01
Month-end close & reconciliation
Pull GL, sub-ledger, and bank data, match transactions, and flag variances — draft entries for a controller to approve, never post unsupervised.
Agentic AI in corporate finance & insurance
Agents that work inside close, claims, and underwriting.
We build agents that reconcile ledgers, verify claims, and draft underwriting memos — every figure traced to source, with a reviewer as the final gate.
Trusted by operators
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~20% → ~40% conversion to a final saleThree-agent product advisor guiding customers through print-order configuration.
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Supply chain intelligence agents reducing manual coordination overhead.
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37 kernels ported from CUDA to Intel GaudiLive AI workloads moved to new hardware for on-prem LLM deployment.
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AI agent implementation for a global automotive enterprise.
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2 hrs → 3 min to create a workflowText-to-workflow agents building validated node graphs inside the platform.
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42% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmarkHIPAA-compliant guideline-executing triage — 42% to 98% on the open 50-scenario benchmark.
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Why agentic AI
Corporate finance and insurance run on numbers no one can afford to get wrong
Month-end close, claims adjudication and underwriting all depend on unstructured documents: bank statements, claim forms, submission packets. A rules engine cannot parse them, and a generic chatbot cannot be trusted to summarize them without inventing a figure. The expensive failure here is a quiet one, a plausible-sounding hallucination that lands in a ledger or a claim file before anyone catches it. Pairing retrieval with validation and a hard escalation rule closes that gap: uncertain output stops and waits for a reviewer.
every figure traced to source, nothing posted unreviewed
Use cases
Where agents earn trust in finance and insurance
Recurring workflows across multiple systems — with a reviewer who signs off before anything is final.
02
Claims intake & verification
Cross-check claim details, policy terms, and supporting documents across systems — hours of manual review compressed to minutes before sign-off.
03
Underwriting submission triage
Extract risk factors from packets, loss runs, and statements, then draft a structured memo — unclear cases route straight to an underwriter.
04
Variance analysis & commentary
Compare budget to actuals, isolate material variance drivers, and draft FP&A commentary — same day, not at month-end.
Measured in delivered work
Checked figures from delivered agent systems
Two figures from projects we delivered in other industries: Mixam in print on demand and Schmitt-Thompson in clinical triage. They measure the validation and multi-agent orchestration that finance and insurance workflows need. Each figure links to its case study.
Agents do not post unsupervised journal entries or approve claims. They compress the gathering, cross-checking and drafting that sits before every decision. Each step is logged for review.
95.4%
Success rate for a production multi-agent advisor
Mixam, print on demand. A three-agent product advisor that guides customers through print-order configuration, with 15 tools working against more than a billion product combinations.
42% → 98%
Accuracy lift on the open 50-scenario triage benchmark
Schmitt-Thompson Clinical Content, healthcare. Guideline-executing medical triage: the LLM answers the triage assessment questions and the guideline sets the disposition. It reached 98% on the open 50-scenario benchmark, on par with a 94.4% nurse-panel correct-disposition rate.
Client results
Proof from finance and insurance deployments
We do not yet have a published finance or insurance engagement. These cases are the closest available proof — the same multi-agent orchestration and staged-validation mechanism, shipped in retail, healthcare and media.
A print-order advisor that lifted conversion to a final sale
Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations.
~20% → ~40%conversion to a final sale
96%+web-to-print orders handled end to end
Clinical triage that executes the guideline, step by step
The model answers the assessment questions. The guideline decides the disposition, the same way every time.
42% → 98%accuracy, raw LLM vs guideline-executing
96%weighted F1 across 566 scenarios
Multilingual AI agent-powered chatbot supporting journalist training
A bilingual English/Arabic agent supporting ARIJ Network in training investigative journalists, answering from ARIJ's own knowledge base, in English or Arabic.
EN + ARone agent answering in English and Arabic from ARIJ's own materials
2languages, English and Arabic, from one knowledge base
Delivery path
From workflow audit to a production finance agent
Our process keeps controller and compliance sign-off aligned with engineering, with control risks surfaced before full build commitment.
Scope the close and claims workflow
We audit the target process (close calendar, claims queue, or underwriting pipeline) along with SOX, GDPR, and existing system-of-record boundaries, ranking use cases by impact and control risk.
- Workflow & controls audit
- Data access map
- Prioritized use case
Build, test and validate
We implement against real financial data shapes, with an evaluation suite scored against your own reconciliation rules or underwriting guidelines before any output reaches a reviewer.
- Working prototype
- Evaluation suite
- Escalation rules
Deploy with monitoring and support
Production rollout with monitoring, full audit logging, and a structured handoff so your finance or claims team operates the system independently.
- Production deployment
- Audit trail & monitoring
- Operator runbook
Not sure where to start?
A 30-minute call is usually enough to find your highest-value use case
Talk directly to our founders and PhD AI engineers. We will show you real results from 50+ agentified workflows and walk through how to apply them to your own finance workflows. Every example is something already running in production.
Independence
How we help you stay independent
Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement.
Technological sovereignty
We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe.
Small language models
Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure.
Open source
We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source.
FAQ
Agentic AI in corporate finance & insurance, answered
What is agentic AI in finance?
Agentic AI in finance is software that plans and carries out a multi-step financial task across systems (pulling data, cross-checking it against source documents, and drafting an output) with a human reviewer as the final gate. Unlike a chatbot that answers one message at a time, an agent completes work such as reconciling a ledger or verifying a claim, and escalates when its confidence drops. It differs from analytical AI, which scores and predicts, and from generative AI, which drafts and summarizes.
How quickly do finance and insurance agents pay off?
The return comes from compressing review-heavy work while your team keeps every decision. In healthcare, our guideline-executing validation pipeline for Schmitt-Thompson Clinical Content reached 98% on a curated 50-scenario open benchmark and 96% weighted F1 across 566 expert-validated scenarios. We bring the same staged validation to a reconciliation or claims workflow. We scope the first workflow to reach a measurable result within weeks, then expand from proven value.
Do you work with banks and fintechs, or only corporate finance and insurance teams?
Both. The same auditable, human-in-the-loop pattern applies across financial services — banks, fintechs, asset managers, and insurers. We do not yet have a published banking engagement, but the closest reference is Mixam, where a three-agent product advisor orchestrates against live catalog and order APIs inside one auditable system. Whatever the institution, agents gather and cross-check data and draft an output, with your reviewer as the final gate.
Where does agentic AI actually fit inside corporate finance and insurance operations?
In workflows with a clear decision boundary and a document trail already required for audit — reconciliation, claims verification, underwriting intake, variance analysis. We do not deploy agents to post a journal entry or approve a claim unsupervised. We deploy them to gather, cross-check, and draft, with a controller, underwriter, or claims reviewer as the final gate.
How is this different from a chatbot bolted onto our finance or claims portal?
A chatbot answers one message at a time and holds no state across a task. An agent reasons across multiple sources (a general ledger, a claim file, a submission packet) completes a multi-step task, and escalates when its confidence drops. Our guideline-executing triage pipeline for Schmitt-Thompson Clinical Content (a healthcare deployment) runs staged retrieval and deterministic disposition alongside the model, not instead of it; the same mechanism applies to a ledger or a claim file.
Which corporate finance processes benefit most from agents?
Month-end close and reconciliation, and FP&A variance analysis and forecast commentary — processes with recurring structure, multiple source systems, and a reviewer who signs off before anything is finalized.
Which insurance workflows are actually shipping into production right now?
Claims intake and verification, and underwriting submission triage. Both involve reading unstructured documents (claim forms, loss runs, financial statements) and producing a structured, sourced draft a human can approve quickly instead of assembling from scratch.
How do you handle SOX and GDPR requirements?
Agents operate inside your existing access controls and system-of-record boundaries — we do not stand up a shadow database of financial or policyholder data. Every action is logged, and reasoning is attached to every output, so a controller or compliance reviewer can trace how a figure or a decision was reached.
What happens when the agent is not confident in its output?
It stops and escalates. Confidence thresholds and document coverage gaps route to a human reviewer with the reasoning attached — the same pattern behind the staged validation layer we built for Schmitt-Thompson's clinical triage pipeline (healthcare, not finance); the escalation logic transfers directly.
Can this integrate with our existing ERP, claims, or policy administration systems?
Yes, through your existing APIs and data infrastructure, not a rip-and-replace. Mixam's three-agent product advisor, for example, connects into an existing catalog and order pipeline inside one auditable system, without owning the underlying data — the same integration discipline applies to an ERP, claims, or policy administration system.
Do we own the agent code after deployment?
Yes. Full source ownership of agent logic, integrations, and evaluation harnesses transfers to your team at handoff — no proprietary runtime lock-in on what we build.
Start with one workflow
Automate one close, claims, or underwriting workflow first
A 30-minute call maps the controls, integration points, and a realistic path to an agent your finance or claims team will actually trust.