The answer may sit between provisions.
A provision can create exposure when read with a definition, exception, basket, or release term.
Behavioral infrastructure for AI systems
AI without rules creates chaos.
DAC turns agreements into rules AI can follow.
Agreements define what people, software, and AI systems may, must, and must not do. DAC makes those rules usable by AI while keeping every result tied to its source and human authority.
ClauseFlow · Institutional credit
The answer is rarely in one provision.
One provision may grant a right. Another may limit it. A definition, exception, basket, calculation, or amendment elsewhere may change the answer. ClauseFlow follows those provisions together and links each finding to the cited language.
Every material finding remains linked to the agreement version and cited provisions and remains subject to expert review.
AI can find the right text and still miss the answer.
Finding a provision is not the same as following everything that changes its effect.
ClauseFlow shows the provision path.
Inspect how the relevant provisions work together and the cited language behind each finding.
Why DAC exists
Agreements already define the rules.
They set authority, conditions, boundaries, consequences, and remedies.
As AI systems join human networks, they need a usable form of those rules. DAC preserves the source and human authority behind each decision.
May the drill engage at these coordinates?
Conditional · §4.2
The drill may engage because the coordinates fall within the authorized extraction zone and torque, thermal, and power conditions remain inside mission limits.
Agreement
system
system
Run-to-run consistency
Same agreement. Same question. AI alone may take a different path each time.
An AI model rereads and reasons over the agreement on each run. ClauseFlow follows the same saved provision path, keeping every finding tied to the same cited language for review.
AI aloneRUN 01 / 03
Yes, the agreement appears to permit it.
ClauseFlowRUN 01 / 03
Conditional: only if capacity remains. No separate consent condition appears in the cited provision path.
What ClauseFlow adds
Turn agreement review into reusable, cited analysis.
ClauseFlow expands the terms and interactions a team can review, brings cited evidence forward sooner, and preserves findings across the portfolio.
Agreement elements mapped
Track vulnerabilities, protections, blockers, calculations, and provision paths across each agreement.Broader agreement coverage
Compared with the seven to nine headline issues commonly prioritized in manual review.Cited first-pass findings
Bring cited analysis forward before the transaction, amendment, trade, or legal queue moves.Searchable agreement record
Create a searchable record of negotiated terms, protections, provision paths and precedent across the agreements under coverage.How DAC works
Turn a credit agreement into a connected, reviewable analysis.
ClauseFlow connects the provisions that shape an answer and lets the reviewer check it against the agreement.
The same source-linked structure supports questions, calculations, updates, and approved workflows without rebuilding the agreement for every prompt.
Governing source
Agreement + version
Rights + obligations
Conditions + exceptions
Representations + warranties
Events + remedies
Resolved stateCovenants + restrictions
Improve existing AIReuse the workAPI / MCP
Every result links back to the agreement, clause path, and version.
Built for institutional credit.
Credit decisions depend on how the agreement is read.
ClauseFlow supports teams that originate, underwrite, manage, trade, amend, and restructure corporate credit.
CLO and leveraged-loan managers
Query protections, provision paths, concentrations, amendments, and precedent across large loan portfolios.
Private-credit and direct-lending platforms
Test negotiated protections, sponsor requests, amendment language, structural capacity, and accepted precedent.
Credit-focused and multi-strategy asset managers
Connect downside underwriting, relative value, distressed analysis, special situations, and legal diligence.
Banks and dealer platforms
Support leveraged finance, loan capital markets, trading, restructuring, legal, and risk workflows.
ClauseFlow
Follow the provisions behind the answer.
ClauseFlow connects each provision in the chain, shows the interaction visually, and links the result back to the agreement.
ClauseFlowcited agreement analysis
Ready for a questionProject Helix Credit Agreement · 412 pages
Project Helix Credit Agreement
The Borrower may designate any Restricted Subsidiary as an Unrestricted Subsidiary if, immediately after giving effect, the designation is permitted as an Investment under Section 6.04.
Investments may be made in an aggregate amount not to exceed the greater of $500,000,000 and 26.0% of Adjusted EBITDA, subject to the conditions stated herein.
Upon any Subsidiary becoming an Unrestricted Subsidiary, the Liens on its property shall be automatically released without further action by any Secured Party.
Agreement
Credit agreement
document set
Conditional right
Designation permitted?
§5.13
Capacity test
Capacity remains?
§6.04(q)
Automatic effect
Lien release automatic
§9.09(d)
Missing condition
If consent were required
counterfactual protection
Potential exposure
Potential collateral release
Ask a question…
Product preview uses a fictional agreement and an illustrative scenario. Every answer remains subject to expert review.
Formula Composer
See how one definition changes the calculation.
Definitions, baskets, ratios, and covenants depend on one another. Change one input to see its effect on capacity, leverage compliance, headroom, and the provision path.
Every supported number remains linked to the clause behind it.
Formula Composerequations extracted from the text · chained · computed
§1.01 · Definitions
Adjusted EBITDA means EBITDA for such period plus, without duplication, cost savings and synergies (uncapped)…
Eadj = E + ∑i Ai
CalculationAdjEBITDA = EBITDA + Σ Addbacks
Used in
§6.04(q) · Investments
…Investments not to exceed the greater of $500,000,000 and 26.0% of Adjusted EBITDA…
B = max( 500, 0.26 Eadj ) $M
CalculationBasket = max($500M, 26% × AdjEBITDA)
Used in
§7.11 · Financial covenant
…shall not permit the First Lien Net Leverage Ratio to exceed 3.50 : 1.00…
λ = DnetEadj ≤ 3.50
CalculationFLNLR = NetDebt ÷ AdjEBITDA ≤ 3.50×
E · EBITDA$1,840Minput
∑A · Addbacks$260Minput
Dnet · Net debt$6,900Minput
Eadj · Adj EBITDA$2,100M§1.01
B · Basket$546M§6.04(q)
λ · FLNLR3.29ק7.11 · cap 3.50×
⊢ compliant · 3.29× ≤ 3.50× · incremental headroom $450M
Each input and calculation links to its cited provision.
Across the portfolio
See where the same exposure repeats across the portfolio.
See whether an exposure is isolated, recurring, or concentrated.
Review priorities
Agreements ranked by material harm and compounding exposure paths
01
Asteron Consumer HoldingsSenior Secured Credit Agreement · 2024
3 critical findings02
Meridian Energy PartnersFirst-Lien Credit Agreement · 2023
Compounding03
Harborstone MaterialsCredit Facilities Agreement · 2024
2 high findings04
Northline Retail GroupSecured Notes Agreement · 2022
1 critical findingSeverity by agreement
Where harm concentrates across the book
Asteron Consumer2 C · 8 H · 3 M
Meridian Energy1 C · 4 H
Harborstone Materials2 H · 2 M
Northline Retail1 C · 1 H
Hearthland Home1 M
Recurring exposures
Exposure patterns repeating across agreements
Collateral release6 / 13
Uptier priming5 / 13
Incremental debt5 / 13
Sacred rights gaps4 / 13
Double-claim structure2 / 13
29 cited findings · 13 agreementsIllustrative data · no live portfolio information
Thirteen agreements arrive as individual documents.
Keep terms and findings together
Build a searchable record of negotiated terms, protections, connected provisions, and precedent.
See how provisions work together
Trace LME, covenant, collateral, priming, amendment, voting, and restructuring paths.
Check the source behind each finding
Inspect the controlling language, provision path, calculations, assumptions, ambiguity, and review points.
Reach a cited first pass sooner
Bring cited evidence forward before a financing, amendment deadline, sponsor request, or repricing event.
See what an amendment changes.
Connect amendments, update selected calculations and protections, and identify changed exposure.
Operational targets
Targets describe intended operating performance and remain subject to workflow, agreement, and evaluation scope.
Internal benchmark · two credit agreements
DAC produces precise, accurate, and complete analysis.
Rather than relying on a single AI model, DAC combines findings from multiple models. It then reviews the complete analysis and corrects or removes anything that does not pass review, resulting in 71 of 71 pathways found and zero failures in this benchmark.
How much each system found
More is better · DAC's 71-pathway reference set
DAC
71 / 7137
GPT-6 Astra
53 / 7133
Claude Opus 5
20 / 7133
Gemini 3.8 Flash
14 / 7115
Leading LegalTech
Platform
Platform
7 / 7115
Pathways that fail review
Smaller bars are better · Number of failed pathways
DefectiveDisprovedBlocked
DAC0 / 710%0 defective · 0 disproved · 0 blocked
GPT-6 Astra
3 / 614.9%0 defective · 0 disproved · 3 blocked
Claude Opus 5
16 / 2857.1%10 defective · 3 disproved · 3 blocked
Gemini 3.8 Flash
10 / 1758.8%5 defective · 2 disproved · 3 blocked
Leading LegalTech
Platform
Platform
13 / 1776.5%5 defective · 6 disproved · 2 blocked
Novel mechanisms
37novel mechanisms
12% more than the next best shown.37 vs 33.
Pathway coverage
34%broader coverage
71 reference pathways found.Next best shown: 53.
Fewer failures
0 / 71failed review
No DAC pathway failed review.Other systems shown: 4.9%–76.5% failed.
Review the methodology
Agreement setCoty 2015 first-lien ($4.5B) and Phreesia 2026 revolver ($275M) credit agreements.
Multi-model synthesisDAC is the consolidated output of a multi-model cohort, not a single-model result. Distinct findings surfaced across the cohort are combined into one candidate set. The resulting 71-pathway reference set makes DAC's 71 / 71 coverage a measure of the consolidated output rather than a single-model hit rate.
Coverage and noveltyThe left chart reports how many reference pathways each system found. Novel mechanisms are separate, single-provision building blocks and are not added to the pathway bars.
Failure reviewThe right chart uses a common 0–16 scale. Failed pathways are classified by their mutually exclusive worst outcome: defective, disproved, or blocked by the agreement.
DenominatorsFailure fractions and rates use each system's own asserted pathways: DAC 71, GPT-6 Astra 61, Claude Opus 5 28, Gemini 3.8 Flash 17, and the LegalTech platform 17.
Final-output reviewCandidate pathways are reviewed and either corrected or withdrawn before DAC's final output. Competitor outputs were assessed as delivered. The 0% failure rate describes DAC's final reviewed output, not guaranteed production accuracy.
Discovery contextOther systems first surfaced 67 of DAC's 71 pathways; DAC re-derived 51 in corrected form. The result reflects the breadth and quality of the consolidated output, not exclusive first discovery.
Why Fable was not includedFable declined to generate clause chains for the Coty benchmark under its safeguards. Rather than work around the refusal and risk an unreliable or non-replicable result, the team used Opus instead.
Evidence limitationInternal DAC benchmark, protocol v1.13, dated September 6, 2026. Review was model-based with no human adjudication. Two scored systems were omitted from the source; results are limited to systems shown and do not establish production accuracy, legal validity, or quantified cost savings.
First application · ClauseFlow
See how provisions work together.
Exposure can emerge when provisions interact across an agreement. ClauseFlow traces those interactions, links every finding to the source, and finds recurring exposure across a portfolio.
Explore ClauseFlowFollow connected provisions through the agreement.
Inspect the evidence behind each finding.
Find repeated risk across the portfolio.
Our mission
Empower humanity to command the intelligence it creates.
People set the rules. DAC helps intelligent systems operate within the authority people and institutions define.
Founders
Anish Patel
Founder & CEO
Saw firsthand during the financial crisis how small clauses in agreements could move billions of dollars. Was on the advisory team that supported the issuance of Lehman's final CLO the week before the bank's failure. Leads DAC's product, architecture, and institutional partnerships.
Shalu Maheshwari, Esq.
Co-Founder & General Counsel
Senior IP litigator and strategist turned technology operator. Defines the legal and conceptual architecture that carries agreement logic into engineering and product design while overseeing IP strategy, compliance, and go-to-market frameworks.
Advisors
Dr. Edward Hunter
Director of Engineering, Johns Hopkins DS&AI Institute
AI and computer-vision pioneer whose work in visual tracking and large-scale AI systems helped shape modern motion-capture infrastructure. Advises on technical architecture and long-term AI strategy.
Howard Widra
Former Head of Direct Originations at Apollo; Former CEO and Chairman at MidCap Financial
Private-credit and capital-markets leader who deployed billions in capital and scaled a leading lending platform; brings deep institutional investor relationships.
Wayne C. Plewniak
Managing Director & Head of Fixed Income, GAMCO/Gabelli
Institutional fixed-income leader with deep credit expertise and buy-side relationships.
Jason Turner
Chief Executive Officer, Entanglement, Inc.
Quantum and AI infrastructure entrepreneur; former QED-C (NIST) workforce lead. Advises on scalable AI systems and research partnerships.
Tyler Weiss
VFX Producer, Image Engine
Emmy-nominated producer in game and film visual effects.
VFXGame of Thrones, Avatar: The Last Airbender, Alien: Romulus, 3 Body Problem, and Fantastic Beasts: The Secrets of Dumbledore.
GamingThe Lord of the Rings, FIFA, Madden NFL, Batman: Arkham Asylum, and Deus Ex.
Talk with DAC
Put agreement rules behind AI action.
Tell us where intelligent systems need clear authority, traceable decisions, and human review.
Test ClauseFlow
Test one agreement your team already knows.
Bring one signed agreement and one question your team has already answered. Compare ClauseFlow's cited answer with your existing analysis.
Build with DAC
Build the agreement layer.
Whether you are shaping an institution, a consequential AI system, or the next application of DAC's infrastructure, we would like to hear from you.