Your Underwriters never have to second-guess
Cogentiq Underwriting operates behind the scenes; delivering trusted data quality, risk indicators rules alone can't surface, and sharp insights on every single file.
Your Underwriters never have to second-guess
Cogentiq Underwriting operates behind the scenes; delivering trusted data quality, risk indicators rules alone can't surface, and sharp insights on every single file.
What slows underwriting isn’t risk.
It’s rework
What slows underwriting isn’t risk.
It’s rework
Most submissions aren't incomplete.
They're contradictory, outdated, or missing the evidence that matters.
Your underwriters end up reconciling data and chasing proof instead of doing what they do best,
evaluating risk.
Most submissions aren't incomplete.
They're contradictory, outdated, or missing the evidence that matters.
Your underwriters end up reconciling data and chasing proof instead of doing what they do best,
evaluating risk.
1
Data quality gap
IT systems extract data, but underwriters still reconcile conflicts, chase brokers, and decide what to trust. The process to get decision-grade data remains manual.
1
Data quality gap
IT systems extract data, but underwriters still reconcile conflicts, chase brokers, and decide what to trust. The process to get decision-grade data remains manual.
2
Rule engine blind spots
Rule engines are binary, values just below a threshold pass silently, those just above hit a hard stop. No specific context for the grey areas that matter.
2
Rule engine blind spots
Rule engines are binary, values just below a threshold pass silently, those just above hit a hard stop. No specific context for the grey areas that matter.
3
Post-bind QA lag
QA teams see the portfolio best but review a fraction of cases, after bind. Their insights stay locked in spot checks instead of feeding back as real-time controls.
3
Post-bind QA lag
QA teams see the portfolio best but review a fraction of cases, after bind. Their insights stay locked in spot checks instead of feeding back as real-time controls.
Empowering underwriters with intelligence
at every decision point
Empowering underwriters with intelligence at every decision point
Cogentiq Underwriting sits inside your core systems to make every submission decision-grade, by running explainable conditional decisions, surfacing what rules alone miss, and continuously validating files with complete decision lineage. No rework and no second-guessing.
Cogentiq Underwriting sits inside your core systems to make every submission decision-grade, by running explainable conditional decisions, surfacing what rules alone miss, and continuously validating files with complete decision lineage. No rework and no second-guessing.
How it works, in your workflow
How it works, in your workflow
Four modules carry a submission from clean intake to continuous governance
Four modules carry a submission from clean intake to continuous governance
How Cogentiq UW works ?
How Cogentiq UW works ?
Embed into decisioning at each step of the Core/ workflow system
Embed into decisioning at each step of the Core/ workflow system
1
01
Each module can be deployed independently
2
01
Data Triage, Rule Engine Assistant and QA Bot provide intelligence beyond route processing
3
01
Underwriting Copilot provides intelligence across the process and acts as a tool to raise alerts and do deeper risk analysis.
What improves when you control the decision
What improves when you control the decision
1
Cleaner files. Rationale, evidence, and conditions are structured objects, not scattered across emails and notes.
2
Fewer broker loops. Evidence requests tied to actual gaps, not generic checklists.
3
Lower leakage risk. Credits are proof-gated. No pricing benefit applied without verified evidence.
4
Every case reviewed pre-bind, not a difficult post-bind correction.
Module
Module
1
Data Triage
Data Triage
Transform messy intake into decision-ready data
Transform messy intake into decision-ready data
1
Cross-document reconciliation across ACORDs, schedules, inspections, and loss runs.
1
Cross-document reconciliation across ACORDs, schedules, inspections, and loss runs.
2
All the data from any 3rd party is aggregated, cross-compiled, and summarized in one screen.
2
All the data from any 3rd party is aggregated, cross-compiled, and summarized in one screen.
3
Severity-weighted issue detection: critical, moderate, or low.
3
Severity-weighted issue detection: critical, moderate, or low.
4
Auto-resolution where evidence is clear, with confidence-based reasoning the underwriter can verify or override.
4
Auto-resolution where evidence is clear, with confidence-based reasoning the underwriter can verify or override.
5
Targeted broker emails generated from actual gaps, not generic checklists.
5
Targeted broker emails generated from actual gaps, not generic checklists.
6
Auditable actions at field-level: accept, override, escalate, or route to broker. All from one screen.
6
Auditable actions at field-level: accept, override, escalate, or route to broker. All from one screen.
Module
Module
2
Rule Engine
Rule Engine
Explainable decisions and risk intelligence, with proof-gating built in
Explainable decisions and risk intelligence, with proof-gating built in
1
Get clarity on conditional decisions upfront with pre-drafted override rationale and supporting data.
1
Get clarity on conditional decisions upfront with pre-drafted override rationale and supporting data.
2
Explainable triggers backed by evidence citations, guideline references, and AI-generated underwriting recommendations.
2
Explainable triggers backed by evidence citations, guideline references, and AI-generated underwriting recommendations.
3
Risk intelligence beyond rules by reasoning across financial health signals, boundary conditions, triage escalations, and external alerts.
3
Risk intelligence beyond rules by reasoning across financial health signals, boundary conditions, triage escalations, and external alerts.
4
AI-assisted assessment of appetite position, pricing sensitivity, and recommended next steps.
4
AI-assisted assessment of appetite position, pricing sensitivity, and recommended next steps.
5
Credit proof-gating that holds economics until evidence is verified, thereby ensuring compliance without slowing decisions.
5
Credit proof-gating that holds economics until evidence is verified, thereby ensuring compliance without slowing decisions.
Module
Module
3
Underwriting Copilot
Underwriting Copilot
AI assistance that is pro-active and contextual
AI assistance that is pro-active and contextual
1
Evidence-grounded summaries, so underwriters see what matters without digging through submissions.
1
Evidence-grounded summaries, so underwriters see what matters without digging through submissions.
2
Policy-aligned drafting drawn from your carrier guidelines, not generic templates.
2
Policy-aligned drafting drawn from your carrier guidelines, not generic templates.
3
It only surfaces recommendations based on what the data supports.
3
It only surfaces recommendations based on what the data supports.
4
Consistency guidance from historical treatment patterns, offered as reference, not mandate.
5
Surfaces data readiness for triage, decision logic for underwriting, and lineage analysis for QA.
Module
Module
4
QA Agent
Continuous validation that makes corrections, not just detects compliance issues
Continuous validation that makes corrections, not just detects compliance issues
1
Every case validated pre-bind, across completeness, guideline adherence, evidence quality, data freshness, and compliance.
1
Every case validated pre-bind, across completeness, guideline adherence, evidence quality, data freshness, and compliance.
2
Leakage detection for unverified credits, unmet conditions, and pricing gaps.
2
Leakage detection for unverified credits, unmet conditions, and pricing gaps.
3
Portfolio-level batch analysis with AI-generated insights on compliance, rule triggers, and broker channel risk.
3
Portfolio-level batch analysis with AI-generated insights on compliance, rule triggers, and broker channel risk.
4
Pattern-based control recommendations from recurring defects. QA as prevention, not policing.
4
Pattern-based control recommendations from recurring defects. QA as prevention, not policing.
5
Full decision lineage reconstruction. Who did what, when, using which data, and why.
5
Full decision lineage reconstruction. Who did what, when, using which data, and why.
6
Risk-weighted case prioritization for targeted review.
6
Risk-weighted case prioritization for targeted review.
Not passive Data. Not just Digitization. Expert-level underwriting Decisions at Scale
Not passive Data. Not just Digitization. Expert-level underwriting Decisions at Scale
Where the market stops, Cogentiq continues
Where the market stops, Cogentiq continues
Built for the real work on underwriting desks
Built for the real work on underwriting desks
Insights from leaders
In-media
In-media
Our experts
Onil Chavan
Client Partner - Insurance
Mallesh Bommanahal
Client Partner - Insurance AI Products
Faster decisions. Lower risk. Smarter Underwriting.
Faster decisions. Lower risk. Smarter Underwriting.
Recognition and achievements
Select Fractal accolades
Named leader
Customer analytics service provider Q2 2025
Representative vendor
Customer analytics service provider Q1 2021
Great Place to Work
9th year running. Certifications received for India, USA, UK, and UAE
Recognition and achievements
Select Fractal accolades
Named leader
Customer analytics service provider Q2 2025
Representative vendor
Customer analytics service provider Q1 2021
Great Place to Work
9th year running. Certifications received for India, USA, UK, and UAE
Registered Office:
Level 7, Commerz II, International Business Park, Oberoi Garden City,
Off W. E. Highway Goregaon (E), Mumbai - 400063, Maharashtra, India.
Phone: +91 22 6850 5800
Email: investorrelations@fractal.ai
CIN : L72400MH2000PLC125369
GST Number (Maharashtra) : 27AAACF4502D1Z8
Registered Office:
Level 7, Commerz II, International Business Park,
Oberoi Garden City, Off W. E. Highway Goregaon (E),
Mumbai - 400063, Maharashtra, India.
Phone: +91 22 6850 5800
Email: investorrelations@fractal.ai
CIN : L72400MH2000PLC125369
GST Number (Maharashtra) : 27AAACF4502D1Z8
