Designed and implemented an advanced Retrieval-Augmented Generation platform that reasons over text, tables, charts, scanned documents, and images in a single grounded workflow. The system includes a dedicated evaluation stack for retrieval quality, citation faithfulness, factual consistency, latency/cost tradeoffs, and regression gating before release.
Built for teams that need reliable, evidence-backed AI assistants in production, not demo-only chatbots.
Knowledge workers lose time verifying AI answers across scattered PDFs, dashboards, and knowledge bases with inconsistent evidence quality.
Production-grade multimodal RAG stack with ingestion, retrieval orchestration, citation validation, and release-gated evaluation automation.
Higher answer correctness and lower unsupported claims, while preserving operational guardrails for latency and cost under realistic traffic.
Data connectors, retrieval baseline, citation-ready answer format, and initial benchmark dashboard for one core use case.
Advanced reranking, regression gating, observability, and role-aware controls for reliable rollout and change management.
Monthly evaluation cycles, drift checks, and retrieval quality tuning tied directly to business-critical task metrics.
Most RAG systems are optimized for plain text, but production knowledge sources are rarely pure text. Critical evidence lives inside screenshots, scanned PDFs, plots, tables, slide decks, and mixed-layout documents where semantics are distributed across modalities. Naive chunking and single-index retrieval often miss this structure, causing incomplete context assembly, weak grounding, and brittle answers.
This project addresses three hard problems simultaneously: (1) multimodal ingestion that preserves layout and visual semantics, (2) query-time orchestration that routes requests to the right retrievers and ranking stack, and (3) rigorous evaluation that measures not just answer quality but retrieval precision, citation support, and stability under distribution shift.
The first pipeline transforms heterogeneous artifacts into aligned multimodal embeddings and structured metadata views. It builds separate but linkable indexes for text, tables, figures, and layout regions.
Pulls from drives, wikis, APIs, and object stores with versioned snapshots.
Collects inline figures, charts, and screenshots as first-class retrieval units.
Extracts blocks, tables, captions, headers, and semantic region boundaries.
Rebuilds relational structure and links cells to surrounding narrative context.
The second pipeline classifies query intent, retrieves candidate evidence from specialized indexes, fuses and re-ranks multimodal context, then generates citation-grounded responses with confidence and abstention behavior when evidence is insufficient.
The third pipeline formalizes evaluation as a first-class system. Every model/index/prompt change is tested on curated and adversarial multimodal benchmarks, then blocked or promoted based on objective thresholds.
Recall@K, MRR, nDCG, modality coverage, and cross-source diversity measured per query class.
Span-level citation support and contradiction checks for each generated claim segment.
Adversarial prompts, OCR noise perturbations, table transposition stress tests, and long-context overload.
Latency distributions, cost-per-answer, cache hit-rate, and failure recovery effectiveness.
Built a canonical artifact schema linking text spans, table cells, and visual regions under shared provenance IDs.
Combined sparse lexical retrieval with dense embedding retrieval and modality-specific ANN subindexes.
Implemented learned rerankers that optimize both relevance and evidence complementarity across modalities.
Added citation-first prompting, claim decomposition, and abstention behavior for insufficient evidence cases.
Created nightly benchmark sweeps with threshold-based pass/fail release gates and automatic rollback signals.
Closed the loop by mapping failures back to parsing, retrieval, ranking, or generation components.
These plots summarize gains from multimodal retrieval, reranking, and citation validation, while exposing tradeoffs in latency and cost across model/index configurations.
Exact-match and semantic consistency uplift
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Text, tables, figures, and mixed queries
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Impact of validator and reranker improvements
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Bubble size indicates average response cost
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Post-fix residual failure composition
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Mixed chart: quality vs dollars per 1K answers
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| Configuration | Correctness | Citation Faithfulness | Recall@10 | Median Latency |
|---|---|---|---|---|
| Text-only RAG Baseline | 54% | 63% | 58% | 0.9s |
| + Multimodal Indexing | 67% | 74% | 72% | 1.2s |
| + Cross-Modal Reranker | 78% | 86% | 84% | 1.4s |
| Full System + Eval Gate | 91% | 91% | 88% | 1.6s |
If your team is deciding whether to fund a multimodal RAG initiative, use this guide to quickly assess fit and execution path.
You rely on PDFs, dashboards, tables, and screenshots for critical decisions, and answer mistakes create operational or compliance risk.
You mainly need text QA today, but expect expansion to complex documents and visual evidence in upcoming quarters.
If your data is fragmented with no ownership or no defined success metrics, start with data readiness before a full RAG build.
2-4 week build focused on one high-impact workflow, baseline retrieval, and measurable business KPI improvements.
Full architecture, eval gates, monitoring, and rollout plan with handoff documentation for your internal team.
For teams with engineers in place who need architecture review, benchmark strategy, and delivery acceleration.
Quick planning calculator to estimate potential yearly time and cost savings from grounded multimodal retrieval.
Assumes 240 working days/year and consistent adoption across selected users.
Directional estimate for planning. Actual realized value depends on workflow quality and adoption rate.
For a focused workflow, teams usually see measurable value in 2-4 weeks through reduced lookup time and higher answer reliability.
Not fully. We can start with your highest-value document sources and define a phased data quality plan while shipping an MVP.
By combining retrieval quality controls, citation validation, abstention policies, and hard regression gates in the deployment pipeline.
Yes. Typical integrations include cloud storage, internal wikis, BI exports, and role-aware auth layers without replacing your systems.
If your team is planning a RAG initiative, I can help from architecture and data ingestion to evaluation gates and production rollout.
Multimodal enterprise search, grounded assistants for operations, and high-accuracy QA where traceability is mandatory.
A build plan, technical implementation, benchmark suite, and a deployment strategy aligned with business KPIs.
Send your use case and data landscape; I will propose an MVP scope, timeline, and success metrics.
Grounded enterprise QA over documents with citation-aware answers.
Evaluation and guardrails for tool-using LLM systems.
Forecasting and intervention planning for urban traffic systems.
Condition-aware sensor fusion for autonomous driving.