SmartPath AI
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SmartPath AI
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AI That Plans, Decides & Acts — Without Being Asked Twice
We design and deploy autonomous AI agents that operate across your systems, execute multi-step workflows, handle exceptions independently, and escalate only decisions that genuinely require human judgment.
Goal Received
Business objective parsed
Planning
Task graph decomposed
Tool Use
APIs & databases queried
Validation
Output quality checked
Complete
Goal achieved ✓
85%
Reduction in manual task intervention
4.2×
Faster multi-step task completion
99%
Execution audit coverage
14d
Days to first autonomous workflow live
Select a scenario below and run the simulation to see exactly how our agents plan, execute, and deliver — step by step.
TRIGGER: New credit memo request received
Retrieve borrower data
Calculate financial ratios
Check covenant compliance
Generate structured memo
Route to underwriter
Every agent we deploy is purpose-built for your workflows — with the reasoning, tool access, and oversight controls your enterprise requires.
Agents receive a high-level objective and autonomously plan, sequence, and execute the steps required — without step-by-step human instruction.
When conditions change mid-task, agents reason about the new context and adapt their approach rather than failing or halting for human intervention.
Agents operate across your entire stack — querying databases, calling APIs, reading documents, writing to systems — treating your tools as an orchestrated toolkit.
When an agent encounters an error, it diagnoses the issue, adjusts its approach, and retries — with full logging of what happened and why.
For high-stakes decisions, agents pause and surface the decision to a human approver before proceeding — autonomous speed with enterprise oversight.
Agent performance is monitored against outcome metrics. We retrain and refine decision models based on real operational data.
Three enterprise agent deployments — the workflow we inherited, what we built, and what it achieved.
⚠ The Challenge
A commercial lending firm was manually processing 800+ credit memo requests monthly. Each required pulling borrower data from 4 systems, calculating ratios, flagging exceptions, and routing — a process taking 2–3 hours per memo.
⚡ What We Built
We deployed an autonomous agent that retrieves borrower financials, runs ratio calculations, generates a structured summary, flags covenant exceptions, and routes the completed memo to the right underwriter — all without human involvement in routine cases.
2hr→12min
Per-memo processing time
91%
Memos handled without analyst input
Zero
Missed covenant flags post-deployment
$620K
Annual analyst time reclaimed
Our analysts now review memos instead of assembling them. The agent handles the data work; our people handle the judgment calls.
— Head of Credit Operations
⚠ The Challenge
An M&A advisory firm was spending 40–60 hours per deal on initial document review — manually reading NDAs, financial summaries, and cap tables to flag risks.
⚡ What We Built
We built an autonomous due diligence agent that ingests deal room documents, extracts key entities, identifies flagged risk terms, cross-references findings, and produces a structured preliminary findings report ready for senior advisor review.
60→6hrs
Hours per due diligence pass
3×
More deals reviewable per analyst
100%
Document coverage on every deal
94%
Risk flag accuracy vs. manual review
We're running initial due diligence in hours instead of weeks. The agent doesn't miss documents and doesn't get tired at page 300.
— Managing Director, Transaction Advisory
⚠ The Challenge
A national distributor was handling 300+ daily supply chain exceptions manually — each requiring a coordinator to diagnose, identify alternatives, notify stakeholders, and update 3 systems.
⚡ What We Built
We deployed an autonomous exception management agent that monitors supply chain events in real time, classifies each exception, identifies resolution options, executes approved resolutions, and escalates only genuinely novel situations to humans.
78%
Exceptions resolved without coordinator
4hr→22min
Average exception resolution time
300+
Daily exceptions handled automatically
41%
Reduction in coordinator headcount needed
Our coordinators used to spend their entire day on exception emails. Now they focus on the exceptions that actually require human thinking.
— VP of Supply Chain Operations
Rule-based automation executes instructions. Agents pursue goals — and adapt when the path changes.
Traditional automation breaks on edge cases
Agents reason through novel situations and adapt rather than failing
RPA requires a human to handle every exception
Agents classify exceptions and resolve the majority autonomously per your playbook
No visibility into why a decision was made
Every agent action is logged with inputs, reasoning steps, and outcome — fully auditable
Scaling requires proportional headcount
Agents scale horizontally — 10× the volume with no additional agent instances or cost
A structured 4-phase engagement — from workflow mapping to live autonomous operation with full audit coverage.
Map target workflows into tasks, decision points, and tool dependencies — identifying what can be automated, what needs human gates, and what requires reasoning.
Design the agent's goal structure, tool integrations, memory model, and escalation logic — built around your existing systems and risk tolerance.
Agents are deployed in shadow mode first — running in parallel with humans to validate accuracy before taking autonomous control.
Every action is logged. We monitor accuracy, escalation rates, and business outcomes — retraining and refining based on live production data.
Tell us which workflow costs your team the most time and judgment. We'll scope an agent deployment and show you what autonomous operation looks like in 14 days.