Test the rate-suggestion engine in real time
Adjust the 3 parameters on the left; the logistic model computes the optimal offer, with no network calls, 100% deterministic.
Click above for an analysis of why these numbers make sense.
Deterministic algorithm · logistic regression with platform priors. See the full maths →
Each surface does one thing, well
Grouped by moment in the flow: what comes in, what gets decided, what goes out and what gets reported. Each card marks whether the AI is LLM (Claude) or det. (deterministic).
Invoice extraction
Drag in a PDF. Claude vision reads supplier, tax ID, totals, IBAN, ATCUD, line items and dates. Returns confidence per field + detected anomalies.
Contract intelligence
Master agreements read by the AI. Extracts payment terms, early-discount clauses, penalties, jurisdiction and red flags for legal.
Supplier de-duplication
Exact match on (tax ID + country), Jaccard on company names, email domain. Suggests merges at upload, avoiding yield fragmented across duplicates.
Spend categorisation
15 canonical categories (COGS, OpEx-IT, Logistics, CapEx…). Rules compiled over vendor name + sector + amount band. Manual override always available.
Rate-suggestion engine
Logistic regression anchored in each supplier's history. Grid-search over (discount, days early) that maximises expected yield × p(acceptance).
Opportunity scanner
Applies the same model to every eligible invoice. Ranked by expected €. Hero card on the dashboard: "€420k of yield available this week".
Acceptance forecast
Each pending offer gets a probability. Aggregate: "8 of 12 projected to accept · €42k of expected yield". Risk surfaced early.
What-if simulator
"What if I raise the target discount to 1.5%?" Interactive sliders · logistic model from your history · projection of yield, acceptance and cash deployed.
Policy recommender
A 4-question mini-flow (industry · AP volume · typical term · risk). Claude proposes initial parameters with a rationale. Deterministic fallback with no LLM.
Risk score on approvals
Each four-eyes offer gets 0–100. 11 weighted factors: sanctions, recent IBAN, outlier amount, new supplier, decline streak, fraud signals. Claude explains why.
Anti-fraud graph
4 SQL detectors: IBAN changes, IBAN collisions across suppliers, threshold-gaming, disproportionate first invoices. Each signal with an idempotent dedup_key.
Alert explainer
Each banner has an "Explain" button → Claude reads the contextual payload and returns 2–3 sentences on the cause + 1–3 actionable recommendations. Server-side cache avoids re-tokens.
Invoice anomalies
Duplicate doc numbers, 10× outliers, tax-ID mismatch, out-of-hours uploads, round figures. Surfaced in the upload modal before promoting to AP.
Treasury pacing
Linear extrapolation over day-of-month vs cap. Urgent banner when the projection exceeds the cap before day 25. Button to adjust the policy.
Supplier health
0–100 score per supplier. Compares the recent 6 weeks vs the previous 6. Trend (improving / stable / degrading / dormant) with visible drivers.
Re-quote signal
When a supplier's accepted median is ≥30bps above what we typically offer, it suggests a re-quote. Yield left on the table, made visible.
Email writer
4 types: concrete offer · re-quote · follow-up · onboarding. PT / ES / EN. Uses the supplier's real history (typical rate, last decline). Editable output.
CFO reports
Quarterly review · CSRD/ESG narrative · weekly audit summary. ~500 words, structured sections, anchored in real data. Board-ready.
Audit-log summary
Weekly summary of platform events for the compliance officer. ~250 words. Identifies changes, risk signals and recommended actions.
Natural-language search
Command palette ⌘K · "invoices due this week above €10k" · "top 10 suppliers by yield". Deterministic heuristics + Claude on the long tail.
Spend insights
Breakdown by category with Δ vs the previous quarter · top vendors · an "Analyse with AI" button produces a 2-3 sentence synthesis identifying the changes.
Review-queue triage
"Approve all with high confidence" in a batch. "Reject suspicious ones" in a batch. The decision is always human, but the AI pre-filters. Audit log preserved.
AI earns its place when a deterministic alternative is genuinely impossible or 10× worse
Most of what looks "AI-shaped" is a SQL aggregation in disguise. The list below is the subset where Claude wins on merit.
LLM only where rules fall short
Unstructured text, narrative multi-factor reasoning, natural-language generation or fuzzy pattern-matching at scale, that is where Claude dominates.
Everything else is logistic regression, SQL rules, moving averages. Faster, cheaper, reproducible.
Every call is auditable
Every call to Claude is logged in the corp_ai_drafts, corp_ai_explanations and corp_ai_narratives tables with the full prompt, tokens consumed and model used.
If a narrative comes out wrong, it is forensically reconstructible.
The human decides, the AI accelerates
The AI suggests, classifies, narrates, never executes autonomous financial decisions.
Creating an offer, approving a payment, changing a policy: all require human confirmation. Decisions above the four-eyes threshold require two distinct approvers.
Deterministic fallback always
Every LLM surface has a fallback path. If the Anthropic key fails, or if you want to disable it, the platform keeps working with heuristics.
Zero vendor lock-in. AI is an acceleration, not a critical dependency.
Single-digit euros a month on LLM
For an active corporate, in practice.
~$0.01 / invoice
~$0.005 / draft
cached server-side
Ready to see AI in production?
5 minutes to create an account · no card · no minimums · AI active from second zero.