How LinqAlpha assesses funding theses utilizing Satan’s Advocate on Amazon Bedrock

This can be a visitor submit by Suyeol Yun, Jaeseon Ha, Subeen Pang and Jacob (Chanyeol) Choi at LinqAlpha, in partnership with AWS.

LinqAlpha is a Boston-based multi-agent AI system constructed particularly for institutional traders. Over 170 hedge funds and asset managers worldwide use LinqAlpha to streamline their funding analysis for public equities and different liquid securities, remodeling hours of guide diligence into structured insights with multi-agent giant language mannequin (LLM) programs. The system helps and streamlines agentic workflows throughout firm screening, primer technology, inventory worth catalyst mapping, and now, pressure-testing funding concepts by way of a brand new AI agent referred to as Satan’s Advocate.

On this submit, we share how LinqAlpha makes use of Amazon Bedrock to construct and scale Satan’s Advocate.

The Problem

Conviction drives funding selections, however an unexamined funding thesis can introduce danger. Earlier than allocating capital, traders typically ask, “What am I overlooking?” Figuring out blind spots often entails time-consuming cross-referencing of knowledgeable calls, dealer experiences, and filings. Affirmation bias and scattered workflows make it laborious to problem one’s personal concepts objectively. Contemplate the instance thesis, “ABCD will probably be a generative AI beneficiary with profitable AI monetization and aggressive positioning.” The thesis appears sound till you probe whether or not open supply alternate options may erode pricing energy or if monetization mechanisms are absolutely understood throughout the product stack. These nuances typically get missed. That is the place a satan’s advocate is available in, a task or mindset that intentionally challenges the thesis to uncover hidden dangers and weak assumptions. For traders, this sort of structured skepticism is crucial to avoiding blind spots and making higher-conviction selections.

Traders have historically engaged in satan’s advocate pondering by way of guide processes, debating concepts in staff conferences, or mapping out professionals and cons by way of casual state of affairs evaluation. LinqAlpha got down to construction this guide and improvised course of with AI.

The answer

Satan’s Advocate is an AI analysis agent purpose-built to assist traders systematically pressure-test their funding theses utilizing their very own trusted sources at 5–10 occasions the pace of conventional assessment. To assist traders take a look at their funding theses extra rigorously, Satan’s Advocate agent in LinqAlpha follows a structured four-step course of from thesis definition and doc ingestion to automated assumption evaluation and structured counterargument technology:

1. Outline your thesis
2. Add reference paperwork
3. AI-driven thesis evaluation
4. Structured critique and counterarguments

This part outlines how the system works from finish to finish: how traders work together with the agent, how the AI parses and challenges assumptions utilizing trusted proof, and the way the outcomes are introduced. Specifically, we spotlight how the system decomposes theses into assumptions, hyperlinks every critique to supply supplies, and scales this course of effectively utilizing Claude Sonnet 4.0 by Anthropic in Amazon Bedrock. Amazon Bedrock is a completely managed service that makes high-performing basis fashions (FMs) from main AI firms and Amazon obtainable on your use by way of a unified API.

Outline your thesis

Traders articulate their thesis as a core assertion supported by underlying reasoning. For instance, ABCD will probably be a GenAI beneficiary with profitable AI monetization and aggressive positioning. They enter this thesis in Satan’s Advocate within the Funding Thesis subject, as proven within the following screenshot.

Add reference paperwork

Traders add analysis reminiscent of dealer experiences, knowledgeable calls, and public filings within the Add Information subject, as proven within the following screenshot. The system parses, chunks, and indexes this content material right into a structured proof repository.

AI-driven thesis evaluation

Satan’s Advocate deconstructs the thesis into specific assertions and implicit assumptions. It scans the proof base to search out content material that challenges or contradicts these assumptions.

Structured critique and counterarguments

The system generates a structured critique the place every assumption is restated and immediately challenged. Each counterpoint is sourced and linked to particular excerpts from the uploaded supplies. The next screenshot reveals how the system produces a structured, evidence-linked critique. Ranging from the investor’s thesis, it extracts assumptions, challenges them, and anchors every counterpoint to a particular supply. On this case, the declare that ABCD will profit from generative AI is examined in opposition to two core weaknesses: a scarcity of a confirmed monetization path regardless of new options reminiscent of Product, and a monitor document of avoiding worth will increase resulting from buyer sensitivity. Every argument is grounded in uploaded analysis, reminiscent of knowledgeable calls and analyst commentary, with clickable citations. Traders can hint every problem again to its supply and consider whether or not their thesis nonetheless holds beneath strain.

Software circulate

The Satan’s Advocate agent is a multi-agent system that orchestrates specialised brokers for doc parsing, retrieval, and rebuttal technology. In contrast to a hard and fast pipeline, these brokers work together iteratively: the evaluation agent decomposes assumptions, the retrieval agent queries sources, and the synthesis agent generates counterarguments earlier than looping again for refinement. This iterative back-and-forth is what makes the system agentic fairly than a static workflow. The general structure will be described as 4 interdependent levels from ingestion to critique supply. The structure follows a four-stage circulate from knowledge ingestion to critique supply.

Enter thesis

Customers submit an funding thesis, typically as an funding committee (IC) memo. The enter is obtained by a customized software working in an Amazon Elastic Compute Cloud (Amazon EC2) occasion, which routes the request to Amazon Bedrock. Claude Sonnet 4 by Anthropic in Amazon Bedrock interprets the assertion and decomposes it into core assumptions. Amazon EC2 runs a Python-based orchestration layer constructed by LinqAlpha, which coordinates API calls, manages logging, and controls agent execution.

Add paperwork

These paperwork are dealt with by a preprocessing pipeline working in an EC2 occasion, which extracts uncooked knowledge and converts it into structured chunks. The EC2 occasion runs LinqAlpha’s parsing software written in Python and built-in with Amazon Textract for doc parsing. AWS Lambda or AWS Fargate may have been alternate options, however Amazon EC2 was chosen as a result of prospects in regulated finance environments required persistent compute with auditable logs and strict management over networking. Uncooked information are saved in Amazon Easy Storage Service (Amazon S3), structured outputs go into Amazon Relational Database Service (Amazon RDS), and parsed content material is listed by Amazon OpenSearch Service for retrieval.

Analyze thesis

Claude Sonnet 4 by Anthropic in Amazon Bedrock points focused retrieval queries throughout Amazon OpenSearch Service and aggregates counter-evidence from Amazon RDS and Amazon S3. A structured immediate template enforces consistency within the rebuttal output. For instance, the agent receives prompts like:

You might be an institutional analysis assistant designed to behave as a Satan’s Advocate. 
Your process is to problem funding theses with structured, evidence-linked counterarguments. 
At all times use offered paperwork (knowledgeable calls, dealer experiences, 10-Ks, transcripts). 
If no related proof exists, clearly state "no counter-evidence discovered".
Thesis: {user_thesis}
Step 1. Establish Assumptions
- Extract all specific assumptions (said immediately within the thesis).
- Extract implicit assumptions (unspoken however required for the thesis to carry).
- Label every assumption with an ID (A1, A2, A3...).
Step 2. Retrieve and Check
- For every assumption, problem retrieval queries in opposition to uploaded sources (OpenSearch index, RDS, S3).
- Prioritize authoritative sources on this order:
   1. SEC filings (10-Ok, 10-Q, 8-Ok)
   2. Skilled name transcripts
   3. Dealer/analyst experiences
- Establish passages that immediately weaken, contradict, or increase uncertainty in regards to the assumption.
Step 3. Structured Output
For every assumption, output in JSON with the next fields:
{
  "assumption_id": "A1",
  "assumption": "",
  "counter_argument": "",
  "quotation": {
       "doc_type": "10-Ok",
       "doc_id": "ABCD_10K_2023",
       "web page": "47",
       "excerpt": "Administration famous that monetization of Product options stays exploratory, with no dedicated pricing mannequin."
  },
  "risk_flag": " (relative significance of this counterpoint to the thesis)"
}
Step 4. Output Formatting
- Return all assumptions and critiques as a JSON array.
- Guarantee each counter_argument has at the least one quotation.
- If no proof discovered, set counter_argument = "No counter-evidence present in offered sources" and quotation = null.
- Maintain tone factual and impartial (keep away from hypothesis).
- Keep away from duplication of proof throughout assumptions until extremely related.
Step 5. Analyst Voice Calibration
- Write counter_arguments within the type of an institutional fairness analysis analyst. 
- Be concise (2–3 sentences per counter_argument).
- Give attention to materials dangers to the funding case (aggressive dynamics, regulation, margin compression, know-how adoption).

The next is a pattern output:

[
  {
    "assumption_id": "A1",
    "assumption": "ABCD will successfully monetize GenAI features like Product",
    "counter_argument": "Recent disclosures suggest Product monetization is still experimental, with management highlighting uncertainty around pricing models. This raises questions about near-term revenue contribution.",
    "citation": {
      "doc_type": "10-K",
      "doc_id": "ABCD_10K_2023",
      "page": "47",
      "excerpt": "Management noted that monetization of Product features remains exploratory, with no committed pricing model."
    },
    "risk_flag": "High"
  },
  {
    "assumption_id": "A2",
    "assumption": "Open-source competitors will not significantly erode ABCD's pricing power",
    "counter_argument": "Expert commentary indicates increasing adoption of open-source alternatives for creative workflows, which could pressure ABCD’s ability to sustain premium pricing.",
    "citation": {
      "doc_type": "Expert Call",
      "doc_id": "EC_DesignAI_2024",
      "page": "3",
      "excerpt": "Clients are experimenting with Stable Diffusion-based plugins as lower-cost substitutes for ABCD Product."
    },
    "risk_flag": "Medium"
  }
]

Evaluation output

The ultimate critique is returned to the consumer interface, exhibiting an inventory of challenged assumptions and supporting proof. Every counterpoint is linked to authentic supplies for traceability. This end-to-end circulate allows scalable, auditable, and high-quality pressure-testing of funding concepts.

System parts

The Satan’s Advocate agent operates as a multi-agent system that orchestrates parsing, retrieval, and rebuttal technology throughout AWS companies. Specialised brokers work iteratively, with every stage feeding again into the following, facilitating each doc constancy and reasoning depth. Traders work together with the system in two methods, forming the inspiration for downstream processing. Traders can enter their thesis in a pure language assertion of funding view. Typically, this takes the type of an IC memo. Another choice is to add paperwork. Traders can add finance-specific supplies reminiscent of earnings transcripts, 10-Ks, dealer experiences, or knowledgeable name notes.

Uploaded supplies are parsed into structured textual content and enriched with semantic construction earlier than indexing:

• Amazon Textract – Extracts uncooked textual content from PDFs and scanned paperwork
• Claude Sonnet 3.7 vision-language mannequin (VLM) – Enhances Amazon Textract outputs by reconstructing tables, deciphering visible content material, and segmenting doc constructions ( headers, footnotes, charts)
• Amazon EC2 orchestration layer – Runs LinqAlpha’s Python-based pipeline that coordinates Amazon Textract, Amazon Bedrock calls, and knowledge routing

Processed knowledge is saved and listed for quick retrieval and reproducibility:

• Amazon S3 – Shops uncooked supply information for auditability
• Amazon RDS – Maintains structured content material outputs
• Amazon OpenSearch Service – Indexes parsed and enriched content material for focused retrieval

Reasoning and rebuttal technology are powered by Claude Sonnet 4 by Anthropic in Amazon Bedrock. It performs the next capabilities:

• Assumption decomposition – Sonnet 4 breaks down the thesis into specific and implicit assumptions
• Retrieval agent – Sonnet 4 formulates focused queries in opposition to OpenSearch Service and aggregates counterevidence from Amazon RDS and Amazon S3
• Synthesis agent – Sonnet 4 produces structured rebuttals, citation-linked to supply paperwork, then returns outcomes by way of the Amazon EC2 orchestration layer to the consumer interface

The LinqAlpha Satan’s Advocate agent makes use of a modular multiagent design the place completely different Claude fashions focus on distinct roles:

• Parsing agent – Combines Amazon Textract for OCR with Claude Sonnet 3.7 VLM for structural enrichment of paperwork. This stage makes certain tables, charts, and part hierarchies are faithfully reconstructed earlier than indexing.
• Retrieval agent – Powered by Claude Sonnet 4, formulates retrieval queries in opposition to OpenSearch Service and aggregates counterevidence from Amazon RDS and Amazon S3 with long-context reasoning.
• Synthesis agent – Additionally utilizing Claude Sonnet 4, composes structured rebuttals, citation-linked to authentic sources, and codecs outputs in machine-readable JSON for auditability.

These brokers run iteratively: the Parsing agent enriches paperwork, the Retrieval agent surfaces potential counter-evidence, and the Synthesis agent generates critiques that may set off extra retrieval passes. This back-and-forth orchestration, managed by a Python-based service on Amazon EC2, makes the system genuinely multi-agentic fairly than a linear pipeline.

Implementing Claude 3.7 and 4.0 Sonnet in Amazon Bedrock

The LinqAlpha Satan’s Advocate agent employs a hybrid method on Amazon Bedrock, combining Claude Sonnet 3.7 for doc parsing with vision-language help, and Claude Sonnet 4.0 for reasoning and rebuttal technology. This separation facilitates each correct doc constancy and superior analytical rigor. Key capabilities embrace:

• Enhanced parsing with Claude Sonnet 3.7 VLM – Sonnet 3.7 multimodal capabilities increase Amazon Textract by reconstructing tables, charts, and part hierarchies that plain OCR typically distorts. This makes certain that monetary filings, dealer experiences, and scanned transcripts keep structural integrity earlier than coming into retrieval workflows.
• Superior reasoning with Claude Sonnet 4.0 – Sonnet 4.0 delivers stronger chain-of-thought reasoning, sharper assumption decomposition, and extra dependable technology of structured counterarguments. In comparison with prior variations, it aligns extra intently with monetary analyst workflows, producing rebuttals which might be each rigorous and citation-linked.
• Scalable agent deployment on AWS – Working on Amazon Bedrock permits LinqAlpha to scale dozens of brokers in parallel throughout giant volumes of funding supplies. The orchestration layer on Amazon EC2 coordinates Amazon Bedrock calls, enabling quick iteration beneath real-time analyst workloads whereas minimizing infrastructure overhead.
• Giant context and output home windows – With a 1M-token context window and help for outputs as much as 64,000 tokens, Sonnet 4.0 can analyze total 10-Ok filings, multi-hour knowledgeable name transcripts, and long-form IC memos with out truncation. This permits document-level synthesis that was beforehand infeasible with shorter-context fashions.
• Integration with AWS companies – By way of Amazon Bedrock, the answer integrates with Amazon S3 for uncooked storage, Amazon RDS for structured outputs, and OpenSearch Service for retrieval. This offered LinqAlpha with safer deployment, full management over buyer knowledge, and elastic scalability required by institutional finance purchasers.

For hedge funds, asset managers, and analysis groups, the selection of Amazon Bedrock with Anthropic fashions isn’t merely about know-how; it immediately addresses core operational ache factors in funding analysis:

• Auditability and compliance – Each counterargument is linked again to its supply doc (10-Ok, dealer be aware, transcript), creating an auditable path that meets institutional governance requirements.
• Information management – The Amazon Bedrock integration with personal S3 buckets and Amazon Digital Personal Cloud (Amazon VPC) deployed EC2 cases retains delicate paperwork inside the agency’s safe AWS atmosphere, a vital requirement for regulated traders.
• Workflow pace – By scaling agentic workflows in parallel, analysts save hours throughout earnings season or IC prep, compressing assessment cycles from days to minutes with out sacrificing depth.
• Choice high quality – Sonnet 3.7 facilitates doc constancy, and Sonnet 4.0 provides monetary reasoning power, collectively serving to traders uncover blind spots that will in any other case stay hidden in conventional workflows.

This mixture of AWS primarily based multi-agent orchestration and LLM scalability makes the LinqAlpha Satan’s Advocate agent uniquely suited to institutional finance, the place pace, compliance, and analytical rigor should coexist. With Amazon Bedrock, the answer achieved managed orchestration and built-in integration with AWS companies reminiscent of Amazon S3, Amazon EC2, and OpenSearch Service, which offered quick deployment, full management over knowledge, and elastic scale.

“This helped me objectively gut-check my bullish thesis forward of IC. As an alternative of losing hours caught in my very own affirmation bias, I rapidly surfaced credible pushbacks, making my pitch tighter and extra balanced.”

— PM at Tiger Cub Hedge Fund

Conclusion

Satan’s Advocate is one among over 50 clever brokers in LinqAlpha’s multi-agent analysis system, every designed to deal with a definite step of the institutional funding workflow. Conventional processes typically emphasize consensus constructing, however Satan’s Advocate extends analysis into the vital stage of structured dissent, difficult assumptions, surfacing blind spots, and offering auditable counterarguments linked on to supply supplies.

By combining Claude Sonnet 3.7 (for doc parsing with VLM help) and Claude Sonnet 4.0 (for reasoning and rebuttal technology) on Amazon Bedrock, the system facilitates each doc constancy and analytical depth. Integration with Amazon S3, Amazon EC2, Amazon RDS, and OpenSearch Service allows safer and scalable deployment inside investor-controlled AWS environments.

For institutional purchasers, the influence is significant. By automating repetitive diligence duties, the Satan’s Advocate agent frees analysts to spend extra time on higher-order funding debates and judgment-driven evaluation. IC memos and inventory pitches can profit from structured, source-grounded skepticism, supporting clearer reasoning and extra disciplined decision-making.

LinqAlpha’s agentic structure reveals how multi-agent LLM programs on Amazon Bedrock can remodel funding analysis from fragmented and guide into workflows which might be scalable, auditable, and resolution grade, tailor-made particularly for the calls for of analysis on public equities and different liquid securities.

To study extra about Satan’s Advocate and LinqAlpha, go to linqalpha.com.

Concerning the authors