A small language model built deep, not wide.
Not a general-purpose LLM with a compliance prompt bolted on. ReQL's Small Language Model starts deeply grounded in Indian BFSI regulation — with every answer traceable back to the circular, clause, and date it came from — and is built to extend the same approach across industries and geographies over time.
SLM
Why we're building this.
A world where no financial institution — however small, however remote — is one missed circular away from a compliance failure. Where regulatory knowledge isn't locked inside a handful of expensive consultants and legal teams, but available instantly, accurately, and in plain language to whoever needs it.
To build the regulatory intelligence layer for Indian BFSI — starting with a language model that actually understands how RBI, SEBI, MCA, and IRDAI regulation is written, cross-referenced, and amended, and that never answers a compliance question without showing its source.
Narrow domain. Deep coverage. Every answer cited.
General-purpose LLMs know a little about everything. A domain SLM is built the opposite way — and for regulated work, that matters more than raw parameter count.
What the model actually reads.
Every clause is chunked, embedded, and stored with its citation metadata intact — regulator, circular number, section, and effective date.
RBI · MCA · SEBI 2024 Public statute and regulator counts.
Small, deep, and grounded — not big and general.
A general-purpose LLM knows a little about everything. Our SLM is built the opposite way: narrow domain, deep coverage, and a hard requirement that every claim traces to a source document.
Corpus ingestion
Circulars, master directions, and amendments from RBI, SEBI, MCA, ICAI, CBDT, CBIC, FEMA, Ind-AS, and IRDAI, structured from 2021 to present.
Structured indexing
Every clause is chunked and embedded with its citation metadata intact — regulator, circular number, section, and effective date.
Domain fine-tuning
The model is tuned specifically on regulatory Q&A, gap analysis, and clause comparison — the actual tasks compliance teams perform daily.
Citation-grounded evaluation
Every output is scored not just on correctness, but on whether it cites the right source — answers without a valid citation are treated as failures.
Every answer either finds a source, or admits it can't.
A model tuned on a fixed corpus doesn't need to guess. On our internal regulatory-Q&A eval set, the SLM either returns a valid citation or explicitly declines.
ReQL bench · Internal eval Measured on ReQL's regulatory-Q&A eval set; representative, not audited.
Training is the start. It keeps learning after that.
The corpus doesn't go stale and the model doesn't stay static — both update on a continuous loop, fed by new regulation and by how compliance teams actually use it.
New circular published
A new circular, amendment, or master direction is picked up as soon as the regulator releases it.
Ingested and cross-linked
It's chunked, cited, and cross-referenced against every existing clause it amends or supersedes.
Reviewed by compliance officers
Where an answer is corrected or flagged by a real user, that correction feeds back into evaluation.
Model re-evaluated
Accuracy and citation quality are re-scored against the updated corpus before anything ships to users.
Why a narrow, grounded model beats a bigger one here.
No hallucinated compliance advice
A general LLM will confidently invent a plausible-sounding rule. A model grounded in a fixed corpus either finds the source or says it can't find one — there's no in-between.
Keeps up with amendments, not just headlines
Indian regulation moves in circulars and master direction updates, not press releases. A domain-specific model is built to track that granularity continuously.
Self-hostable, because compliance data is sensitive
A smaller model can run inside your own perimeter — no policy document or client contract needs to leave your infrastructure to get an answer.
Deep before wide
Every design decision — from corpus selection to evaluation criteria — starts from how RBI, SEBI, and IRDAI actually publish and structure regulation. Depth first is what makes expansion into new industries and geographies reliable later, not a rewrite.
Deep in Indian BFSI today. Built to go wider from here.
The corpus, the citation engine, and the evaluation approach are all built to generalize — the roadmap is to extend the same grounded, cited approach to new industries and new geographies, not to rebuild it from scratch each time.
Now — Indian BFSI
RBI, SEBI, MCA, ICAI, CBDT, CBIC, FEMA, Ind-AS, and IRDAI, covering banks, NBFCs, fintechs, and insurers.
Next — more industries
Extending the same corpus-and-citation approach to sectors beyond BFSI as the model architecture generalizes.
Then — new geographies
Applying the same training methodology to other regulatory regimes, starting where demand is clearest.
Vision — cross-industry, cross-border
One grounded, citation-first regulatory intelligence layer, wherever compliance work happens.
See the SLM answer a real regulatory question.
Book a demo and bring a real circular or policy document — we'll run it live.