Agentic AI for the plant floor, the supply chain, and the front office
We design and operate AI agents that run inside real constraints — machine limits on the line, live disruptions in the supply chain, and licensure in financial services. A governed reference architecture underneath every engagement, not a demo bolted onto a slide.
Four domains. One discipline.
An agent only earns its place if it holds up against real constraints. We build for four environments where those constraints are unforgiving.
Smart Manufacturing
Predictive maintenance, vision-based quality control, and agentic production scheduling on the plant floor.
View use cases →Supply Chain Management
Demand sensing and forecasting, plus automated supply planning with continuous scheduling adjustments.
View use cases →Financial Services
Agentic AI for RIA & wealth management, investment banking, and private equity — built around licensure and audit trails.
View use cases →Financial Operations
Predictive cash-flow forecasting, automated close and reconciliation — the AI-driven analytics practice behind Touchstone Corporate Finance.
View use cases →A reference architecture for governed, agentic AI
Every engagement runs on the same five-layer stack: agents that plan and act, grounded in your own systems of record, wrapped in the observability and governance a regulated or safety-critical environment requires.
ERP · CRM · WMS · docs · market data
Cloud-hosted
Fastest to stand up — runs on your existing cloud AI platform (Azure OpenAI, AWS Bedrock, Google Vertex).
Hybrid
Sensitive data and retrieval stay inside your environment; orchestration and models run in the cloud.
On-premise
Full data control for the most regulated environments — banking, wealth management, and defense-adjacent manufacturing.
Governance & risk controls
The risks any agentic system introduces, and what's built into the stack to manage each one.
| Risk | Likelihood | Mitigation built into the stack |
|---|---|---|
| Hallucinated or ungrounded output | High | Retrieval-grounded generation, confidence scoring, and a verifier/critic step before anything reaches a system of record |
| Data privacy exposure | Medium | Encryption in transit and at rest, automatic PII redaction, role-based access on every connected data source |
| Biased or unfair outputs | Medium | Balanced training and evaluation sets, periodic fairness audits, human review on sensitive decisions |
| Regulatory violation | Low–Medium | Policy-enforcement layer, full audit trail on every agent action, compliance review before go-live |
| Loss of user trust | Medium | Transparent rationale on every recommendation, a human fallback path, explainable outputs |
AI that watches, predicts, and adjusts on the line
Plants run on physical constraints an agent has to respect — machine limits, shift patterns, and safety rules. We build systems that operate inside those limits, not around them.
Predictive Maintenance & Vision-Based Quality Control
Unplanned downtime and defect leakage quietly erode OEE and gross margin.
Computer-vision models inspect the line in real time while sensor-based predictive models flag equipment likely to fail — before it does — and route work orders automatically.
Agentic Production Scheduling
Static schedules can't react to machine variability, staffing gaps, or last-minute order changes.
A plant-floor digital twin simulates line conditions; an agentic scheduler recommends — and, within set guardrails, executes — line rebalancing and changeover sequencing in near real time.
The sensing and planning layer that keeps commitments in sync
Supply chains carry the cost of every wrong guess about demand. We build the layer that keeps inventory, capacity, and delivery promises aligned — continuously, not once a month.
Demand Sensing & Forecasting
Traditional monthly forecasts lag real demand signals, driving stockouts on some SKUs and excess inventory on others.
ML models fuse POS data, market signals, and promotional calendars into a short-horizon demand signal that recalibrates continuously instead of on a fixed cycle.
Automated Supply Planning with Scheduling Adjustments
Manual re-planning can't keep pace with supplier delays, capacity shifts, or transportation disruptions.
An agentic planning layer continuously re-optimizes supply plans and delivery schedules against live constraints, executing routine adjustments and escalating exceptions for sign-off.
AI that produces an audit trail, not just an answer
Financial services runs on documentation, deadlines, and licensure. We build agentic workflows for RIA & wealth management, investment banking, and private equity firms — plus the AI-driven financial operations work behind Touchstone's own Corporate Finance practice — each one scoped to the compliance requirements of that seat.
Advisor Onboarding & Regulatory Reporting
Advisor onboarding (U4/U5, ADV filings) and recurring reports — FOCUS, N-PORT/N-CEN, UBPR — consume compliance hours and carry real error cost.
Agentic workflows extract, validate, and file recurring regulatory reports and onboarding paperwork, routing anything ambiguous to a compliance officer for sign-off.
IRA & Retirement Account Guidance Support
RMD calculations, rollover analysis, and fiduciary suitability documentation across a large book of IRA and retirement accounts are slow to do consistently by hand.
An assistant drafts RMD and rollover analysis and flags accounts drifting from stated suitability under ERISA 3(21)/3(38) frameworks, with a documented rationale trail for every recommendation an advisor acts on.
AI-Accelerated Deal Screening & CIM Drafting
Sourcing teams manually screen hundreds of targets and spend days drafting teasers and CIMs.
An agent screens targets against thesis criteria, extracts financials from data rooms and filings, and produces a first-draft CIM or teaser for a banker to edit — not approve blind.
Automated Diligence & Data-Room Intelligence
Buy-side and sell-side diligence means reviewing thousands of data-room documents against tight deadlines.
Document-intelligence agents extract key contract terms, flag red-flag clauses, and surface financial anomalies across the data room into a structured diligence memo for the deal team.
Portfolio Company KPI Monitoring
Firms struggle to get consistent, timely KPI visibility across a portfolio where every company reports differently.
Automated ingestion normalizes portfolio-company financials into a live KPI view, using Touchstone's xCIPM performance-management approach, with variance and anomaly alerts for the deal partner.
AI-Driven Deal Sourcing & Thesis Screening
Sourcing teams must continuously sift thousands of private companies against an evolving investment thesis.
Continuous market scanning scores private companies against thesis criteria and surfaces a ranked, evidence-backed target list for the sourcing team to run down.
Predictive Cash-Flow Forecasting & Anomaly Detection
Spreadsheet-based forecasting rarely gives more than a few days' warning before a shortfall hits, and unusual transactions surface at month-end instead of when they happen.
Models trained on the company's own ERP and banking history continuously project cash position and score incoming transactions against expected patterns, escalating anomalies before they compound.
Automated Month-End Close & Reconciliation
A close that runs late almost every month leaves the controller no time for forecasting, and reconciliation errors surface too late to matter.
Machine-matched reconciliation automates the repetitive share of the close across bank, ledger, and subledger data, flagging exceptions for the controller instead of requiring a manual line-by-line match.
These are technology and automation engagements delivered by Touchstone's Consulting & Technology practice. They are not investment, tax, or legal advice, and are offered independently of any broker-dealer, RIA, or investment-adviser affiliation.
Financial Operations use cases above reflect the AI-driven analytics practice at Touchstone Corporate Finance, a sister practice offering On-Demand, Fractional, Interim, Project-Based, and Virtual CFO engagement models — see cf.touchstonenext.com for scope and team.
How Touchstone deploys agentic AI
The same discipline behind every Touchstone engagement, applied to AI: pick the workflow with the clearest payoff, build it with guardrails, then keep tuning it in production.
Assess
We map the workflow, the data it depends on, and where a mistake would actually cost you something — then pick the use case with the clearest payoff and the safest blast radius.
Build
We wire the system into your existing data and applications, with guardrails and a human checkpoint wherever judgment or licensure is required.
Operate
We monitor accuracy, cost, and exceptions after go-live, and keep tuning the system as your data and constraints change.
Ready to put agentic AI to work?
Tell us which workflow is worth automating first. We'll tell you whether it's an AI problem, a process problem, or both.