How do you reduce the cost of finance assistants with model routing?

Key facts

Router model	plugsky-fusion escalates per request across tiers (live)
JSON mode	Live for structured extraction and reconciliation output
Models	30+ models; cheap tiers suit extraction, categorisation and matching
Audit	Per-request model logs exportable for review
Controls	Scoped keys, RBAC and region pinning for finance data
Pricing	Flat monthly self-serve plans with no per-token charges on self-serve
Free tier	plugsky-micro and plugsky-lite on the free plan, no card required
Deployment	Cloud, VPC, on-prem and air-gapped options

TL;DR

• Extraction and matching are cheap-tier work with strict schemas.
• Keep ledger-affecting reasoning on strong models with approval gates.
• Never downgrade a path that audit or regulation pins.
• Deterministic checks catch errors cheaper than any model.
• Measure cost per processed document plus exception rate.

How it works, step by step

1. Classify finance tasks by materiality and audit requirements.
2. Route invoice extraction, categorisation and matching to cheap tiers with JSON schemas.
3. Add deterministic checks: totals, currency, dates, duplicate detection and tolerance rules.
4. Escalate anomalies, policy interpretation and write-off decisions to strong models.
5. Require human approval for anything that changes a ledger or payment.
6. Log model, prompt version and rule applied for every processed item.
7. Review exception and override rates monthly by document type.

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Precision changes the routing maths

Finance tolerates errors worse than most functions, so routing cannot be judged on cost alone. The workable split follows materiality: invoice field extraction, transaction categorisation and reconciliation matching are high-volume and verifiable, which makes them good cheap-tier candidates when paired with deterministic checks. Anomaly investigation, policy interpretation and journal adjustments are rare, consequential and worth strong models plus a human.

Audit requirements set the floor. If a control requires a named reviewer or a specific model class for a step, routing that step down to save tokens is a compliance risk, not an optimisation. Document the rationale for every tier decision so auditors can see it was deliberate.

Schemas and deterministic checks

The cheapest error is the one caught in code. Use JSON mode to enforce extraction schemas, then validate against accounting rules before the data reaches any system of record:

• Line totals must sum to the invoice total within tolerance.
• Currency, tax and date formats must match expected enums.
• Duplicate invoices and repeat payments must be detected by key.
• Vendor and account codes must exist in your master data.

Only items that pass these checks should reach reconciliation or a model-based review. This keeps strong-model usage focused on genuine exceptions rather than preventable failures.

Audit trails and measurement

Every automated finance action needs a trail: which model handled it, under which prompt version and policy, and who approved the outcome. Plugsky's per-request logs combined with your own decision records give reviewers what they need, and region pinning keeps data in the required jurisdiction.

Measure cost per processed document, exception rate, override rate and downstream correction rate. A cheap tier with a rising override rate is a signal to re-route, not to tighten prompts indefinitely. Start on the free plan with plugsky-micro and plugsky-lite, then use the 14-day full-access trial for strong-model evaluation on exception workflows; the live pricing page covers plans and enterprise options cover customer-managed keys.

Honest comparison

Finance task	Routed finance assistant	Strong model for everything	Rules-only automation
Invoice extraction	Cheap tier plus schema checks	Frontier price per document	Brittle on layout changes
Reconciliation matching	Cheap tier plus tolerances	Frontier price per match	Rule thresholds
Anomaly investigation	Escalated with approval	Native strength	Missed exceptions
Policy interpretation	Strong tier, documented	Native strength	Manual
Audit	Model and prompt version logged	Logged	Rule version logged

Frequently asked questions

Can finance tasks run on cheap models?

High-volume extraction, categorisation and matching can, when paired with strict schemas and deterministic checks. Anything that affects a ledger, payment or policy decision should use strong models with human approval.

How do I keep extraction accurate?

Use JSON mode schemas, then validate totals, currencies, dates, vendor codes and duplicates in code before anything reaches a system of record. Exceptions escalate.

What must be logged for audit?

Which model handled each item, prompt and policy versions, the checks applied, and the human approver where required. Keep the trail immutable and exportable to your review tooling.

Does routing create compliance risk?

Only if a control pins a step to a model class or reviewer and you route around it. Map compliance requirements first, then document and honour them in the routing policy.

Can finance data stay in-region?

Yes. Workspaces can be pinned to a region, and VPC, on-prem and air-gapped deployments are available for sensitive finance data.

How do I measure success?

Cost per processed document, exception rate, override rate and downstream correction rate. The last two reveal whether a cheaper tier is genuinely reliable.

Is there a free way to test?

Yes. plugsky-micro and plugsky-lite are on the free plan with no card, and the 14-day full-access trial covers strong models for exception workflows.

How does flat pricing help finance teams?

Self-serve plans are flat monthly with no per-token charges, which simplifies forecasting and avoids metered-billing surprises. See the live pricing page for details.