T7 builds underwriting copilots that read the file, cite the guideline and draft the first-pass decision — while keeping the human underwriter in charge.
Senior underwriters spend hours per case on document review; junior throughput is limited by training and consistency.
Regulators want every decision explainable — most AI tools give a score, not a reason.
The copilot ingests the submission (application, financials, medical, prior decisions), retrieves relevant guidelines and comparable prior cases, and drafts a decision note with reasoning and citations.
ML-based risk scores ship alongside SHAP-based reason codes so underwriters and regulators see why.
Every decision has a full audit trail from source doc to final call.
OCR + LLM extraction on submission documents; structured facts feed the underwriting engine.
Risk model produces a score with reason codes; RAG pulls guideline and precedent decisions.
Copilot drafts decision note with citations; underwriter approves, edits or overrides.
Production-grade GPT, Claude, Gemini and open-source LLMs — grounded in your data.
Explore serviceExtract structured data from invoices, forms, IDs and reports with 95%+ accuracy.
Explore serviceProduction ML for forecasting, churn, risk and pricing — trained on your data.
Explore serviceNo. Every decision is human-in-the-loop; the copilot drafts and reasons, the underwriter approves.
Protected-attribute testing pre-launch and continuous drift monitoring on fairness metrics post-launch.
Yes — Guidewire, Duck Creek and custom PAS via API/EDI.
Industries where this workflow ships, the insights behind it, and the tradeoffs to weigh.
Underwriting, claims and fraud AI
AI-powered healthcare software
AI copilots for law firms and in-house teams
A field report on where AI is really deployed in banks, insurers and NBFCs — and where it's still theatre.
Perplexity, ChatGPT Search, Google SGE and Copilot are becoming the new front page for B2B buyers. Here's how enterprises make sure their products, docs and thought leadership are quoted — not skipped — by LLM-driven answer engines.
How OpenAI's GPT models compare to Anthropic's Claude for enterprise workloads
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