AI-powered data analysis assistant for due diligence

Turning complex, fragmented data into reliable insights for faster analysis and decision-making

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Project highlights

Industry: Business Consulting

Client services: AI Business Transformation

Timeline: May-December 2025

Location: United States

Team size: 5 members

Duration: 7 months

About the client

The client is a global technology advisory firm that helps investors and portfolio companies make informed, data-driven decisions across the investment lifecycle. Their teams provide services such as technical due diligence, architecture reviews, and technology value-creation planning to support investment and operational strategy.

The organization operates in fast-paced environments where accuracy, traceability, and clarity of insights are critical to decision-making.

Business challenges

During investment evaluations and operational assessments, teams must analyze large volumes of data from multiple systems within tight timelines. Extracting reliable insights quickly was becoming increasingly difficult due to both technical and workflow constraints.

Key challenges included:

Analysts needed to answer complex, business-critical questions under compressed due-diligence timelines
Ambiguous natural-language questions often required contextual interpretation and decomposition into multiple structured queries
Data was distributed across multiple platforms and connected through complex foreign-key relationships
Mixed modalities, including relational data and semi-structured JSON, complicated query generation and interpretation
Large, domain-specific schemas made manual querying time-consuming and error-prone

Beyond technical complexity, the client needed outputs that were:

Verifiable and reviewable
Clearly traceable to underlying data
Easy to refine and follow up on during active engagements

In this context, incomplete or unclear insights could directly impact investment decisions, making reliability and explainability essential.

Goals set to Achievion

Achievion was engaged to design and implement a solution that would improve how analysts interact with complex data environments and accelerate insight generation during analytical and diligence workflows.

The objectives included:

Enable reliable natural-language access to complex, multi-source data systems
Deliver a controlled and extensible solution that integrates seamlessly with existing analytical workflows
Improve consistency and trust by grounding query generation in schema-aware, metadata-driven logic
Reduce analyst workload by automating table discovery, join planning, SQL generation, validation, and summarization
Provide verifiable outputs that support rapid iteration and follow-up analysis
Ensure governance, traceability, and security aligned with enterprise and investor expectations

These goals supported faster, more confident decision-making while reducing analytical friction and operational risk.

Solution

Achievion designed and implemented a modular multi-agent system that converts ambiguous natural-language questions into precise, validated queries and clear business narratives.

The architecture separates responsibilities across specialized agents, ensuring reliability, traceability, and scalability.

Core components include:

Orchestrator Agent – Interprets user intent and coordinates task execution
Navigator Agent – Identifies relevant tables, relationships, and execution plans
Query Generation Agent – Drafts SQL queries aligned with schema constraints
Validation and Guardrails Layer – Detects ambiguity, enforces rules, and prevents unsafe or inefficient queries
Response Composer – Converts query results into concise, business-ready insights with assumptions and context

This architecture shifts due diligence workflows from manual, schema-heavy querying to structured analytical workflows. Analysts spend less time preparing queries and validating joins, and more time interpreting results and making decisions.

The result is fast, controlled access to business-critical data through natural-language questions—without compromising reliability or governance.

Business outcome

Achievion delivered a modular, production-ready solution that significantly improves how analysts access and interpret complex data. By transforming ambiguous natural-language questions into validated queries and clear business insights, the system enables faster, more confident decision-making in high-stakes analytical workflows.

Key outcomes included:

Faster insight generation
Analysts can move from questions to validated results much more quickly, reducing the time spent on manual query drafting, join discovery, and data interpretation.
Improved accuracy and reliability
Schema-aware, metadata-driven query generation and validation significantly reduce errors and ambiguity, ensuring outputs are consistent, traceable, and reviewable.
Greater trust in analytical results
Clear assumptions, validation steps, and structured outputs help teams confidently rely on insights during time-sensitive decision-making processes.
Reduced workload for technical and analytical teams
Automation of repetitive tasks—such as schema exploration, query refinement, and summarization—frees analysts to focus on interpretation, strategy, and higher-value activities.
Seamless integration with existing workflows
The solution operates within established processes and governance frameworks, allowing teams to adopt new capabilities without disrupting their current working methods.

Timeline

June 2025

Foundation & Multi-Agent Framework

Conducted discovery sessions and analyzed real due-diligence workflows
Designed orchestration logic and baseline agent interactions
Implemented initial navigator capabilities for schema exploration

July-September 2025

Stabilization & Query Reliability Improvement

Improved reliability for SQL-addressable requests
Enhanced intent handling and ambiguity detection
Strengthened validation, guardrails, and security controls

October-December 2025

Productionization

Expanded methodology support and domain alignment
Increased testing coverage and reliability validation
Introduced monitoring and performance optimization

Team

Product Manager

AI Solutions Architect

Data Engineer

LLM Engineer

QA Engineer

Tech Stack

AI and ML:

Amazon Bedrock

Amazon Bedrock AgentCore

Data Management:

Amazon OpenSearch Service

Amazon Athena

AWS Glue

Deployment:

Amazon API Gateway

Python

Amazon CloudWatch

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