Orbweaver · Kadi · KrishiMitra · Vayu · MargaDrishti · VaidyaMitra · AGENTIQ
I build AI systems for problems that already have victims — crop loss, air quality, road capacity, fraud rings, misdiagnosis, thin-file credit — and I build them so a sceptical reader can check every claim I make.
I work across the whole range rather than one corner of it:
| What I reach for | Where it shows up | |
|---|---|---|
| Classical ML | XGBoost · LightGBM · CatBoost · scikit-learn · igraph | Orbweaver's account scorer, KrishiMitra's crop model, CreditSetu's risk tiering |
| Deep learning | PyTorch · ResNet50 / VGG16 / EfficientNetV2 · CNN-LSTM · GraphSAGE · Grad-CAM | OpenForensics' three-backbone ensemble, the retinopathy grader, Vayu's forecaster |
| Generative AI | Gemini 3.1 · Amazon Bedrock (Nova Pro) · RAG · vision OCR | VaidyaMitra reads strips and reports, Kadi's grounded bilingual assistant, Specledger's extraction |
| Agentic systems | LangGraph · MCP tool layers · planner + executor splits | SmartAlloc's 7-agent pipeline, AGENTIQ's permission-checked tool layer, Inflx |
The thing that stays constant across all four is not the technique.
A model is allowed to propose. Something deterministic — a threshold, a knapsack, a peeling objective, an assertion evaluator — is what decides. That separation is the single design decision I repeat most, because it is what makes "why did this happen?" answerable by a person.
flowchart LR
E["evidence in"] --> D["deterministic<br/>parse · validate · features"]
D --> M["<b>the model proposes</b><br/>XGBoost · CNN · LLM · agent"]
M --> G{"calibrated —<br/>enough evidence?"}
G -->|no| A["<b>abstain</b><br/>route to a human"]
G -->|yes| DEC["<b>deterministic decides</b><br/>peeling · knapsack · assertions"]
DEC --> O["output + what it cost<br/>evidence · ₹ · false positives"]
classDef learned fill:#3a1f12,stroke:#e2621c,stroke-width:2px,color:#f3ede7
classDef proved fill:#122a1b,stroke:#4ade80,stroke-width:2px,color:#f3ede7
classDef plain fill:#1c1a18,stroke:#5a534c,color:#e8e2dc
classDef soft fill:#1a1f26,stroke:#4a90ad,color:#e8e2dc
class M learned
class DEC proved
class E,D,O plain
class A,G soft
Why it is worth the extra work. Ring membership in Orbweaver comes from a peeling objective with a proved ½-approximation bound, so "why is this account in this ring?" is checkable arithmetic rather than a model's opinion. AGENTIQ generates test assertions with an LLM and then evaluates them with a tool, because a model grading its own output is not evidence. Specledger's extraction works with the LLM switched off entirely — the model adds recall, it is not load-bearing.
| Project | The hard part |
|---|---|
| KrishiMitra | CatBoost crop recommendations cross-checked against five years of government district returns, leaf disease at 93.75% over 10,162 images, FAO-56 irrigation advisory, Soil Health Card baselines from 13.35M tests. 12 languages, deployed free |
| Vayu | LightGBM + CNN-LSTM forecasting over a 15,360-cell satellite grid, Gaussian-plume ROI ranking, and difference-in-differences verification that an intervention actually worked — never a guessed AQI |
| Kadi | 59,985 siloed FIRs into one explainable link graph across 31 districts and 298 stations. Shared modus operandi ranks as a hypothesis, never as a name. The translator refuses to touch FIR numbers, dates and identifiers |
| VaidyaMitra ⟨org⟩ | Every identifier is masked before it reaches the model. Jan Aushadhi generic matching with substitution-safety warnings, vision OCR, ten Indian languages, on Amazon Bedrock |
| MargaDrishti | Bengaluru road-capacity loss on one H3 × hourly substrate — 298k violations, 8 model families, and a published audit of enforcement bias in its own training data |
| Diabetic-Retinopathy-Detection | Temperature-scaled confidence with reliability diagrams and ECE; low-confidence cases escalate to a human grader. Front page says not a medical device, because it is not |
| Medicure-AI | Photograph a strip → composition, NPPA price, Jan Aushadhi generic, interaction warnings — each with a calibrated confidence and an honest refusal when evidence is thin |
| CreditSetu | pip install creditsetu. Validated against 150,000 real borrowers with real default outcomes — 0.82 AUC using only 7 of 14 features, to close the circularity gap of testing on its own synthetic data |
| Project | The hard part |
|---|---|
| Orbweaver | Densest-subgraph extraction over a 35.7M-edge account graph: 0.7292 ring precision against a 0.2242 base rate, always reported with the 0.371 real customers swept in per fraudster caught. 36 dated failures published alongside |
| AGENTIQ ⟨org⟩ | B.Tech final-year project. Six vulnerability families probed by baseline differential, so a finding needs a material deviation rather than a suspicious-looking string. Every outbound request passes a permission-checked, SSRF-guarded, audited tool layer |
| artifact-repro-triage | Checks whether a paper's repository contains what its README promises. 0% → 100% detection of fabricated file claims across 742 artifacts |
| OpenForensics | Three-backbone deepfake ensemble with calibrated confidence and per-backbone Grad-CAM — the dashboard shows the evidence, not just the verdict |
| Specledger | A logistic calibrator over 11 evidence features picks an auto-publish threshold hitting a measured precision floor on held-out data — instead of trusting an LLM's self-reported confidence |
| MedGuardX | Context-aware PII/PHI masking: an engine on PyPI, a hardened FastAPI service with JWT RBAC, and an app on top |
| Project | The hard part |
|---|---|
| SmartAlloc | A 7-agent LangGraph pipeline over linear programming that finds compute waste and predicts SLA bottlenecks before they land |
| Talent-Intelligence | 100,000 candidates ranked in under 18 seconds, CPU only, with honeypot and fake-profile filtering |
| PrimeTradeDS | 211K Hyperliquid trades against Bitcoin Fear/Greed sentiment — what moves trader behaviour, and what does not |
| Adaptive-Graph-Search-Suite | Graph traversal on realistic map topologies, built to be watched while it runs |
The claim is cheap; these are the times it cost me something.
| Where | What I published anyway |
|---|---|
| MargaDrishti | A target of PR-AUC ≥ 0.45 was set assuming ~10% prevalence. The real label rate is 0.291%, so the goal was unreachable by construction. Reported as a 46.9× lift over base rate with the original goal marked wrong — not as a 3× shortfall |
| MargaDrishti | Seven model families all returned PR-AUC 0.9999 on one task. That is the signature of a recovered business rule, not a hard problem — so it is reported as a recovered rule, because presenting it as modelling performance would mislead |
| MargaDrishti | The review process changed regime mid-window, so every model on that task is miscalibrated. Reported as not-yet-answerable rather than as a weak result |
| Orbweaver | Four of thirteen investigations came back negative and are published beside the nine that worked, including one where the hypothesis was exactly backwards |
| CreditSetu | The live demo runs on synthetic data, and the README says so above the numbers rather than below them |
Shipped on FastAPI, Streamlit and Next.js; deployed to Cloud Run, Vercel,
Render and AWS; packaged to PyPI where it makes sense. But the stack matters
less than the discipline around it — a temporal split a test enforces, a
held-out set nothing touches, the false-positive cost printed next to the
detection rate, and a FAILURES.md recording what I got wrong on the way.
The banner, the timeline and PROJECTS.md are generated from the GitHub API — my own repositories and both organisations — so none of them can quietly fall behind what I have actually shipped. The three avatars (mine and the two organisations') are drawn by the same scripts, in one visual language:
make refresh # pull the current repository list
make assets # redraw the banner and the timeline
make index # rewrite PROJECTS.md
make # the last twoClaiming reproducibility on 28 projects and then hand-maintaining my own profile would have made this the one dishonest page on the account.
Final-year computer science at LNMIIT Jaipur. I build under two organisations — VaidyaMitra for clinical work and B-TechProject for my final-year project. Most of what I build ends up deployed somewhere free, because a model nobody can open is a claim nobody can check.
