Home / Services / AI Agents

Copilots answer.
Agents act.

An AI agent is software that takes goals and gets work done. It looks up information across your systems, drafts communications, updates records, makes decisions, and escalates to a human when the stakes call for it. This is where AI stops being a chatbot and starts being a coworker.

Done right, agents reduce manual effort, surface exceptions, support better decisions, and involve people when judgment is required.

← Back to all services
Three places agents are working today

Where mid-market teams put agents first.

These are the three workflows where agents earn their keep fastest. Skim what's possible, then go deep on the scenario that matches what's slowing your team down.

/ 01 →
Sales agent
Qualifies inbound leads, drafts personalized first-touch outreach, routes responses with full context, and updates the CRM. Your reps spend their time closing, not chasing.
Lead qualification & outreach
/ 02 →
Purchase order agent
Reads incoming POs (PDFs, scans, emails, even handwritten faxes), validates against your catalog and pricing, and pushes clean orders to your ERP. Exceptions route to humans.
Order processing & fulfillment
/ 03 →
AR collections agent
Watches aging invoices, gathers customer context, sends routine reminders automatically, and escalates the judgment calls to a controller with a drafted message ready to send.
AR & cash flow
Why this matters

Most agent projects fail. Here's why.

The flashy demo is easy. Building agents that actually work in production (the kind that handle the messy reality of your data, your edge cases, your compliance constraints) is the hard part. The graveyard of failed agent pilots is full of demos that worked great until they touched real operations.

What most agent projects look like
Demos that never reach production.
Polished pilot connected to test data. Beautiful conversation. No system permissions. No audit logs. No idea what happens at scale. No story for how it integrates with the ERP everyone actually uses. Three months later: quietly shelved.
What we build instead
Agents that survive Monday morning.
Plugged into your real data foundation. Real tool permissions and audit trails. Human checkpoints on high-stakes actions. Measurable evaluation harnesses. A path from "agent #1 in production" to "agent fleet running operations," without the cliff.
Three scenarios

What real agents do.

Here are three practical agent workflow scenarios for mid-market companies using the Microsoft stack. Each example is grounded in real operational challenges and shows what becomes possible when data, systems, reporting, and automation are connected through the right foundation.

These scenarios are not just about agents. They are about building smarter operating workflows that reduce manual effort, surface exceptions, support better decisions, and involve people when judgment is required.

01.
Sales · Lead qualification & outreach

The inbound lead the sales team never gets to.

The scenario: a B2B SaaS company doing $20M ARR with two inbound sales reps. They generate 400 leads a month from forms, demos, and content downloads. The team can meaningfully follow up on maybe 80. The other 320 sit in HubSpot getting one generic email and dying. Their best lead this quarter went to the trash because nobody got to it before Wednesday.

How the agent works
/01
Lead arrives in HubSpot from a "request a demo" form. Agent triggers within seconds.
/02
Enrichment. Agent pulls company data from Clearbit and LinkedIn, checks the existing CRM history (have we talked before? what stage? any prior reps?), and looks up the contact's published role and signals.
/03
Qualification scoring. Against your defined ideal customer profile (company size, industry fit, role seniority, intent signals), the agent produces a fit-and-intent score with reasoning the rep can audit.
/04
Personalized outreach draft. For qualified leads, the agent drafts a personalized first-touch email that references something specific about their company, the page they came from, and a clear next step. Human in loop
/05
The rep reviews and sends from their own inbox, or hits "send" in Teams without leaving their flow. Lower-fit leads get an automated nurture sequence with measurable engagement signals.
/06
Follow-up cadence. If the lead doesn't respond in 4 days, the agent drafts a follow-up. If they reply, it categorizes the response and routes to the rep with full context. No manual logging. CRM updated automatically.
The outcome
Sales reps spend their time on conversations, not on triage. Every qualified lead gets a personalized first touch within minutes of arriving. Lower-fit leads get nurtured properly instead of ignored. The pipeline grows because no one gets dropped. The team feels less like a triage center.
02.
Operations · Order processing & fulfillment

The order that arrives as a PDF.

The scenario: a B2B distributor that receives 60% of its purchase orders by email: PDFs, scanned forms, and even photos of handwritten faxes from long-time customers. Two ops people spend 20 hours a week each manually keying these into the ERP. Errors are normal. Delays are normal. And growth is being capped by how fast humans can type.

How the agent works
/01
PO arrives in a monitored shared inbox. Agent picks it up immediately, whether it's a clean PDF, a scanned image, or an email with line items in the body.
/02
Document understanding. Agent extracts the customer, PO number, line items (SKU, quantity, price, ship-to), payment terms, and requested delivery date. Confidence-scored at every field.
/03
Validation. Agent checks customer master data (correct account? credit status?), pricing against agreed contract terms, and inventory availability for each SKU.
/04
Auto-create in ERP for clean orders meeting all validation rules. Order confirmation drafted and sent. ETA calculated and communicated to the customer.
/05
Exceptions routed to humans: pricing mismatches, low-confidence extractions, unfamiliar customers, inventory shortfalls. Routed with context, suggested resolution, and a one-click approve workflow. Human in loop
/06
Continuous learning. Every human override teaches the agent. Same customer's PO format next month gets processed cleanly without intervention.
The outcome
Order entry stops being a bottleneck. The ops team moves from keying data to resolving exceptions. Higher-judgment work, fewer hours, lower error rates. The business can take on a larger customer without hiring two more order entry people. And finance has clean data flowing into the ERP from the moment the PO arrives.
03.
Finance · AR & collections

The DSO that's been creeping up for a year.

The scenario: a professional services firm with 200 active clients, billing monthly. DSO has crept from 38 days to 62 over 18 months. The controller can name maybe a dozen accounts that are chronically late. The other 30+ are silently aging because nobody has time to follow up systematically. Every dollar of unpaid AR is a dollar the line of credit is paying interest on.

How the agent works
/01
Daily AR sweep. Agent pulls the aging report, identifies invoices crossing dunning thresholds (15, 30, 45, 60 days past due), and groups by customer.
/02
Context gathering. Pulls relationship history: payment patterns, prior disputes, account size, account manager. Determines whether this is a chronic late payer, a one-off, or a new pattern.
/03
Drafts the right message. A friendly nudge for a first-time 15-day-late customer reads completely differently than a firm follow-up for a chronic 45-day-late account. Same agent, different tone, same brand voice.
/04
Routine collections sent automatically: first reminders, second reminders, statement copies. CFO sees a daily summary in Teams.
/05
High-value or sensitive accounts escalate to the controller or account manager with a drafted message and full context. They decide whether to send as drafted, edit, or pick up the phone. Human in loop
/06
Replies categorized and routed. "Payment sent yesterday" goes to AR reconciliation. "We have a dispute" goes to the account manager. "Can we set up a payment plan?" goes to the controller. Nothing gets lost in an inbox.
The outcome
Collections becomes a systematic process instead of "whatever the controller has time for this week." DSO trends down because the long tail of moderately-late accounts is being worked, not just the squeaky wheels. The CFO has visibility into AR risk in real time. And the controller's time gets reinvested into the conversations that actually require judgment.
← Back to all services
Production anatomy

What it actually takes to build one that works.

The model is maybe 10% of the work. The other 90% is the infrastructure around it. The part nobody talks about in the keynote demos. Here's the operating model we build for every agent we put into production.

Anatomy of a production business agent A business agent sits at the center, connected to business context inputs above (data, permissions), governance outputs below (audit, governance review, cost and performance), and control checkpoints on each side (escalation to humans, quality monitoring). BUSINESS CONTEXT · INPUTS / 01 Connects to your business data Fabric, SQL, ERP, CRM, SharePoint Not public training data / 02 Works within approved permissions Defined actions, roles, limits, approval paths / BUSINESS AGENT Reads. Reasons. Acts. Escalates. / 03 Escalates to people when needed Approvals, exceptions, judgment / 04 Monitors quality continuously Accuracy, exceptions, drift / 05 Creates an audit trail Actions, decisions, approvals, exceptions — all captured / 06 Supports governance review Clear accountability when exceptions occur / 07 Measures cost and performance Time saved, cycle time, error reduction, ROI CONTROL · VISIBILITY · ACCOUNTABILITY

A useful agent is not just a chatbot. It is a governed workflow connected to real business data, approved actions, human checkpoints, and measurable outcomes.

How we work

Crawl. Walk. Run.

Nobody buys an "agent platform" on day one. The path that actually works: prove value with one focused agent, then expand the playbook to two or three more, then build the fleet. Each stage earns the next.

Stage 01 · Crawl

One agent. One workflow. Real production.

Focused build · single use case

We pick one workflow with clear ROI and a clean data path. Scope it tight. Build it right, with the full production anatomy, not a demo. Ship it to a small group, measure it, iterate.

You end with one agent in production, an evaluation framework that proves it's working, and the institutional muscle for everything that follows.

Stage 02 · Walk

Two or three agents. Patterns emerge.

Expanded build · adjacent workflows

With one agent proven, we build the next two or three, usually in adjacent workflows that share infrastructure. The second agent costs less than the first because the foundation is already there.

You end with a small fleet of production agents, reusable patterns for the next ones, and a team that understands what they need to keep this growing.

Stage 03 · Run

An agent fleet. Running the business.

Operational AI · ongoing evolution

Five, ten, twenty agents handling distinct parts of operations. Coordinated where they need to be, independent where they don't. A platform team (yours, with us in advisory) keeping them tuned, evaluated, and evolving.

You end with AI infrastructure that genuinely runs work, not just answers questions. The kind of moat that compounds.

What's slowing your team down?

Send me the system, report, or process that's slowing your team down. I'll help identify whether it's a data, automation, integration, or AI problem, and tell you honestly whether agents are the right move or whether you need to fix something underneath first.

Tell me what's slowing your team → ← Back to services ← Back to home