Inventory. Revenue. Vendors. Supply chain. Cash flow. Cross-system failures are hiding across all of it. Sign up — your critical intelligence tabs are waiting.
Upload a finance pack; receive a dollar-quantified action list within 60 seconds — no hallucination, full citations.
Generic AI summarisers hallucinate. That is disqualifying for anyone making a real financial decision. DuluthPath takes the opposite approach: the intelligence engine is deterministic at the extraction layer. We find the facts, cite them, and let the AI reason only about their implications — never invent numbers.
Every statement in a DuluthPath brief is backed by a line-and-page reference in the source document. A CFO can drill through any claim to the exact paragraph, row, or cell that generated it.
DSO, DPO, inventory coverage, intercompany AR. Output: a ranked list of cash-release opportunities with dollar impact and owner.
Discount leakage, freight over-spend, supplier price variance. Output: specific leakage events with root cause and dollar value.
26-week SKU-level projection with red/yellow/green bands and auto-recommended PO actions.
Reduced time-to-insight from weeks to minutes. An executive can prep for a board meeting in an hour with a defensible, cited brief, rather than spending three days chasing analysts. And because recommendations are tied to system actions, the path from insight to outcome is measurably shorter.
Trust in enterprise AI is a solved problem if you design for it from the start. Three principles matter. First, separate extraction from reasoning. Extraction is deterministic — rules, regex, and structured-field readers. Reasoning uses a large language model only over already-validated facts. The LLM never invents a number; it only explains or ranks facts that extraction has cited from the source.
Second, every claim carries a citation. When the platform reports "DSO is trending 4 days worse since Q2", you can click the number and drill to the exact ledger rows and aging buckets that support it. No black-box insights. Third, policy gates every automated action. AI recommends; humans approve (or a governed policy approves on behalf of humans below a dollar threshold); systems execute. Every step is logged.
From weeks of analyst prep to minutes of auto-generated, cited briefs. A CFO can prepare board materials the night before, not the week before.
Planners go from reviewing 1,200 POs to reviewing 40 AI-surfaced exceptions. Controllers close books by focusing on the 5% of accounts that need judgement.
Every approved AI action becomes a reusable policy. Over time the platform shifts from "AI suggests" to "governed policy executes" — which is the only way AI scales safely in the enterprise.
Enterprise customers do not evaluate platforms on feature lists alone — they evaluate them on the compliance posture that will be audited by internal risk teams and external regulators. DuluthPath is built to pass those reviews. The platform is SOC 2 Type II and ISO 27001 attested, with GDPR, HIPAA, and PCI-DSS aligned controls available on request. Data residency is configurable at the tenant level — US, EU (Frankfurt and Dublin), UK, Canada, APAC (Sydney, Singapore, Tokyo) — so data never leaves the jurisdiction required by your regulator or your customer contracts.
All customer data is encrypted at rest with AES-256 and in transit with TLS 1.3. Encryption keys can be managed by DuluthPath or customer-managed via bring-your-own-key backed by AWS KMS, Azure Key Vault, or Google Cloud KMS. Identity federation supports Okta, Azure AD, Google Workspace, Ping, and any SAML 2.0 or OIDC provider. Role-based access control is fine-grained down to the field level, with just-in-time elevation for break-glass scenarios and full immutable audit logs streamed to the customer's SIEM of choice (Splunk, Elastic, Datadog, Sumo Logic, Microsoft Sentinel).
We do not view compliance as a sales objection to deflect. We view it as the first thing an enterprise buyer deserves to understand, because it is the thing that determines whether the platform can actually run in production. If your organisation has specific additional frameworks — 21 CFR Part 11, EU AI Act, GxP, FedRAMP Moderate, TISAX — ask us; most of them are already mapped or on the active roadmap.
Every enterprise software purchase is ultimately a financial decision, and the defensible ROI story sits on three pillars: cost reduction, revenue enablement, and risk avoidance. DuluthPath customers typically report cost reductions in three places. First, retirement of 30–50% of existing integration middleware licence spend over two years, as hand-built pipelines are consolidated onto the governed platform. Second, reduction in reconciliation and data-quality FTE hours by 30–45% within the first two quarters as master data and lineage eliminate manual matching work. Third, reduction in external consulting spend, since the governed model and pre-built accelerators remove the "custom integration per acquisition" tax most enterprises pay.
On the revenue side, the most common outcomes are DSO reduction of 5–12 days, recovery of 1.5–3% of revenue previously lost to discount leakage and unresolved deductions, and 10–20% improvement in forecast accuracy (which translates directly into lower safety stock and higher service level). Risk avoidance shows up as faster close cycles (reduces audit findings), better lineage (reduces SOX exposure), and stronger data residency (reduces regulatory risk). Customers can expect a three-year ROI in the 3.5x–7x range on the subscription investment, with a payback period typically inside nine months. We publish an ROI calculator you can configure with your own numbers to sanity-check these claims against your situation.
The TCO conversation is equally important. The sticker price of an enterprise integration platform is usually the smallest part of its real cost. Implementation services, internal team time, infrastructure, ongoing maintenance, and the opportunity cost of delayed business outcomes typically add up to five to ten times the platform licence. DuluthPath's deployment model is designed to shrink those hidden costs — opinionated canonical models, pre-built industry packs, and a small focused implementation team instead of a brigade of forward-deployed engineers. The net effect is a lower three-year TCO than comparable incumbents, even when our list price is similar.
The extraction layer is rule/regex-based, not LLM-based. The LLM reasons only over facts extracted deterministically and must cite every number.
DuluthPath Intelligence for reasoning, Gemini 3 for structured extraction. Customer data is never used to train third-party models.