Audit-ready Finance RAG that passes Compliance Review and holds quality in production.
We design and build Finance RAG systems with measurable retrieval and answer quality, continuous regressions, and a full audit trail — so CFO, Risk and Compliance can defend every answer.
Golden dataset + baseline KPIs + CI/CD regression + audit dossier — standard in every delivery.
What breaks 80% Finance-RAG at first Compliance Review
- No measurable retrieval baseline: no golden dataset, no Recall@K targets, no regression history.
- No evidence trail per answer: no linked sources, no query/retrieval logs, no way to reconstruct how the answer was produced.
- No documented RAG architecture: no data lineage, no DPIA, no clear model and retrieval governance.
- No SLO/SLA for quality: no thresholds for faithfulness, hallucination, or latency tied to production monitoring.
Why this is a structural risk
For Finance, Risk and Compliance, an "almost correct" answer is not a minor defect. A system that sometimes misses critical documents or produces non-defensible answers creates regulatory and audit risk.
Our work starts from these failure modes and designs the architecture, evaluation and governance to eliminate them.
How we make Finance RAG pass Compliance Review
We design the system so that every answer has traceable evidence, measurable quality and a documented governance trail — from retrieval to DPIA and SLO/SLA monitoring.
Retrieval Quality
Golden dataset and baseline Recall@K targets, with automated regression runs on every significant change to data or model.
Evidence Trail
Per-answer context logging, linked sources, query/retrieval traces and audit logs aligned with internal audit expectations.
Compliance Package
DPIA, data lineage, policy mapping and model governance documentation included in the architecture deliverables.
SLO/SLA & Monitoring
Defined quality and latency SLOs tied to metrics like faithfulness, hallucination rate and p50/p95 latency, with alerting.
Measurable Quality with Automated Evaluation
For Finance and Risk teams this means the system reliably finds the right evidence when it matters, not "sometimes gets it right". Every RAG system includes a comprehensive quality evaluation stack integrated into production. Not declarations—engineering proof.
Retrieval Evaluation
Continuous measurement of retrieval quality with industry-standard metrics integrated into production pipeline.
- Recall@K measurement (target ≥0.95)
- Precision@K tracking
- Evidence completeness scoring
Answer Quality Evaluation
Automated answer quality verification with faithfulness, correctness, and hallucination detection frameworks.
- Faithfulness evaluator (target ≥0.90)
- Hallucination detector
- Answer correctness scorer
Automated Regression Loop
Continuous quality verification with automated regression testing integrated into CI/CD and production monitoring.
- Weekly regression cycles
- Monthly comprehensive review
- Production auto-checks
Framework Integration
Integration with industry-standard evaluation frameworks for financial RAG systems.
- Automated evaluation pipeline
- Financial domain metrics
- CI/CD pipeline hooks
Security & Compliance
Enterprise-grade security controls with audit trail and compliance monitoring built into every layer.
- Session audit logging
- Data lineage tracking
- DPIA documentation
Production Monitoring
Real-time production monitoring with SLA tracking and automated alerting on quality degradation.
- SLO/SLA monitoring
- Quality trend analysis
- Automated alerting
Example A: 48h Regression Sample
Real production metrics from automated regression run on Arabic Finance RAG. 12 queries evaluated across financial document corpus.
Evaluated using automated quality evaluation pipeline. Data anonymized.
Enterprise Compliance & Audit Readiness
The goal is simple: answers you can defend in front of auditors and regulators, with clear evidence and governance. We design compliance and audit artefacts as part of the architecture, not as an afterthought.
Audit Trail
Complete session logging with context trace, retrieval path, and answer provenance. Every query is traceable to source documents.
DPIA & Risk Maps
Data Protection Impact Assessment documentation, risk registers, and mitigation strategies included in every P3 Architecture deliverable.
Security Boundaries
Multi-tenancy isolation, access control, encryption at rest and in transit, and data loss prevention mechanisms built into architecture.
Model Governance
Evaluation governance framework with model versioning, performance tracking, and change management procedures.
SLO/SLA Monitoring
Production monitoring with defined service level objectives and service level agreements. Automated alerting on quality degradation.
Data Retention
Clear data retention policies with automated purging, archival procedures, and compliance with regulatory requirements.
API Security & Rate Limiting
Production API with authentication, authorization, rate limiting, and usage tracking. Prevents abuse and ensures fair resource allocation across tenants.
Incident Response & Rollback
Documented incident response procedures with rollback capabilities. Version control for models, prompts, and configurations enables rapid recovery from quality degradation.
Every Build phase delivers a complete audit-ready dossier including: risk log, data lineage documentation, evaluation governance framework, security architecture, SLO/SLA definitions, and compliance checklist. Ready for internal audit and regulatory review.
What You Actually Receive
Concrete artifacts from every Discovery phase. Not consulting fluff—engineering documentation ready for implementation.
P1: Feasibility Snapshot
10-15 page diagnostic report with clear go/no-go recommendation.
- Executive summary (1 page)
- Data readiness assessment
- Use-case suitability matrix
- Organizational readiness score
- ROI projections
- Risk assessment
P2: Readiness Audit
25-40 page comprehensive audit with CFO briefing and data map.
- CFO briefing (1 page)
- Source systems map (up to 10 systems)
- Data quality report
- Current process diagnostic
- RAG-readiness scorecard
- Risk register
P3: Architecture & Roadmap
35-60 page architecture document with diagrams and implementation roadmap.
- Target system design
- Data architecture (ingestion, indexing)
- Retrieval/LLM/evaluation architecture
- 1-3 high-level diagrams
- 3-5 sequence diagrams
- Compliance package (DPIA, audit trail)
- 3-6 month implementation roadmap
Build: Quality Dossier
Comprehensive quality assurance documentation delivered with every Build phase.
- Golden dataset (20-50 Q/A pairs)
- Baseline KPIs report
- Evaluation framework documentation
- Regression test suite
- SLO/SLA definitions
- Production monitoring setup
Full Discovery Layer
Combined P2 + P3 deliverables for comprehensive Discovery program.
- Complete P2 Readiness Audit output
- Complete P3 Architecture output
- Integrated risk register + architecture
- CFO briefing + technical roadmap
- 55-100 combined pages
- Build-ready specifications
Run: Managed Service
Monthly managed service package with continuous quality monitoring and support.
- Monthly quality regression report
- Production incident log
- SLA compliance report
- Model performance trends
- Quarterly architecture review
- Continuous improvement roadmap
Quality Assurance Process
5-stage evaluation governance integrated into every Build and Run engagement.
Golden Dataset
20-50 curated Q/A pairs from domain experts
Baseline KPIs
Initial quality measurement and threshold definition
Continuous Evaluation
Automated quality checks on every deployment
Regression Testing
Weekly/monthly regression with trend analysis
Acceptance Criteria
Quality gates for production release approval
Example B: Investment Document RAG
Baseline metrics from a different production system — Investment Document Analysis RAG (Canada). Demonstrates consistent quality across deployments.
Retrieval Quality
Answer Quality
Productized Discovery Services
McKinsey-style process with deeper engineering rigor. Every engagement starts with Discovery to ensure architecture quality, predictable Build, and stable Run. Fixed scope. Fixed pricing. 100% prepaid.
Choose the right Discovery tier:
- P1 — Feasibility Snapshot: when you are unsure if Finance RAG is worth the investment or which use-case to start with.
- P2 — Data & RAG Readiness Audit: when you have data and a defined use-case, but need a structured assessment and roadmap.
- P3 — Architecture & Roadmap: when you are committed to Build and need a full architecture, governance and delivery plan.
Finance RAG Feasibility Snapshot
Determine if RAG/Q&A makes sense for your organization. Quick assessment of data readiness, use-case suitability, and organizational readiness with clear go/no-go recommendation.
What's Included
- Up to 2 interviews (CFO/Finance/Risk)
- Data Readiness analysis
- Use-case Suitability assessment
- Organizational Readiness review
- 10-15 page report with ROI analysis
- Clear go/no-go recommendation
Finance RAG & Data Readiness Audit
Comprehensive Discovery to collect facts, risks, and data mapping. Full diagnostic of current Q&A processes and RAG-readiness with actionable recommendations.
What's Included
- Up to 5 interviews (CFO, Risk, Compliance, BI, IT)
- Source systems mapping (up to 10 systems)
- Data Quality Review
- Current Q&A process diagnostic
- RAG-readiness diagnostic
- 25-40 page report
- 1-page CFO Briefing
Enterprise RAG Architecture & Roadmap
Complete architecture and roadmap that makes Build the natural next step. Target system design, data architecture, compliance package, and implementation roadmap.
What's Included
- Target system design (Finance KB/Compliance Q&A/IR RAG)
- Data architecture (ingestion, indexing, multi-tenancy)
- Retrieval/LLM/evaluation architecture
- Compliance package (logging, audit, metrics, SLO/SLA)
- 35-60 page architecture document
- 1-3 high-level diagrams
- 3-5 sequence diagrams
- 3-6 month roadmap
Full Discovery Layer — $40,000
Combine P2 + P3 for the complete Discovery program. Recommended entry for mature organizations ready for enterprise RAG implementation.
Start with DiscoveryWhat "audit-ready" Finance RAG looks like in practice
Representative, anonymized samples showing how questions, retrieved context, answers and quality metrics connect in a Finance RAG system.
Audit query: liquidity coverage ratio explanation
"Section 4.2 — Liquidity Coverage Ratio. For Q4 2024, the bank maintained LCR at 112% based on HQLA and 30-day net outflows as defined in internal policy FIN-LCR-2021…"
The system explains how LCR was calculated, which datasets were used, which internal policy sections apply, and links the answer back to specific paragraphs in the policy and reporting pack.
Audit query: change in provisioning policy
"Memo COM-2023-11 — Provisioning Policy Update. Effective 01.01.2024, stage 2 provisioning threshold adjusted from 30 to 60 days past due, aligned with guideline XYZ…"
The system outlines what changed, why, on which date, which internal memo and policy clauses govern the change, and links to the board approval minutes.
Audit query: reconciliation of IR disclosures
"IR deck Q2 2024, slide 18 — Adjusted EBITDA bridge…"
"Annual report 2023, note 7.3 — Reconciliation of adjusted metrics to IFRS figures…"
The system reconciles adjusted and IFRS metrics, explains the treatment of one-off items, and points to the exact notes and slides that support the final numbers.
Discovery → Build → Run
McKinsey-style engagement structure with deeper engineering rigor in RAG quality. Fixed pipeline. Fixed pricing. Predictable outcomes. Every Build starts with Discovery.
Discovery
Productized diagnostic and architecture services. Choose P1, P2, P3, or Full Discovery based on organizational maturity.
$5k - $40k
Build
Fixed-scope RAG implementation based on approved architecture. 40/40/20 milestone payments. Quality gates at every stage.
from $120k
Run
Managed service with continuous quality monitoring, monthly regression, and SLA-backed support. Ensures stable production performance.
from $15k/month
Why Discovery First?
Like McKinsey and Deloitte, we never start implementation without proper diagnostic. Discovery ensures architecture quality, prevents costly mistakes, and guarantees successful Build and Run phases.
Prevents Costly Failures
80% of RAG projects fail due to poor architecture and unclear requirements. Discovery de-risks Build by validating feasibility, data readiness, and use-case fit before any implementation.
Fixed Scope = Predictable Budget
Productized Discovery with strict scope boundaries ensures no scope creep, no surprises, and clear deliverables. You know exactly what you're getting and what you're paying for.
Build-Ready Architecture
P3 Architecture & Roadmap output becomes the blueprint for Build phase. No guesswork, no rework. Implementation team receives production-ready specifications with compliance and quality metrics defined.
Finance RAG Solutions We've Built
Real implementations of Finance RAG systems for banks, investment firms and insurance clients — with measurable quality, audit outcomes and reduced manual escalations.
Three examples of audit-ready outcomes
- Arabic Finance RAG (Middle East): reduced manual research time from ~15 minutes to 2–3 seconds per query, with faithfulness ≥0.9.
- Terabyte-scale document search: moved audit query handling from 15 minutes to 2–3 minutes with stable retrieval quality.
- Compliance Q&A automation: automated 30%+ of repetitive compliance queries with traceable answers and evidence logs.
Arabic-Optimized Financial RAG
Production-grade RAG platform with FastAPI, Azure OpenAI, and PGVector for Arabic financial documents. Multi-stage retrieval with sub-2-second response times with automated quality verification.
Terabyte-Scale Document Search
Comprehensive RAG system with AWS Kendra and Bedrock for terabyte-scale document repositories. Reduced information retrieval time from 15 minutes to 2-3 minutes.
Financial Document Analysis RAG
AWS Bedrock and Kendra system for investment document libraries. Generates precise answers from financial reports, enabling instant access to key metrics and investment strategies.
Graph-Enhanced Financial RAG
LlamaIndex and Neo4j system for complex financial document relationships. Graph-based retrieval enables multi-hop reasoning across interconnected data, improving accuracy for complex queries.
Automated Query Resolution System
LangChain and FastAPI platform for automated financial and insurance query processing. Achieved 30%+ automated resolution with continuous faithfulness and relevance verification.
LLM Evaluation Framework
Built automated quality assessment system that increased coverage from 2% to 25% and improved AI assessment accuracy from 66% to 91%. Ensures correct DeepEval integration.
Who we are and how we deliver
Entity & jurisdiction
DataFlux Software operates as a specialized consulting firm focused on Finance RAG and AI architecture for regulated clients.
Engagement model
We work through a Discovery → Build → Run pipeline with fixed-scope Discovery tiers, milestone-based Build and retainers for quality & compliance.
NDA & info security
Standard NDAs, data access limited to agreed scopes, and architectures designed for isolation, access control and data minimization.
SLO/SLA responsibility
Clear SLO/SLA definitions for quality and latency, with monitoring and alerting integrated into the delivery scope where applicable.
Start with Discovery
30-min qualification call (required before NDA) to discuss your Finance RAG needs. We'll determine which Discovery product fits your organization and timeline.
Request Qualification Call