Manual dialing, all day
Agents spend their shift working down a list — re-reading notes, re-checking balances, re-dialing numbers — instead of resolving the accounts that actually need a person.
Solutions · First-party collections
First-party collections that work the case before you open it
The moment a payment is missed, the case opens itself with full context. AI agents call, draft, negotiate, and follow up within guardrails. Every contact clears compliance before it executes. And when the promise is kept, the case closes itself — with the evidence to prove every step.
The signature flow
System
Payment missed — the case opens itself
Compliance Gate
Reg F budget checked — 2 calls remaining
AI Agent
AI calls, negotiates within guardrails
System
Promise kept — case auto-closes
Every step gated before execution and hash-chained — full walkthrough below
The status quo
Collections is run on adrenaline and spreadsheets
Most first-party shops are a dialer, a policy binder, and a lot of hope that the two agree. The work is manual, the risk is invisible until it isn't, and the people carrying both burn out.
Compliance as fear
Reg F budgets, calling windows, consent, protected states — tracked in spreadsheets and tribal knowledge, audited after the fact. Every dial carries a quiet question: was that one allowed?
Agent burnout
High-volume, low-judgment work drives turnover, and turnover drains the experience your hardest cases need. The team you keep spends its time on the work that needs it least.
End to end, hands off
One missed payment — from delinquency event to auto-closed case
Follow a single account through the whole loop. Watch for three things: the compliance gate fires before actions, AI and human actors are explicit at every step, and every transition leaves an evidence record.
- System
Payment missed — delinquency event streams from the core
No nightly batch, no morning report. The lending core emits the delinquency event the moment it is true on the ledger.
Evidence: Delinquency event with the ledger facts that triggered it.
- System
Case auto-opens with a full context snapshot
Balance, due dates, payment history, prior contacts, consent, channel preferences, and any protected-state flags — captured at the moment of opening, not reassembled later.
Evidence: Context snapshot pinned to the case record.
- Compliance GateAllowed
Contact budget checked before any outreach is even drafted
Reg F 7-in-7 call budget computed per debt in real time — voicemails and limited-content messages count. Borrower-timezone calling windows, consent, and protected states are evaluated too.
Evidence: Budget evaluation with the exact rule versions consulted.
- AI Agent
AI agent drafts the outreach
Every figure comes straight from the ledger. The model writes the words; the ledger supplies the numbers — and the channel can be an AI voice call the agent conducts itself, behind the same execution-time gate as any send.
Evidence: Draft, grounding facts, and model and prompt versions.
- Compliance GateAllowed
Compliance gate re-checks at execution time
Five possible outcomes: allowed, warning, approval-required, blocked, or missing-facts — and missing facts never permit. The check runs at send time, not draft time, so nothing slips through a stale evaluation.
Evidence: Decision record listing every rule consulted and its result.
- Human
Human approves the borrower-facing message
Required in early autonomy levels. As trust graduates, this step becomes autonomous within policy — and the gate still runs either way.
Evidence: Approval, approver identity, and the autonomy policy in force.
- AI Agent
Borrower engages — AI negotiates a promise to pay within guardrails
On the phone or in the thread, bounded by policy: a maximum 30-day horizon, a minimum amount, a maximum number of installments. Hardship language routes to a hardship case, the borrower can ask for a human mid-call, and anything out of bounds routes to a supervisor instead of being agreed.
Evidence: Call recording and negotiation transcript plus every guardrail check.
- System
Promise recorded, follow-up scheduled automatically
The promise becomes a first-class object the platform tracks against the ledger — no sticky note, no tickler file.
Evidence: Promise terms and the scheduled follow-up.
- Ledger
Payment in flight — value-dated, no late fee
The payment is value-dated to its authorization date. While ACH settles, the borrower never shows late, late fees are suppressed, and collections stand down. The promise sits in pending-evaluation until funds clear.
Evidence: Value-dated entry and the suppression record it triggered.
- System
Promise kept — case auto-closes
Promises evaluate automatically against ledger facts: kept, partially kept, or broken. A kept promise on a cured account closes the case with a system actor, under guardrails — no human had to remember to.
Evidence: Closure decision and the complete hash-chained evidence graph for the case — exportable for exams.
Division of labor
What the AI does, and what your team does
AI agents are governed workers, not a chatbot on the side — same queues, same permissions, same audit trail as your people. The split is by judgment required, and you decide where the line sits.
The AI agent
- Places and answers collections calls — AI voice agents negotiate promises to pay by phone, inside policy bounds: horizon, minimum amount, installments
- Recognizes hardship language mid-call and routes it to a hardship case — with a warm handoff to a human whenever the borrower asks or policy requires
- Drafts every outreach with figures rendered from ledger facts — spoken figures included
- Reads replies, classifies intent, and recommends the next action
- Schedules follow-ups and keeps the case timeline moving — call recordings and transcripts land there too
- Escalates anything out of bounds instead of improvising
Your team
- Approve borrower-facing content in early autonomy levels
- Take the conversations that need a person — hardship, disputes, complexity
- Approve out-of-bounds promises as supervisors
- Set policy: guardrail bounds, autonomy levels, escalation paths
- Review the manager view: queues, SLA health, escalations
The guardrails
Autonomy you can defend to an examiner
Every bound is explicit policy, every check runs before execution, and every decision is recorded — collections compliance as architecture, not aspiration. This is what lets AI do real collections work without anyone holding their breath.
Promise-to-pay bounds
- Maximum horizon: 30 days out, no further
- Minimum amount as a percentage of what is owed
- Maximum number of installments
- Out of bounds? Supervisor approval — the AI cannot agree on its own
Promises then evaluate themselves against ledger facts: kept, partially kept, or broken — pending-evaluation while a payment is in flight.
Per-debt contact budget · trailing 7 days
2 of 7 calls remaining
Attempt countedRemainingVoicemails & limited-content messages count
Computed per debt, in real time, before every attempt — by anyone, human or AI. Illustrative view; the budget also enforces borrower-timezone calling windows, consent, and protected states.
Execution-time gate
Every send and every dial is re-checked at the moment of execution. Five outcomes, and only one of them lets the action through:
AllowedWarningApproval requiredBlockedMissing facts — never permits
Missing facts never permit. If the platform cannot prove an action is allowed, it does not happen.
The manager view
Run the floor, not the firefight
Managers see one command center across human and AI workers: queue depth, SLA health, escalations, and approvals — with every case carrying its own context.
SLA health at a glance
Which cases are aging, which queues are backing up, which promises come due today — without pulling a report.
Smart queues
Work routes by skill, capacity, and case state. AI agents and humans draw from the same queues with the same permissions and the same audit trail.
Escalations and approvals
Out-of-bounds promises, gate warnings, and approval-required actions land in one place — with the full context snapshot attached, not a case number to go look up.
What changes
The outcomes this is built for
These are the outcomes the architecture is built to produce — and what we measure with every design partner.
More accounts cured
Every delinquency is worked from the moment it happens — consistently, in policy, without waiting for a queue to be triaged by hand.
Fewer violations
Compliance runs before the action, not in next quarter's audit. An attempt that would breach a budget or a window simply does not execute.
Agents on exceptions, not dialing
The repetitive middle of collections is automated under guardrails, so your team's judgment goes where judgment is needed.
FAQ
First-party collections, answered
Yes — governed AI voice agents place and answer collections calls, hardship conversations included. Every dial clears the compliance gate first: contact budget, calling window in the borrower's timezone, consent, protected states. On the call, every figure the agent speaks comes straight from the ledger, promises to pay stay inside your guardrails, hardship language routes to a hardship case, and the call hands to a human the moment the borrower asks or policy requires. Every call is recorded and transcribed into the case's evidence trail. Autonomy is graduated — voice can start with reminder and early-delinquency calls and expand on your evidence — and you can pause AI activity instantly.
Watch a delinquency resolve itself, end to end
Bring a real scenario from your portfolio. We will walk it through the case lifecycle, the guardrails, and the evidence graph with the founding team.