Hire a RAG Developer, Toronto, ON

Hire a RAG developer in Toronto.

A compliance analyst at Scotiabank needs to know whether the bank's internal mortgage underwriting guidelines satisfy the current OSFI B-20 residential mortgage requirements. The relevant source documents are published in both English and French. The analyst works in English. The RAG system needs to return the correct French-language regulatory passage in response to an English query.

Toronto is home to TD Bank, Scotiabank, RBC, CIBC, and BMO. All five operate under OSFI guidelines that differ from US equivalents: OSFI stress testing is not CCAR, CDIC is not FDIC, and CSA securities law is not SEC. A RAG system built for a Toronto bank must handle Canadian regulatory language, English and French retrieval, and cross-border disambiguation when the same topic has different answers in Canada vs. the US.

We build RAG systems for Canadian financial services document libraries, scoped to your document library and compliance requirements.

How we work

Tell us about your Canadian regulatory document challenge.

The Toronto regulatory document environment.

Toronto's Big Five banks generate and consume regulatory documentation at a scale that few other financial centres match. OSFI publishes supervisory guidelines, advisory letters, and capital adequacy requirements. CDIC publishes deposit insurance framework documentation. The CSA publishes securities regulation across provincial jurisdictions. FINTRAC publishes anti-money laundering compliance guidance. Each of these bodies produces documents that compliance and legal teams at RBC, CIBC, and BMO need to query regularly.

The bilingual requirement adds a layer of complexity that US financial RAG does not have. Federally regulated entities must serve employees in both official languages. Internal policies are published in English and French. OSFI guidelines are available in both. A retrieval system that only handles English queries returns incomplete results for French-speaking compliance staff in Quebec operations.

Many Toronto financial companies have US subsidiaries. TD Bank operates TD Bank, N.A. in the US under OCC supervision. RBC has US wealth management operations. Scotiabank has US broker dealer operations under FINRA. These entities face different regulatory requirements than their Canadian parent companies, and the same query topic (stress testing capital requirements, mortgage underwriting standards, AML thresholds) may have materially different answers in the two jurisdictions.

A RAG system that does not handle jurisdiction disambiguation will merge Canadian and US regulatory answers in a way that could mislead the user. The system needs to either filter by jurisdiction or return clearly labeled results from both jurisdictions when the query is cross-border.

Canadian vs. US regulatory document RAG.

Jurisdiction tagging at ingestion

Every document is tagged with a jurisdiction field at ingestion: CA, US, or both. For entities with mixed-jurisdiction content (TD Bank Group documents that address both OSFI and OCC requirements), we tag at the section level. At query time, jurisdiction filters prevent incorrect cross-border answers.

OSFI-specific document library coverage

OSFI supervisory guidelines, capital adequacy requirements (CAR guideline), stress testing guidance (DFAST/CCAR equivalents), and residential mortgage underwriting guidelines (B-20) are treated as primary sources. OSFI documents are versioned: earlier guidance letters do not supersede current guidelines. Version metadata is tracked at ingestion.

CSA and provincial securities law

Canadian securities regulation is provincially administered through the CSA. Ontario Securities Commission, AMF (Quebec), and BCSC rules are tagged by province. A query from a RBC Capital Markets compliance officer about prospectus requirements returns Ontario-specific rules by default, with an option to surface other provincial rules for cross-listing analysis.

FINTRAC AML compliance documentation

FINTRAC publishes AML and ATFF guidance that differs from US FinCEN requirements. The two are tagged separately. A query about beneficial ownership reporting thresholds returns the current FINTRAC threshold for Canadian entities and the FinCEN threshold for US subsidiaries, labeled by jurisdiction.

Bilingual retrieval for federally regulated entities.

Multilingual embedding model selection

Monolingual English embedding models score French-language chunks lower for English queries even when the content is equivalent, the model lacks the training signal to encode cross-language semantic proximity. We use mE5 (multilingual E5) or LaBSE for bilingual corpora. Both are trained on aligned translation pairs and encode English and French representations of the same regulatory concept close together in embedding space. An English query about OSFI mortgage underwriting returns the correct French-language B-20 passage.

Language-agnostic query handling

A Scotiabank compliance analyst working in French can query in French and retrieve content from English-language documents. A bilingual policy team can submit queries in either language and receive results without language-specific configuration. The system handles both directions uniformly because the embedding space is shared across languages.

Document-level language tagging for source attribution

Even when retrieval is language-agnostic, the source citation tells the user which language version of the document the result came from. A result sourced from the French version of an OSFI guideline is labeled as such. For teams that need to cite the official French or English version in a regulatory submission, this matters.

Translation boundary handling

When a document exists in both English and French versions, we ingest both and deduplicate at the semantic level. The two language versions of the same OSFI guideline are linked by document ID so a query that retrieves from one version can also surface the corresponding passage in the other language on request.

How we work.

The discovery call starts with your document corpus: which regulatory bodies are in scope, whether the corpus is bilingual, whether it includes US subsidiary regulatory documents, and what queries your compliance or legal team submits most frequently.

After the call we deliver a written scope within two business days. For Canadian financial services projects, the scope includes: jurisdiction tagging strategy, embedding model recommendation with reasoning on bilingual performance, version management for OSFI guideline updates, access control requirements, and evaluation criteria drawn from real query examples from your team.

Build takes four to eight weeks. We run evaluation on 80-100 queries including bilingual test cases and cross-border disambiguation queries before handoff. You receive benchmark numbers across both language directions.

Full process details →

Questions about Canadian regulatory document RAG.

How do you handle queries that might have different answers under Canadian vs. US regulations in the same document corpus?: We tag every document at ingestion with jurisdiction metadata: CA, US, or a list when a document covers both. The document types that require this are well-defined: OSFI guidelines are CA, SEC rules are US, CDIC documentation is CA, FDIC documentation is US. For documents from entities with both Canadian and US subsidiaries (TD Bank's US operations fall under OCC supervision; its Canadian operations fall under OSFI), we tag at the section level where documents mix jurisdiction-specific content. At query time, the system checks whether the query contains jurisdiction-specific language and applies the appropriate filter. If a TD compliance team member asks about stress testing capital requirements, the system returns OSFI B-20 guidance for the Canadian entity and OCC stress testing guidelines for the US subsidiary separately, labeled by jurisdiction. Ambiguous queries without a clear jurisdiction signal return results from both jurisdictions with explicit labels rather than silently merging them.
What embedding models handle English/French bilingual retrieval well, does query language matter if documents are in both?: Query language does matter if you use a monolingual English embedding model. An English query against a French document chunk will score lower than it should because the embedding model encodes the two as semantically distant. Multilingual models trained on paired translation data: mE5 (multilingual E5 from Microsoft) and LaBSE (Language-agnostic BERT Sentence Embeddings), encode English and French representations of the same concept close together in embedding space. A query in English about OSFI mortgage underwriting requirements returns relevant French-language chunks from the bilingual OSFI B-20 guideline. For federally regulated Canadian financial institutions that publish internal policies in both official languages, this matters operationally: a Scotiabank compliance analyst should be able to query in their preferred language and retrieve content regardless of the language it was written in.
PIPEDA restricts purpose limitation: can you build a RAG system on customer data for an AI use case different from the original data collection purpose?: PIPEDA's purpose limitation principle under Section 5 requires that personal information be used only for the purposes identified at collection, or purposes a reasonable person would consider appropriate in the circumstances. Using customer data collected for lending decisions to train or populate a RAG system for a different AI use case, say, marketing personalization, requires either new consent or a defensible 'reasonable purpose' analysis. We do not provide legal advice on specific PIPEDA compliance determinations. What we do: scope RAG systems explicitly to avoid ingesting personal information unless the purpose analysis has been completed by your legal team. For financial document retrieval use cases (regulatory documents, internal policy libraries, anonymized case studies), personal information is typically not in scope. We flag any data set that contains personal information during scoping and ask for confirmation of the legal basis before ingesting it.
Do OSFI's AI risk management guidelines create any specific requirements for a RAG system used in credit risk or compliance workflows?: OSFI's guidance on the use of models (SR 11-7 equivalent guidance and OSFI's own model risk management guidance) requires that models used in credit risk and compliance workflows be subject to model risk management practices: documentation, validation, ongoing monitoring, and governance. A RAG system used to inform credit risk decisions: even if a human reviews the output, may be considered a model for this purpose depending on how it is used and documented. The practical implications: document the retrieval architecture and its limitations, establish a validation process for retrieval accuracy on relevant query types, monitor for retrieval quality degradation as the document corpus changes, and define a governance process for updates to the system. We build evaluation harnesses that support ongoing monitoring as part of every compliance-adjacent engagement. We recommend you involve your model risk management team in the governance design, as they will have views on what documentation and validation are required for your specific use case.

Scope your Toronto regulatory document project.

Describe your regulatory document library, the jurisdictions in scope, whether bilingual retrieval is required, and the queries your team needs to answer. We reply within one business day.

Hire a RAG developer in Toronto.

We build RAG systems for Canadian financial services document libraries, scoped to your document library and compliance requirements.

The Toronto regulatory document environment.

Canadian vs. US regulatory document RAG.

Jurisdiction tagging at ingestion

OSFI-specific document library coverage

CSA and provincial securities law

FINTRAC AML compliance documentation

Bilingual retrieval for federally regulated entities.

Multilingual embedding model selection

Language-agnostic query handling

Document-level language tagging for source attribution

Translation boundary handling

How we work.

Questions about Canadian regulatory document RAG.

How do you handle queries that might have different answers under Canadian vs. US regulations in the same document corpus?

We tag every document at ingestion with jurisdiction metadata: CA, US, or a list when a document covers both. The document types that require this are well-defined: OSFI guidelines are CA, SEC rules are US, CDIC documentation is CA, FDIC documentation is US. For documents from entities with both Canadian and US subsidiaries (TD Bank's US operations fall under OCC supervision; its Canadian operations fall under OSFI), we tag at the section level where documents mix jurisdiction-specific content. At query time, the system checks whether the query contains jurisdiction-specific language and applies the appropriate filter. If a TD compliance team member asks about stress testing capital requirements, the system returns OSFI B-20 guidance for the Canadian entity and OCC stress testing guidelines for the US subsidiary separately, labeled by jurisdiction. Ambiguous queries without a clear jurisdiction signal return results from both jurisdictions with explicit labels rather than silently merging them.

What embedding models handle English/French bilingual retrieval well, does query language matter if documents are in both?

Query language does matter if you use a monolingual English embedding model. An English query against a French document chunk will score lower than it should because the embedding model encodes the two as semantically distant. Multilingual models trained on paired translation data: mE5 (multilingual E5 from Microsoft) and LaBSE (Language-agnostic BERT Sentence Embeddings), encode English and French representations of the same concept close together in embedding space. A query in English about OSFI mortgage underwriting requirements returns relevant French-language chunks from the bilingual OSFI B-20 guideline. For federally regulated Canadian financial institutions that publish internal policies in both official languages, this matters operationally: a Scotiabank compliance analyst should be able to query in their preferred language and retrieve content regardless of the language it was written in.

PIPEDA restricts purpose limitation: can you build a RAG system on customer data for an AI use case different from the original data collection purpose?

PIPEDA's purpose limitation principle under Section 5 requires that personal information be used only for the purposes identified at collection, or purposes a reasonable person would consider appropriate in the circumstances. Using customer data collected for lending decisions to train or populate a RAG system for a different AI use case, say, marketing personalization, requires either new consent or a defensible 'reasonable purpose' analysis. We do not provide legal advice on specific PIPEDA compliance determinations. What we do: scope RAG systems explicitly to avoid ingesting personal information unless the purpose analysis has been completed by your legal team. For financial document retrieval use cases (regulatory documents, internal policy libraries, anonymized case studies), personal information is typically not in scope. We flag any data set that contains personal information during scoping and ask for confirmation of the legal basis before ingesting it.

Do OSFI's AI risk management guidelines create any specific requirements for a RAG system used in credit risk or compliance workflows?

OSFI's guidance on the use of models (SR 11-7 equivalent guidance and OSFI's own model risk management guidance) requires that models used in credit risk and compliance workflows be subject to model risk management practices: documentation, validation, ongoing monitoring, and governance. A RAG system used to inform credit risk decisions: even if a human reviews the output, may be considered a model for this purpose depending on how it is used and documented. The practical implications: document the retrieval architecture and its limitations, establish a validation process for retrieval accuracy on relevant query types, monitor for retrieval quality degradation as the document corpus changes, and define a governance process for updates to the system. We build evaluation harnesses that support ongoing monitoring as part of every compliance-adjacent engagement. We recommend you involve your model risk management team in the governance design, as they will have views on what documentation and validation are required for your specific use case.

Scope your Toronto regulatory document project.

Describe your regulatory document library, the jurisdictions in scope, whether bilingual retrieval is required, and the queries your team needs to answer. We reply within one business day.