• OCR built for finance and legal teams: Our proprietary models read scans, tables, footnotes, and redlines. We detect insertions/deletions and capture table structure so you don’t lose context. And the downstream LLM is able to see page and table layouts like an analyst would – this allows it to extract all data points with 100% confidence.

Image 1: Accurate table detection and parsing: The system is able to identify specific clauses within tables contained in an RMBS prospectus

• ABS‑tuned hybrid retrieval (keyword + semantic): We developed a domain model that blends exact‑match keyword retrieval with semantic search. In our tests on an internal ABS benchmark corpus, our retriever delivered an approximate 10‑point lift in retrieval metrics compared to leading general‑purpose baselines, including OpenAI models.
• Automated question rephrasing: Our system expands and rewrites user questions into ABS‑aware variants to boost recall, precision, and coverage – without extra work for the analyst.

Image 2: Beyond Ctrl+F: The system rephrases user questions and matches the semantics of the document. Top: an appraisal report use case; bottom: an auto receivables transaction use case.

• Low‑hallucination prompting: We use a grounding strategy that prioritizes citations and refusal when the answer isn’t in the docs. On a challenging real life ABS corpus, including for instance scanned financial statements and appraisal reports, we achieved near perfect extraction accuracy on all the relevant tables and data points.

Image 3: Citations and cross referencing: At the bottom, the Cardo AI system correctly cites the section and table cells where it retrieved the information, cross checking the correctness of the section name with a Fees and Expenses summary table from an auto receivables transaction prospectus.

Image 4: Refusal when missing in the text: When asked questions whose answers would not be grounded in the text, instead of hallucinating, the system correctly refuses to answer, motivating its refusal. An RMBS use case.

• Definition intelligence: Our named‑entity engine flags defined terms, locates where they’re defined across the corpus, and provides a grounded summary of meaning and implications in plain English.
• Team question recommendations: Unsupervised signals surface the most popular questions your teammates ask, sharing knowledge, accelerating onboarding and standardizing reviews.
• Templated extraction: Users can automate retrieval at scale with custom templates and receive structured outputs mapped to their own fields or data dictionary – ready for downstream systems.
• Full traceability: Every answer links back to the most relevant page and paragraph so you can verify in seconds. Moreover, unlike general purpose RAG systems, the Cardo AI platform correctly cites source sections and paragraphs.
• Bulk extraction via API: We offer programmatic access to ingestion, hybrid retrieval, Q&A with citations, definition deep-dives, and template‑driven extraction for portfolios at scale.
• Enterprise grade framework: Leverage best in class LLM models in a secured and optimized framework: we are not competing with major LLMs, we are putting them on steroids thanks to our ABF specific framework, delivering enterprise level security and data management.

Overall, our document intelligence platform substantially reduces hallucination rates – often to zero – outperforming general-purpose, state-of-the-art standalone LLMs and RAG systems that we benchmarked and found fell short of our quality standards. To date, the framework achieved a 95% live accuracy on a range of documents spanning from ABF contracts and real estate reports, to invoices and financial statements.

• Due diligence: Pull covenants, triggers, eligibility criteria, reps & warranties, cure periods.
• Ongoing surveillance: Track definition drift across vintages and issuers; monitor triggers and performance tests.
• Operations and servicing: Locate notice requirements, waterfalls, and servicing standards without combing through hundreds of pages.
• Financial analysis: Extract metrics, footnotes, and financial results from financial statements with table‑aware OCR.
• Valuations: Parse appraisal packages to capture comparables, adjustments, and assumptions consistently.

• Faster reviews, fewer misses: From hours to minutes with answers you can audit.
• Consistency at scale: Standardize what “good” looks like across analysts and portfolios.
• Built for ABS nuance: Reads the structures, definitions, and redlines that drive real risk.

Get results you can cite and safely integrate in your high-stakes downstream applications. Ready for a quick demo or a sample extraction on your documents?

• Tailor made models: Train your own retrieval models on proprietary documents. As few as 5–10 unlabeled documents are enough – while still enforcing strict data segregation – to achieve state‑of‑the‑art performance on your data, saving time iterating to get to the data points you need. We’re bringing this self‑serve capability to the UI and API so teams can spin up private retrieval models on demand.
• Standalone table extraction and data correction: not just full legal documents. Soon we’ll offer the OCR+LLM interface as a standalone feature for small documents like balance sheets and financial statements. For instance, this will allow users to save manual data-entry time and reduce mistakes when computing and monitoring deal covenants by automating the full process.