Pure reasoning trajectories, statutory grounded invariance, and cryptographically verified Merkle DBOM provenance for frontier LLM fine-tuning.
Select an engine to inspect its live cognitive output, thought steps, and structured invariants.
Multi-step deliberate cognitive paths with self-correction verification.
Inquisitive guided dialogue probing edge-cases and latent assumptions.
Rigorous chosen vs rejected comparative pairs for policy alignment.
Extended conversational sessions with state tracking & context drift.
Executable JSON function schemas with arguments & structured outputs.
Direct negative boundary calibration against anti-domain pollution.
Statutory citations and factual grounding against verified document nodes.
{
"instruction": "Verify if input tax credit on purchases made in FY 2022-23 can be claimed in GSTR-3B filed on 25th November 2023 under Section 16(4) CGST Act.",
"thought": "1. Section 16(4) specifies deadline as 30th November following the financial year end or furnishing annual return.\n2. Finance Act 2022 amendment extended this from September due date to 30th November.\n3. Return filed on 25th Nov 2023 is prior to 30th Nov deadline.\n4. Conclusion: ITC claim is statutorily valid.",
"response": "Under Section 16(4) of the CGST Act (as amended by Finance Act 2022), the statutory deadline is 30th November following the financial year. Since GSTR-3B was filed on 25th November 2023, the ITC claim is timely and permissible."
}Non-linear state machine executing discovery, multi-engine cognitive loops, neural validation, and cryptographic attestation.
Trafilatura · PDF Plumber · LangDetect
CoT · Socratic · DPO · Tool Schemas
Consensus L2 · Decontamination Probe
SHA-256 Merkle Root · HF & GGUF
Crawls authority repositories (AI Kosh, Bhashini, domain registries). Automatically extracts syntactic invariants, PII masking rules, and anti-domain boundaries.
Distributes synthesis across Celery / Ray multi-worker nodes. Executes cognitive prompt loops (CoT, Socratic, DPO, Tool Calling) with deduplication.
Combines semantic cosine deduplication (>0.92) and domain threshold (>0.40) with multi-criteria LLM Judge evaluation. Rejects hallucinations and script drift.
Hashes every sample with SHA-256 into a cryptographic Merkle root. Emits Data Nutrition Labels and packages into HF, Parquet, or DPO format.
Every synthesized batch is cryptographically anchored into a SHA-256 Merkle tree. Certificates attest to zero PII leakage and statutory fidelity.
9b87df7a3982f6e9...9b87df7a3982f6e9...5542a1ef248f27af...9b87df7a3982f6e9...b418a096c429712a...✦ Sibling Proof5542a1ef248f27af......9b87df7a3982f6e9b418a096c429712aa1b2c3d4e5f60718H(Leaf 0 || Leaf 1) ⟶ Branch 0Proof Depth: Level 2 • Sibling Hash SealedEvery sample is filtered against neural cosine embeddings and graded by our pure LLM Judge rubric before admission into the dataset.
Cognitive multi-step reasoning steps vs generic web crawling.
Verified statutory and circular citations with zero hallucination.
Zero Romanized transliteration leakage on native Indic corpora.
Cosine similarity threshold eliminating redundant synthetic tokens.
Trigger autonomous dataset pipelines programmatically from your training workflows and CI/CD runs.
from rasosynth import RasoSynthClient
client = RasoSynthClient(api_key="rs_live_49f82d1...")
# Launch an autonomous synthesis pipeline
job = client.synthesize(
domain="Finance & Taxation (AI Kosh)",
synthesis_mode="cot", # Options: cot, socratic, preference, rag, tool_calling
dataset_size=10000,
language="indic_multilingual",
mask_indian_pii=True,
dbom_attestation=True
)
print(f"Synthesis Job Active: {job.id}")
print(f"Merkle Provenance Root: {job.merkle_root}")