for the bhg initiative package

the bhg × arcovo initiative

building bhg's ai capability the way the next five years demand: role-specific ai employees that own real workflows, measured against real outcomes.

This is the full package Arcovo put together for BHG following the April 17 reconnect. It consolidates the opportunities surfaced across our conversation history since March 2025 and frames them the way BHG's internal approvers will want to see them. Every workflow in here is designed to run as a role-specific AI employee, priced as a fixed implementation fee plus a predictable monthly hosting cost, measured against outcomes rather than seat counts. Arcovo runs this as a partnership with BHG, not a vendor relationship.

no either/or with copilot. keep using it for drafting and m365. arcovo covers the cross-system workflows copilot was never built to own.

fixed implementation fee + predictable monthly hosting. priced against hours reclaimed, not seat counts.

tech-agnostic architecture. model, orchestration, and cost structure move with the market — no rip-and-replace in 18 months.

what you will find below

five sections, ordered the way bhg's internal approvers will naturally work through them.

aggregated use case library

The full set of AI employees scoped across the Arcovo x CCMR3/BHG conversation history, organized as a team rather than a single bot.

arcovo vs copilot

Where Arcovo fits alongside the Copilot investment BHG already has, led by the architecture and pricing flexibility arguments from the April 17 call.

client implementations

300+ implementations shipped to date, grouped by use case so BHG can see the pattern against their own operations.

roi framework

The four ROI levers, the metrics to track per AI employee, a payback-math shape, and talking points calibrated to Finance, Operations, IT, and the Executive Sponsor.

resource requirements

What Arcovo handles end-to-end versus what BHG provides, by AI employee, so the initiative package is never a surprise to either side.

01 · aggregated use case library

one continuous thread, march 2025 → april 2026.

a consolidated view of the opportunities arcovo and ccmr3/bhg surfaced across the conversation history. weighted toward what we discussed most recently, with the older thread included for continuity.

13 months of context

the partnership in one line

a team of ai employees, each covering one or two systems end-to-end. not a single bot, and not a ripple-out-to-everything platform. a group of role-specific ai employees deployed where the work actually lives — so each one owns a clear outcome and can be measured the way a human hire would be.

conversation timeline

mar 28, 2025

ai discovery call

first look at specialized collections agents. jacob corlyon walked through loan servicing pain points.

may 16, 2025

ai readiness audit kick off

tim and kyle introduced; focus expanded to administrative process automation.

jul 25, 2025

training ai and voicemail management emerge

two high-signal workflows surfaced — later named riley and vera.

aug 22, 2025

three ai employees proposed: riley, vera, casey

role-play training, voicemail triage, and transcript scoring. room aligned on sequencing and scope.

sep 23, 2025

implementation deep dive

8-week plan walked by ellie, olga, and kyle. jira + livevox access flagged as prerequisites.

jan 27, 2026

chance / jeff connect

bhg acquisition in flux. msa paperwork froze at the parent co. thread stayed warm.

you are here

apr 17, 2026

arcovo relaunch discussion

chance, ellie, and savannah aligned on packaging the work for bhg approval. this document is part of that package.

the core team · three named roles

riley, vera, casey — the three anchored by the april 17 conversation.

each owns a bounded workflow and one or two systems. together they cover the biggest day-to-day drags on the operations side of the house.

ai employee

riley

collector training

simulated borrower conversations so new collectors practice against realistic, randomized scenarios before touching live accounts. reduces ramp time and supervisor coaching load.

flagged by chance on apr 17 as the use case copilot cannot do.

voice or chat-based role-play
scenario library tuned to your call flows
automated scoring against your qa rubric
supervisor dashboard for weak-spot coaching

systems: training environment + qa rubric

surfaced jul 25, 2025 · re-anchored apr 17, 2026

ai employee

vera

voicemail triage

voicemail ingestion through the livevox api with transcription, intent classification, and routing. flags callbacks, disputes, and settlement intent into the right queue so collectors are not listening to the queue all day.

livevox api integration
intent and sentiment classification
prioritized routing to collector queues
supervisor visibility dashboard

systems: livevox + collector queues

surfaced jul 25, 2025

ai employee

casey

transcript scoring & coaching

every recorded call scored against your quality rubric, with automatic coaching notes surfaced to the collector and supervisor. turns qa from a sampling exercise into 100% coverage.

full-coverage call scoring
rubric-based rule checks
trend dashboards by collector and team
coaching moments auto-generated

systems: call recording + qa platform

scoping conversation aug 22, 2025

the adjacent team · process & back office

rounding out the team. one or two systems each.

workflows from earlier conversations that sit alongside riley, vera, and casey.

process automation

case management ai employee

end-to-end case handling where one ai-supported specialist does the work that previously required a team of five. rules-based routing, document assembly, status updates, and escalation triggers.

intake classification and routing
document assembly (disputes, validations)
status updates to jira and crm
escalation triggers with audit trail

systems: jira + crm · 5-to-1 staffing ratio referenced may 16, 2025

process automation

administrative process ai employee

the backlog of repetitive admin work that shows up everywhere: inbox triage, form filling, internal ticket routing, data reconciliation between systems. individually small, collectively enormous.

cross-system data reconciliation
internal ticket classification and routing
form and document auto-completion
exception handling with human-in-the-loop

systems: email + ticketing system · primary focus of may 16, 2025 readiness audit

analytics

borrower communication optimizer

right-channel, right-time, right-tone outreach tuned to payment history and responsiveness patterns. lifts contact rates and conversion without increasing outbound volume.

channel timing model
dialer integration
a/b testing harness

systems: dialer + crm · surfaced across sessions, ready for scoping

on the radar

mentioned, not yet scoped.

natural additions once the first wave is live.

automation

dispute handling assistant

classify incoming disputes, pull supporting documentation, and pre-draft the response so a specialist only reviews and sends.

knowledge

internal policy assistant

natural-language search across procedures and client-specific rules. cuts the time collectors spend hunting for the right answer.

02 · arcovo & microsoft copilot

you already have copilot. this is where arcovo fits alongside it.

copilot is built to make individual knowledge workers faster inside microsoft 365. arcovo builds ai employees that run workflows outside any single vendor's walled garden. this is not either/or. copilot keeps doing copilot things. arcovo covers the agentic workflows that live across livevox, zoho, jira, loan servicing, and the other systems where bhg's operations actually run.

the two things chance liked on the april 17 call

no price lock-in

if you build everything into copilot, you are at microsoft's mercy for token costs, seat pricing, and automation limits. microsoft's ai pricing has shifted three times in eighteen months. arcovo runs on a tech-agnostic orchestration layer, so when pricing moves, you can move with it.

agentic workflows outside the microsoft environment

copilot optimizes what lives inside microsoft: teams, sharepoint, outlook, excel. the workflows we're scoping for bhg live outside that environment — livevox, zoho, jira, the servicing platform, call transcripts, voicemail queues. arcovo was built for the rest of the stack.

the architecture argument

why tech-agnostic orchestration matters over a 24-month horizon.

model flexibility

copilot

runs on microsoft's model partnerships.

arcovo

picks the right model per workflow — claude, gpt, gemini, open source. when the leader changes, we swap. you do not rebuild.

orchestration flexibility

copilot

copilot studio is the only path.

arcovo

built on relevance ai (we are their #1 global partner). can move to n8n, make, stack ai, or native code as a use case demands.

system flexibility

copilot

connectors stop short of owning a multi-system workflow.

arcovo

ai employees live across livevox + zoho + jira + your servicing platform as one flow — not a set of tabs a person coordinates.

cost structure flexibility

copilot

per-seat, forever.

arcovo

fixed implementation fee + monthly hosting tied to the ai employee, not to how many people touch it. scales with volume, not headcount.

a quick side-by-side

not a teardown. just where each tool is designed to win.

capability

microsoft copilot

arcovo ai

drafting, summarizing, and search inside microsoft 365

strong

native, polished, deeply integrated.

not the focus

we would not displace copilot here.

agentic workflows across non-microsoft systems (livevox, zoho, jira, servicing)

limited

connectors exist, end-to-end ownership out of scope.

strong

core competency.

model-agnostic (claude, gpt, gemini, open source)

locked

microsoft model partnerships.

flexible

right model per job, swappable.

orchestration-agnostic (swap platforms as the landscape matures)

locked

copilot studio only.

flexible

relevance ai, n8n, make, stack ai, native code.

pricing model

per-seat license, recurring.

fixed fee + monthly hosting.

tied to the ai employee, not seat count.

individual knowledge-worker productivity inside m365

strong

not the focus

owning a cross-system operational workflow

not what it is for

strong

one call-out on scope: some april-17 use cases — the voice-based role-play trainer for new collectors, voicemail triage through livevox — are genuinely not things copilot is built to do. that isn't the main argument for using arcovo. the main argument is the architecture. the use case gap just shows up naturally when you list what bhg actually wants to automate.

the pushback patterns, answered

a few lines to carry into the internal conversation.

“we are a microsoft shop. let’s just stay inside copilot.”

stay inside copilot for what copilot is good at. individual productivity inside m365 is where it earns its keep. the workflows we are scoping are outside that environment — forcing them into copilot means accepting microsoft’s pricing and model roadmap for work that has nothing to do with office. running both is the norm for our clients.

“adding another vendor adds procurement overhead.”

fair. the trade is that a single-vendor ai stack gives up pricing leverage and architectural flexibility. arcovo’s total spend on a single ai employee is a small fraction of a broader copilot rollout, tied to a specific measurable outcome rather than seat counts across the company.

“we don’t want to lock into a specific ai provider.”

exactly the argument for arcovo. copilot locks you into microsoft’s models, orchestration, and pricing. arcovo is model-agnostic and orchestration-agnostic, so when the leader changes, you swap without rebuilding. the flexibility argument cuts in our direction, not against us.

the recommended posture

one framing you can take into the room.

keep copilot for what copilot is for

drafting, summarizing, excel analysis, meeting recaps inside m365. no disruption.

use arcovo for agentic cross-system work

voicemail triage, call scoring, collector training, case management, anything that spans livevox + zoho + jira + servicing.

keep architectural optionality

tech-agnostic orchestration means no rip-and-replace in eighteen months when the ai market moves.

03 · arcovo client implementations

the use cases bhg is scoping are not hypothetical.

we have shipped ai employees into very busy, operations-heavy businesses. the specifics differ; the pattern of deploying a team of role-specific ai employees is the same.

300+

implementations shipped to date

across the use case categories below, deployed into real operations. fixed implementation fee + monthly hosting per ai employee.

revenue ops

finance

customer ops

call ops

operations

insights

risk

field ops

multi-site

implementations by use case

each is an ai employee — or a team of them — deployed against one or two systems.

billed on a fixed implementation fee plus monthly hosting cost.

revenue ops

proposal and quoting automation

ai employees that assemble proposals and quotes from your pricing logic, past-deal data, and discovery notes. reduces "we should send something" to "it is in their inbox" from days to hours.

revenue ops

rfp opportunity surfacing

ai employees that scan the rfp landscape and surface only the opportunities that are a real fit based on your capability profile and win history.

finance

back-office finance automation

invoicing, accounts receivable, accounts payable, reconciliation, and month-end close across whatever accounting stack you run. moves admin weight off finance without changing the system of record.

customer ops

customer onboarding

intake, document collection, setup tasks, kickoff comms, status updates back to client and team. shorter ramp, fewer dropped balls, consistent experience.

customer ops

client communications

recurring client touchpoints, status updates, renewal nudges, and response drafting tailored to client-specific history. frees account managers for the conversations that actually need them.

call ops

call urgency and importance triage

ai employees in front of inbound and outbound call activity, classifying messages by urgency, topic, and next-best action so the queue is prioritized the moment it lands.

same pattern as vera.

call ops

call performance and management dashboards

full-coverage scoring against your own rubric and rolled-up dashboards so managers can see performance by individual, team, and trend without sampling.

same pattern as casey.

operations

case management and back-office automation

intake, document assembly, routing, status updates, and escalation triggers across multiple systems. same structural pattern scoped for bhg.

same pattern as bhg case management.

insights

data visualization across disparate sources

stitches together data across the spread-out software and sources a business actually runs on, and surfaces it as a single, usable view.

risk

compliance and audit

a complete, queryable record of actions taken across regulated workflows. built to stand up to internal audit, external exam, and "who did what, when."

field ops

field operations and dispatch

quoting, dispatch, and follow-up for field-service businesses where admin drag competes with billable hours.

multi-site

multi-location ops replication

the same ai employee replicated across multiple locations, handling inventory, reporting, and customer-facing communications.

04 · roi framework

four levers. metrics per ai employee. payback math.

a simple structure for telling the roi story internally. populate the baseline before deployment, measure at 30, 60, and 90 days. priced against hours reclaimed, capacity unlocked, and revenue lift — not against seat counts.

the narrative

a team of ai employees, each covering one or two systems, priced as a fixed implementation fee plus predictable monthly hosting. measured against hours reclaimed, capacity unlocked, and revenue lift — not against seat counts. bhg keeps architectural flexibility throughout. arcovo runs this as a partnership, not a vendor relationship.

the four roi levers

every ai employee lands on one or more. pick the levers that resonate.

time reclaimed

hours per week reclaimed from repetitive work, reallocated to higher-value activity. the easiest number to defend and the first finance will ask for.

capacity unlocked without headcount

ability to absorb volume growth (acquired portfolios, seasonal spikes) without a proportional hiring plan. the scaling story.

quality and consistency

error rate reduction, full coverage instead of sampling, faster feedback loops into coaching and supervision. the performance story.

revenue lift

right-party contact rate, cure rate, settlement velocity. harder to attribute cleanly, but turns an operational story into a p&l story.

metrics by ai employee

populate the baseline. measure at 30, 60, 90 days.

a metric grid you can populate with bhg numbers.

vera

· voicemail triage

01 triage sla (voicemail → routed action)
02 auto-handled rate
03 hours reclaimed per week

casey

· call scoring

01 qa coverage (sampling → full coverage)
02 time from call to coaching moment
03 coaching moments per collector per week

riley

· collector training

01 ramp time (weeks to baseline proficiency)
02 first-30-day error rate
03 supervisor hours reclaimed

case management

· ai employee

01 staffing ratio (baseline → target)
02 document assembly time
03 audit-trail completeness

admin

· ai employee

01 hours reclaimed per week
02 exception rate
03 month-end close acceleration

cross-cutting

· every engagement

01 time to first value (sow → measurable outcome)
02 actual vs projected cost
03 internal nps from affected teams

payback math · a simple shape

a frame to populate with bhg's internal numbers.

happy to run this with your finance team under nda.

payback horizon

3 – 6 months

our internal benchmark for a contained voicemail, qa, or training workflow.

cost side

implementation (one-time)

fixed-fee quote per ai employee

hosting (monthly)

fixed · tied to the ai employee

covers technical support, kb updates, model updating, all token + platform costs.

benefit side · three inputs

hours reclaimed

hours/wk × fully-loaded hourly cost × 52

headcount avoidance

ftes not hired over 12 months × fully-loaded cost

revenue lift

contact-rate lift × recoverable $/rpc × monthly volume

talking points

calibrated to the people in the approval conversation.

for finance

fixed implementation fee plus predictable monthly hosting cost. no per-seat creep. roi modeled against hours reclaimed and headcount avoidance, measured monthly.

for operations

arcovo delivers the workflow end-to-end. your team does not build, staff, or maintain the ai. we handle the engineering; you review the output and approve the go-live.

for it

model-agnostic, orchestration-agnostic architecture. deployable in your azure tenant or on-prem. runs alongside copilot, not against it.

for ceo / exec sponsor

how bhg builds the capability muscle now so that as ai keeps shifting, we stay in control of architecture, vendor choice, and cost structure, rather than locked to one provider’s roadmap.

05 · resource requirements

what each ai employee requires from bhg — and what arcovo handles end-to-end.

guiding principle: arcovo owns the build. bhg's involvement is kept to access provisioning, subject-matter review, and go-live sign-off. structured so your team is never the bottleneck.

ai employee / workflow

what arcovo handles

what bhg needs to provide

vera — voicemail triage

full build, livevox api integration, intent classification, routing logic, supervisor dashboard, tuning at 30/60/90 days.

livevox api credentials, sample voicemails for training, one hour of sme time to validate intent categories, go-live sign-off.

casey — transcript scoring

rubric encoding, scoring engine, supervisor dashboards, calibration loops.

current qa rubric, 10–15 calibrated sample calls, qa leader time over the build to calibrate scoring thresholds.

riley — collector training

voice ai employee build, scenario library authoring, scoring against qa rubric, supervisor dashboard, onboarding playbook.

sample scenarios from the training curriculum, training leader time over the build, a pilot cohort of new hires post go-live.

administrative process automation

workflow discovery session, build, integration with existing saas tools, exception handling, monitoring.

read access to source systems, sme time to document current state, sign-off.

internal policy assistant

document ingestion, search layer build, citation engine, teams or intranet integration.

read access to policy corpus, a contact to clarify ambiguous docs, go-live sign-off.

case management workflow

process redesign, intake classification, document assembly, crm and jira integration, escalation routing, audit trail.

named internal process owner, sme time, current-state process documentation, access to jira + crm, operations-side change management support.

dispute handling assistant

classification model, document assembly, response drafting, integration with the dispute workflow system.

access to historical dispute data, response templates, sme time, legal review of ai-drafted responses post go-live.

customer communication optimizer

channel-timing model, dialer integration, dashboard.

historical contact and outcome data, integration work with the existing dialer, sme time.

what bhg should expect from arcovo · regardless of use case

the baseline, across every engagement.

dedicated delivery team

an ai solutions manager (asm) as your day-to-day contact and one or more ai solution engineers (ase) doing the build. your point of contact is consistent from sow through go-live.

bi-weekly check-ins during implementation

a standing 30-minute check-in every two weeks once we are inside implementation. status, blockers, upcoming decisions. we come prepared.

asynchronous work product

decisions, artifacts, and questions delivered in writing between meetings so your team can absorb on their schedule.

full audit trail

every ai action logged and reviewable. security posture documented for your infosec team.

where bhg staff becomes essential

the honest version, in case the initiative package asks.

sme validation

we build fast, but we cannot validate that the ai’s interpretation of an edge case, a dispute category, or a procedure policy matches bhg’s specific posture. a named sme per ai employee, even for just a few hours a week, is the single biggest determinant of quality at go-live.

access provisioning

api keys, read access, sample data. usually the longest lead time in any implementation. getting access started on week one — even if scope isn’t fully locked — saves multiple weeks on the back end.

change management

anything that changes how your team works (case management, dispute handling, communication optimizer) requires an internal owner on the operations side to champion the rollout. we provide the playbook; we cannot drive internal adoption.