COMPLYDNA BY OZDNA TR
OZDNA.COM — A COMPLIANCE LLM FOR TURKISH REGULATION

Compliance, in the model's DNA.

ComplyDNA is a compliance LLM trained on Turkish regulation. It knows the MASAK communiqués, the AML/CFT laws and KVKK with their chain of sources, and answers every sentence with an inline [COMMUNIQUÉ / Article] citation. Auditable answers, not guesses.

[01]WHAT IT DOES

A general LLM guesses.
ComplyDNA cites its sources.

A / CITE-FIRST

Inline citations

Every claim arrives with a [COMMUNIQUÉ / Article] tag. Which piece of regulation an answer rests on is traceable at sentence level — the compliance team verifies the source in one click.

B / TR-NATIVE

Native to Turkish regulation

MASAK communiqués and circulars, laws 5549 and 6415, KVKK, the ROM Regulation and crypto-asset rules are in the model's training set — no translation layer, regulatory language handled directly.

C / AUDIT-READY

Audit-ready

Answer + source list + version info are produced together. Thanks to effective-date metadata, "which provision applied on which date" can be answered too.

[02]METHOD — PIPELINE

From regulation to auditable answer
in a four-stage pipeline

ComplyDNA is not a RAG wrapper you upload PDFs into and chat with. Every stage is designed around a compliance team's audit requirements: article-level metadata, a retrieval-confidence threshold, mandatory citations and version traceability are part of the pipeline — not a check bolted on afterwards.

SEQ 01 Regulation corpus

Raw communiqué, law and regulation texts are converted into structured article/paragraph records.

  • Parse: every chunk carries source_code, article_no, paragraph
  • Metadata: validity_status, effective_date, version
  • Filter: repealed articles never enter the index — stale provisions can't produce wrong answers
SEQ 02 Vector index

Article chunks are written into a semantic index; queries can be in Turkish or English.

  • Embedding: multilingual model (bge-m3) — the same article is found by TR and EN queries
  • Store: Qdrant + metadata filters; on-prem disk or a compose stack
  • Threshold: low-confidence retrieval never reaches answer generation — no empty guessing
SEQ 03 Fine-tuning + cite-first

The model learns regulatory language and the citation format; uncited output cannot pass the pipeline.

  • LoRA: the [SOURCE / Article X] reference format and regulatory Turkish
  • Split: train/validation separated at article level — no article appears in both sets
  • Post-check: uncited sentences are rejected; an automatic retry attempts a fix
SEQ 04 Cited answer

Retrieved article texts + the fine-tuned model = an auditable output package.

  • Input: question + top-k article texts (full text, not summaries)
  • Output: inline citations + source list + validity info
  • Audit: question/answer SHA-256 hashes — no plaintext logging, still traceable
Generic RAG / ChatGPT
SourceUpload PDFs or guess from the web — no guarantee of the current communiqué
CitationsOptional; most sentences remain uncited
EvaluationDemo vibes — no golden set, no article-level metrics
DataQueries go to a third-party API; limited on-prem options
ComplyDNA
SourceVersioned regulation corpus — article/paragraph JSONL with validity metadata
CitationsEvery sentence requires [COMMUNIQUÉ / Article]; enforced by post-check
EvaluationCompliance-expert golden set — retrieval + citation accuracy on every release
DataOn-prem: model, index and inference stay inside your perimeter
STACK FastAPI Qdrant bge-m3 LoRA cite-first post-check golden-set eval on-prem / Ollama
Evaluation Golden set: continuous measurement against real questions written by compliance experts. Retrieval accuracy (@k), citation accuracy and answer-refusal rate are reported on every release; metric history is versioned — production discipline, not a one-off pre-launch test.
Data hygiene Article-level separation: the same piece of regulation never sits in the training and validation sets at once. What we measure is generalisation, not memorisation — the model can't see an article in the question and copy the answer; citation accuracy is measured on neighbouring articles and differently-phrased questions.
Deployment On-premises or cloud: local Qdrant + API + demo UI run on a single machine and move onto the corporate network with Docker Compose. An OpenAI-compatible endpoint (Ollama/vLLM) can be attached — customer queries and documents never enter model training.
Freshness Versioned corpus: when a new communiqué or circular lands, parse → index → golden set are re-run. The effective date in an answer is read from metadata — "which provision applies today?" is answerable too.
[03]SAMPLE OUTPUT

Anatomy of an answer

complydna / session source mode: on

> query: Within what period must a suspicious transaction be reported?

Suspicious transactions are reported to the MASAK Presidency within ten business days at the latest from the date the suspicion arises — and immediately where any delay would be risky [MEASURES-REG. / Article 28]. The reporting obligation arises from the institution's obliged-party status under Law No. 5549 [LAW-5549 / Article 4].

--- SOURCES ---
[1] MEASURES-REGULATION / Article 28 · validity: in force
[2] LAW-5549 / Article 4 · validity: in force

[04]COMPLIANCE SUITE — ROADMAP

Ask today.
The suite is next.

ComplyDNA’s primary product is cite-first Ask. The modules below are design-partner scope — not generally available; methodology details are not published publicly.

JR / JURISDICTION RISK

Country / jurisdiction risk

Lower / Medium / Higher bands for onboarding and corridors — multi-source AML geographic risk alongside sanctions and FATF lists. Does not replace customer due diligence.

Coming soon · design partner
RA / RISK APPETITE

Risk appetite framework

A VASP / fintech compliance frame that ties Board-set appetite limits to EWRA and KRI monitoring — accept, restrict, and prohibit rows.

Coming soon · design partner
SA / STR ASSIST

STR / ŞİB assist

MASAK.ONLINE-ready ŞİB XML draft and validation support. The compliance officer reviews and uploads — no auto-submit.

Coming soon · design partner

Design-partner pilots discuss suite scope alongside Ask. Module methodology is shared only under NDA / pilot terms.

[05]PRICING

Clear plans. Visible prices.

P1 / PILOT
Pilot For teams that want to test the product on their own regulatory scope
$2,490 USD
One-time · 4-week POC
  • Single team, limited query quota
  • Golden-set evaluation tailored to your institution
  • 4-week structured POC
  • Weekly evaluation report
Start a pilot
P2 / TEAM
Team For institutions bringing it into the compliance team's daily workflow
$499 / mo
Billed annually · $5,988/year
  • Seat-based access + monthly query quota
  • Cloud API or web interface
  • Regulation-corpus update subscription included
  • Priority support
Get Team
P3 / ENTERPRISE
Enterprise / On-prem For banks and financial institutions whose data cannot leave the premises
$1,990 / mo
Starting at · custom scope available
  • On-premises deployment — you hold the model and index
  • Unlimited users, corporate SSO integration
  • Annual licence + maintenance and regulation updates
  • Custom integration (API, internal systems)
Contact sales

Prices in USD. Team is billed annually. Enterprise starts at $1,990/mo and scales with regulatory corpus scope, seats, and deployment (cloud or on-prem). Questions: hello@ozdna.com.

[06]FREQUENTLY ASKED QUESTIONS

FAQ

What is ComplyDNA?

ComplyDNA is a compliance LLM trained on Turkish regulation (MASAK communiqués, AML/CFT laws, KVKK). It answers compliance questions with an inline [COMMUNIQUÉ / Article] citation on every sentence — the piece of regulation behind each answer is always verifiable.

Are Jurisdiction Risk, Risk Appetite, and STR Assist available today?

No. Ask is the primary product. Those three modules are on the roadmap and discussed only in design-partner pilots; they are not generally available.

Which regulation does it cover?

Law No. 5549 on the Prevention of Laundering of Crime Proceeds, Law No. 6415 on the Prevention of the Financing of Terrorism, the Turkish data-protection law KVKK (6698), MASAK communiqués and circulars, the Measures Regulation, and crypto-asset service provider (VASP/CASP) rules. Coverage is versioned and updated as regulation changes.

How is it different from a general LLM (ChatGPT etc.)?

General models can't cite sources on Turkish regulation questions and don't ground answers in current communiqué text. ComplyDNA builds each answer by first retrieving the relevant article text, ties every claim to its source, and writes in regulatory language. Quality is measured on every release against a golden set written by compliance experts.

Is our data used to train the model?

No. Customer queries and documents never enter model training. On the Enterprise plan the model and index run entirely inside your own infrastructure; data never leaves your perimeter.

How is it deployed — cloud or on-premises?

Both: a fast start via cloud API / web interface, or a fully on-premises installation for banks and financial institutions. Pilot programs usually start in the cloud.

Are the answers legal advice?

No. ComplyDNA outputs are informational and do not constitute legal advice; final decisions require verification by a compliance professional. The inline citations exist precisely to speed up that verification.

[07] — CONTACT

Give your compliance team
a chain of sources, not just an LLM.

The early access program is open. We prepare a demo tailored to your institution's regulatory scope — one email is enough.