Hire a RAG Developer · Denver, CO

Hire a RAG developer in Denver.

Denver's two largest document-heavy industries share a common problem: regulatory frameworks that change frequently, vary by jurisdiction, and are dense enough that manual search is not practical at operational scale.

Colorado's cannabis sector (Wana Brands, Native Roots, Medicine Man Technologies) tracks MED rules that update quarterly plus local municipality ordinances that differ by dispensary location. A compliance question like "what display rules apply at our Boulder location" requires querying both state and municipal documents simultaneously. Denver's energy sector (Occidental, Civitas Resources, DCP Midstream) manages FERC filings, environmental compliance documents, and pipeline regulations that are similarly jurisdiction-specific.

A RAG system built for multi-jurisdictional regulatory documents answers compliance queries with the correct source and effective version. Pricing is scoped to the jurisdictions and document volume involved.

How we work

Tell us about your regulatory document challenge and we will scope a solution.

Why Denver's regulatory documents are a hard retrieval problem.

Most enterprise document libraries are stable: a contract is executed and filed; a policy is updated once a year. Regulatory documents in cannabis and energy are different. MED rules change on a quarterly schedule. FERC filing requirements update as orders are issued. Local municipality cannabis ordinances can change without the kind of advance notice that corporate policy documents receive.

For multi-location operators, the problem goes beyond keeping up with rule changes. It is knowing which version of which rule applies to which location. A Native Roots dispensary in Denver operates under Colorado state rules plus Denver municipal ordinances. A location in Boulder operates under the same state rules plus different Boulder municipal ordinances. The same compliance question gets a different answer depending on the location.

The energy sector version of this problem is FERC pipeline regulations, which apply to specific pipeline segments in specific operating areas. A compliance question about maximum operating pressure or pipeline integrity testing frequencies has a different answer depending on the pipeline segment, its operating classification, and whether it crosses state lines. DCP Midstream and Civitas Resources both manage compliance documentation that varies at this level of geographic specificity.

In both industries, the document structure adds another layer of complexity: regulatory documents define terms in one section and use them throughout. "Licensed premises," "retail marijuana establishment," "transportation manifest," and "licensee" are defined with precision in the MED rules and their definitions materially affect the meaning of every section where they appear.

Cannabis and energy: two distinct use cases.

Cannabis: multi-location compliance queries

A compliance manager at a multi-location operator queries current MED rules plus the municipal ordinance for a specific dispensary location. The system returns the relevant section from both sources with their effective dates. When rules change, the prior version is marked superseded and the compliance team receives a structured diff of what changed.

Cannabis: transportation and track-and-trace

Colorado cannabis transportation requires specific manifest documentation and tracking compliance. A logistics coordinator at Wana Brands queries the current transportation rules for a specific product category and route. The system returns the applicable MED transportation rule section with the required manifest fields listed.

Energy: pipeline regulation by segment

A DCP Midstream compliance team member queries the federal and state regulations applicable to a specific pipeline segment. The query is pre-filtered by facility_id and operating_region metadata before the semantic search runs. Results are scoped to the regulations that govern that segment, not the full regulatory corpus.

Energy: FERC filing cross-reference

FERC orders and regulations reference other orders and tariff provisions extensively. A compliance analyst at Occidental queries a FERC requirement and the system surfaces the cross-referenced orders that elaborate on that requirement. Cross-reference links are indexed at ingestion time and retrieved alongside primary results.

Technical architecture for Denver regulatory RAG.

Jurisdiction-tagged chunk metadata

Every chunk is stored with jurisdiction metadata: state, county, municipality, agency, rule_number, and effective_date. A query pre-filter applies jurisdiction fields based on the user's location context before the semantic search runs. The system never returns a Boulder ordinance to a query scoped to a Denver location.

Scheduled rule update ingestion

A scheduled pipeline monitors MED's public document repository and FERC's eFiling system for new rule publications. When an update is detected, the pipeline ingests the new version, marks prior version chunks as superseded, and generates a change summary diff delivered to your compliance team. The diff lists section-level changes with the specific text that changed, rather than a generic update notification.

Defined term co-retrieval

At ingestion, regulatory definitions sections are extracted and stored as linked metadata on every chunk that references each defined term. A query that returns a chunk using a defined term automatically co-retrieves the definition chunk. Answers about regulatory requirements include the definition of any regulated term they invoke.

Document-type-specific chunking

Dense regulatory text (MED rules, FERC orders) chunks at 200-400 tokens with high overlap to keep compound requirements intact. Corporate policy documents chunk at 400-600 tokens with lower overlap because the semantic unit is the paragraph. Chunk configuration is set per document_type at ingestion.

Questions about cannabis and energy regulatory document RAG.

How do you keep a cannabis RAG system current when MED rules change every quarter?: The Colorado Marijuana Enforcement Division publishes rule updates on a roughly quarterly cadence, and local municipality ordinances (Denver, Boulder, Aurora) have their own amendment schedules. We address this with a scheduled ingestion pipeline that monitors MED's public rule repository and local government document sources for updates, ingests new versions automatically, marks prior versions as superseded, and sends an alert to your compliance team listing what changed. The alert includes a diff summary: which sections were modified, which were added, and which were removed. Your team reviews the diff rather than re-reading the full ruleset. Wana Brands and Medicine Man Technologies both manage multi-location compliance where a rule change at the state level may interact differently with local regulations depending on the dispensary's municipality.
Can RAG handle documents that define terms in one section and use them throughout, like regulatory defined terms?: Regulatory documents typically have a definitions section near the front. A term like 'licensed premises' or 'retail marijuana establishment' is defined once but referenced 40-50 times throughout the document. A naive chunking strategy splits the definition into one chunk and the usage into another, and a query about 'what activities are permitted on licensed premises' may retrieve the usage without the definition, leading to an incomplete answer. We handle this with a definition-linking step: at ingestion, we detect the definitions section, extract defined terms, and store them as metadata linked to every chunk where they appear. At retrieval time, when a chunk mentions a defined term, the definition chunk is automatically co-retrieved. This applies to both cannabis regulatory documents and energy sector FERC filings, which have extensive defined terms sections.
How do you manage multi-jurisdictional documents where the answer to a compliance question differs by location?: Each document chunk is tagged with jurisdiction metadata fields: state, county, municipality, and effective_date. A query from a Native Roots dispensary manager in Denver returns results filtered to Colorado state rules AND Denver city rules, not Aurora or Boulder ordinances. A query from their Pueblo location returns state rules AND Pueblo ordinances. This is enforced as a metadata pre-filter before the semantic search runs. For energy companies like Civitas Resources or DCP Midstream, the same pattern applies to FERC pipeline regulations, EPA regional rules, and state-level environmental permits, each document chunk carries the regulatory jurisdiction and applicable facility identifiers as metadata, so a query about a specific pipeline segment retrieves only the regulations that govern that segment's operating area.
What is the right chunking strategy for dense regulatory text versus more conversational corporate policy documents?: These require different approaches. Dense regulatory text (MED rules, FERC filings) has short, specific sentences where each sentence may carry independently important compliance content. We chunk these at 200-400 tokens with high overlap (50-100 tokens) to ensure that compound requirements that span sentence boundaries are not split. The overlap ensures that a two-sentence requirement where the second sentence qualifies the first stays together in at least one chunk. Corporate policy documents (employee handbooks, operations procedures) are written with more context per paragraph. These chunk at 400-600 tokens with lower overlap because the semantic unit is the paragraph, not the sentence. We configure chunk size and overlap per document type at ingestion time based on a sample review of your actual documents, not a one-size-fits-all default.

Scope your Colorado regulatory document project.

Tell us which regulatory bodies you track, how many locations you operate in, and what queries your compliance team runs most often. We reply within one business day.

Hire a RAG developer in Denver.

Why Denver's regulatory documents are a hard retrieval problem.

Cannabis and energy: two distinct use cases.

Cannabis: multi-location compliance queries

Cannabis: transportation and track-and-trace

Energy: pipeline regulation by segment

Energy: FERC filing cross-reference

Technical architecture for Denver regulatory RAG.

Jurisdiction-tagged chunk metadata

Scheduled rule update ingestion

Defined term co-retrieval

Document-type-specific chunking

Questions about cannabis and energy regulatory document RAG.

How do you keep a cannabis RAG system current when MED rules change every quarter?

The Colorado Marijuana Enforcement Division publishes rule updates on a roughly quarterly cadence, and local municipality ordinances (Denver, Boulder, Aurora) have their own amendment schedules. We address this with a scheduled ingestion pipeline that monitors MED's public rule repository and local government document sources for updates, ingests new versions automatically, marks prior versions as superseded, and sends an alert to your compliance team listing what changed. The alert includes a diff summary: which sections were modified, which were added, and which were removed. Your team reviews the diff rather than re-reading the full ruleset. Wana Brands and Medicine Man Technologies both manage multi-location compliance where a rule change at the state level may interact differently with local regulations depending on the dispensary's municipality.

Can RAG handle documents that define terms in one section and use them throughout, like regulatory defined terms?

Regulatory documents typically have a definitions section near the front. A term like 'licensed premises' or 'retail marijuana establishment' is defined once but referenced 40-50 times throughout the document. A naive chunking strategy splits the definition into one chunk and the usage into another, and a query about 'what activities are permitted on licensed premises' may retrieve the usage without the definition, leading to an incomplete answer. We handle this with a definition-linking step: at ingestion, we detect the definitions section, extract defined terms, and store them as metadata linked to every chunk where they appear. At retrieval time, when a chunk mentions a defined term, the definition chunk is automatically co-retrieved. This applies to both cannabis regulatory documents and energy sector FERC filings, which have extensive defined terms sections.

How do you manage multi-jurisdictional documents where the answer to a compliance question differs by location?

Each document chunk is tagged with jurisdiction metadata fields: state, county, municipality, and effective_date. A query from a Native Roots dispensary manager in Denver returns results filtered to Colorado state rules AND Denver city rules, not Aurora or Boulder ordinances. A query from their Pueblo location returns state rules AND Pueblo ordinances. This is enforced as a metadata pre-filter before the semantic search runs. For energy companies like Civitas Resources or DCP Midstream, the same pattern applies to FERC pipeline regulations, EPA regional rules, and state-level environmental permits, each document chunk carries the regulatory jurisdiction and applicable facility identifiers as metadata, so a query about a specific pipeline segment retrieves only the regulations that govern that segment's operating area.

What is the right chunking strategy for dense regulatory text versus more conversational corporate policy documents?

These require different approaches. Dense regulatory text (MED rules, FERC filings) has short, specific sentences where each sentence may carry independently important compliance content. We chunk these at 200-400 tokens with high overlap (50-100 tokens) to ensure that compound requirements that span sentence boundaries are not split. The overlap ensures that a two-sentence requirement where the second sentence qualifies the first stays together in at least one chunk. Corporate policy documents (employee handbooks, operations procedures) are written with more context per paragraph. These chunk at 400-600 tokens with lower overlap because the semantic unit is the paragraph, not the sentence. We configure chunk size and overlap per document type at ingestion time based on a sample review of your actual documents, not a one-size-fits-all default.

Scope your Colorado regulatory document project.

Tell us which regulatory bodies you track, how many locations you operate in, and what queries your compliance team runs most often. We reply within one business day.