routine, multi-step work your team does every day — faster, consistently, and without pulling anyone away from higher-value work.
It pulls the information a job needs from different systems, makes the bounded decisions the work calls for, takes the actions it has permission to take, updates the records, and triggers whatever comes next. When something falls outside its rules, it brings a person in. The work keeps moving without waiting on someone to move it.
That turns recurring, multi-step work into operating capacity. Steps that used to sit in a queue get done, execution speeds up, and the same team carries more volume with more consistency. Agents take the repeatable work off people's plates, so their judgment goes where it counts.
What an AI Agent Can Own
An agent owns a defined job and carries it from first step to done.
Each agent is scoped to one clear job, with a named business owner, set permissions, and a point where it escalates to a person. Below are a few examples of the work an agent can carry across a consumer business:
Customer Resolution Agent
Answers routine customer questions, gathers the account context, completes approved actions, updates the record, and escalates anything unusual to a person.
Customer Retention Agent
Spots customers who need attention, decides the right next action within set rules, starts the approved outreach, and routes high-value cases to the team.
Reporting and Analytics Agent
Pulls the numbers from your source systems, builds the weekly and monthly reports, and flags anything that moved outside the normal range. Your analysts stop rebuilding the same spreadsheets and start answering the questions the numbers raise.
Campaign Operations Agent
Coordinates recurring campaign work across CRM, analytics, creative, and reporting, so launches stay on schedule without manual chasing.
AP/AR Agent
Matches invoices to purchase orders, codes transactions, and chases the exceptions that don't reconcile. Month-end close moves faster because the routine matching is already done before anyone opens the ledger.
Engines and Agents
AI Engines produce intelligence.
AI Agents carry the work forward.
AI Engines and AI Agents are built differently and do different jobs. An AI Engine is the forecast. An AI Agent is the operations team that reads it and reschedules the flights. Both run on your data, and they are strongest together.
AI Engines
Predictive models, trained on your data.
- Trained on your historical data to learn what drives outcomes
- Score and rank customers, accounts, and actions by likelihood or value
- Forecast demand, response, and revenue
- Optimize media, spend, and targeting
- Retrained and monitored for accuracy as your data shifts
AI Engines
Reasoning wired into your systems, inside guardrails.
- Reason through a goal one step at a time
- Ground each step in your business knowledge and live data before acting
- Act through permissioned connections to your systems
- Coordinate multi-step work from start to finish
- Operate within guardrails, and escalate exceptions to people
A Churn Engine names the customers most likely to leave. A Retention Agent takes it from there.
It pulls each customer's context, decides the right intervention within set rules, sends the approved outreach, updates the CRM, and routes the highest-value accounts to a person. Intelligence and execution in one motion.
Where an Agent Makes Sense
MatrixPoint builds an AI Agent only where the economics justify one.
Almost any workflow can be automated in theory. The question is whether it should be. MatrixPoint weighs each candidate against the numbers that decide the case: how often the work runs, the hours and labor it consumes, how many systems it touches, how often it hits exceptions, and what faster, steadier execution is worth against the cost to build and run it.
That is the economic tipping point.
Some workflows clear it today. Others only make sense at higher volume, and some are better left with people. Discovery tells the difference before a dollar goes into a build.
What an AI Agent Can Own
An agent owns a defined job and carries it from first step to done.
Each agent is scoped to one clear job, with a named business owner, set permissions, and a point where it escalates to a person. A few examples of the work an agent can carry across a consumer business:
Agent Discovery Sprint
Find the work worth automating, and prove the case for it.
Business case
prioritized, costed, and build-ready
- Map and rank candidate workflows by operating value
- Test each against its economic tipping point, so only agents that earn their place move forward
- Deliver a build-ready blueprint, including controls, ownership, and which work should stay with people
Agent Build
Build a production agent into the way the business runs.
Production
working agents in your systems
- Connect the systems and data the agent works from
- Configure its reasoning, business knowledge, permissions, workflow logic, and guardrails
- Set approved actions, human checkpoints, and escalation, then evaluate and go live with monitoring
Agent Management
Keep the agent performing, with MatrixPoint accountable for how it runs.
Ongoing
MatrixPoint operates and improves it
- Monitor performance against defined measures and report to the business owner
- Re-evaluate when models, data, or processes change, and tune accordingly
- Govern permissions and escalations, and expand to new work as the case is proven
What an AI Agent Can Own
An agent owns a defined job and carries it from first step to done.
Each agent is scoped to one clear job, with a named business owner, set permissions, and a point where it escalates to a person. A few examples of the work an agent can carry across a consumer business: