Skip to content
Kavyasri Jadala

— — Selected systems

Systems, not demos — with the numbers.

Knowledge graphs, agents, geospatial risk, governed GenAI and classical forecasting. Each one names the problem, the architecture, my part in it and what was measured — including results that disappointed.

01Enterprise · Bristol Myers Squibb

Healthcare 360 Intelligence

Knowledge graph · Graph-RAG · AWS Neptune · LLMs

Problem
Commercial customer data lived in 20+ systems with no shared meaning. Every business question became a bespoke SQL request, and two teams could get two answers.
System
Entity resolution into a governed RDF/OWL model, SHACL-validated on load into AWS Neptune, with a Graph-RAG assistant that answers in natural language and cites its evidence.
My role
Architect and hands-on lead — semantic model, entity resolution, Graph-RAG, governance
source systems unified
20+source systems unified
recurring ad-hoc requests replaced
~80%recurring ad-hoc requests replaced
SHACL-validated before it reaches the graph
Every loadSHACL-validated before it reaches the graph
  • AWS Neptune
  • RDF / OWL
  • SHACL
  • SPARQL
  • Graph-RAG
  • Entity resolution
  • LLMs
View case studyCode
20+ systems → one governed answer
  1. CRMEngagementWeb analyticsOffline
  2. Entity resolutionrules + fuzzy + embeddings · survivorship
  3. Semantic layerRDF / OWL ontology · SHACL gate
  4. Knowledge graphAWS Neptune · SPARQL
  5. Graph-RAGNL → governed query · hybrid retrieval
  6. Evidence-backed answercitations to source records
02Platform · Dalfin.AI

Agentic Application Generation

LLM agents · Code generation · Full-stack · AI platform

Problem
Every internal app re-models the domain by hand — schema, API, screens — and by the third iteration the layers no longer agree.
System
A planner agent extracts the domain from a brief, deck, screenshot or Figma file into a graph; schema, API and UI agents generate from that one graph; 25 validators gate the output.
My role
Built the platform — domain model, agent orchestration, validation and governance
input types incl. screenshots & Figma
5input types incl. screenshots & Figma
automated validators per app
25automated validators per app
reference domains pass (2 after self-repair)
4 / 4reference domains pass (2 after self-repair)
  • Multi-agent orchestration
  • Open-source LLMs
  • VLM input
  • Temporal KG (Graphiti)
  • React
  • REST
View case studySystem design · code private
One graph drives every agent
  1. RequirementsScreenshotFigma
  2. Planner agentdomain extraction → knowledge graph IR
  3. Schema agentAPI agentUI agent
  4. Validation25 validators · targeted repair
  5. Human approvalgovernance gate
  6. Deployable applicationReact · REST · database
03Open research · urban twin in design

Flood Intelligence

Geospatial AI · Risk modelling · Data provenance

Problem
Cities need to know where water will collect, and what to do, before the storm. The obvious model — rainfall in, flooded area out — needs both numbers for the same flood.
System
A provenance-enforced flood dataset across 12 regions (2 in the UAE) whose validator rejects any figure without a source, evidence tier and method — the foundation for an urban flood twin.
My role
Sole author — research design, provenance validator, dataset, statistics
flood events · 12 sites
48flood events · 12 sites
have both rainfall & extent
2 / 48have both rainfall & extent
coverage anti-correlation, p = 0.004
ρ −0.76coverage anti-correlation, p = 0.004
  • Python
  • Geospatial
  • Sentinel-1 / satellite
  • Provenance validator
  • Statistics
View case studyCode
Target architecture · urban flood twin
  1. RainfallTerrainRoadsBuildings
  2. Feature layergrid · drainage · land use
  3. Flood risk modelblocked until paired data exist
  4. Hotspotsranked cells · drawdown time
  5. Ontology / agent layerassets, owners, interventions
  6. Investigation + actionsscenario comparison
04Enterprise · public companions

Governed Content Intelligence

GenAI · Recommendation · Content lineage · Knowledge graph

Problem
In pharma, AI may only recommend or generate medically, legally and regulatory-approved content — and someone must prove which approved claim each sentence came from.
System
A content graph linking every asset to its evidence, approval and use; generation constrained to approved material; slide-level lineage on every output.
My role
Owned the content-lineage data product and GenAI market adaptation
per-market dossier adaptation
~3 mo → 1 wkper-market dossier adaptation
markets, incl. 6 low- & middle-income
~25markets, incl. 6 low- & middle-income
lineage to source and approval
Slide-levellineage to source and approval
  • GenAI
  • Knowledge graph
  • Lineage
  • Recommendation
  • FastAPI
  • python-pptx
View case studyCode
Recommend only what can be traced
  1. Customerprofile & specialty signals
  2. Interactionsengagement history
  3. Content / metadata graphasset → claim → evidence → approval
  4. Ranking / recommendationeligibility first, relevance second
  5. Generated recommendationapproved content only
  6. Evidence / content lineageevery sentence traceable
05Production · Dalfin.AI + research rebuild

Store × SKU Demand Forecasting

Applied ML · Forecasting · LightGBM · MLOps

Problem
A large retailer replenishes 4,000+ SKUs across 33 stores. Per-series models do not scale, and naive forecasts ignore promotions, seasonality and how products steal demand from each other.
System
Global LightGBM over lag, rolling and hierarchy features with a p90 quantile head for safety stock, served per SKU-store by FastAPI and retrained through Airflow + MLflow.
My role
Lead data scientist — models, serving, retraining pipeline, graph-feature ablation
SKUs × stores in production
4,000+ × 33SKUs × stores in production
WMAPE, naive → LightGBM (simulated rebuild)
0.227 → 0.184WMAPE, naive → LightGBM (simulated rebuild)
graph-feature lift — small, and reported
+0.4%graph-feature lift — small, and reported
  • Python
  • SQL
  • LightGBM
  • XGBoost
  • FastAPI
  • Airflow
  • MLflow
  • Docker
View case studySystem design · code private
From history to a served forecast
  1. Sales historyPromotionsCalendarProduct / store graph
  2. Feature pipelinelags · rolling stats · graph embeddings
  3. Global LightGBMpoint forecast + p90 quantile head
  4. Validation gatetime holdout · 7-day embargo · vs naive
  5. MLflow registrypromote on pass
  6. FastAPI forecastsSKU × store · confidence · metadata
06Open source

Self-Repairing Safety Extraction

Agentic AI · SHACL · Local LLMs · Evaluation

Problem
Adverse-drug-event narratives must become structured records. A model will write one that reads perfectly and contains an invented date.
System
An agent loop extracts to RDF and repairs until a SHACL validator accepts the graph. The verifier never calls a language model.
My role
Sole author
valid records, 3B model + loop
0% → 85%valid records, 3B model + loop
accuracy cost of repair (14B)
85.5% → 83.5%accuracy cost of repair (14B)
LLM calls in the verifier
0LLM calls in the verifier
  • Python
  • RDF
  • SHACL
  • SPARQL
  • Local LLMs
  • Benchmark harness
View case studyCode
Validator, not an opinion
  1. Safety narrative
  2. LLM extractionlocal 3B / 14B
  3. RDF record
  4. SHACL + SPARQL rulesdeterministic verifier
  5. Violations → repair promptuntil conformant or budget spent
  6. Conformant graph