LLM integration in Denver.

The technical reality of LLM features in Denver businesses.

Questions from Denver buyers.

Can a model extract reliable data from decades of land, lease, and title documents?

Yes, with the right pipeline in front of it. Energy land records mix typed agreements, scanned carbon copies, and handwritten amendments, so the build starts with OCR quality scoring: documents that pass go straight to extraction, documents that fail route to enhancement or human keying instead of silently producing garbage. Extraction itself targets a typed schema (lessor, lessee, legal description, royalty terms, dates, depth clauses) with field-level confidence and a pointer to the source passage. On a labeled sample of your own documents we typically see strong accuracy on typed material and honest flagging on the marginal scans, which is the property that makes the system trustworthy at scale.

Marketing copy in a regulated category keeps getting bounced by compliance. Can a model help without making it worse?

Only if the rules are in the loop, not in the model's imagination. The pattern that works: your compliance-approved language, claim rules, and jurisdiction restrictions live in a structured rule set; the model drafts against those rules and cites which rule each sentence satisfies; and anything outside the approved space routes to your compliance reviewer with the flag pre-written. For multi-state operators (cannabis is the obvious Denver case, but the same applies to financial products and supplements), the rule set is keyed by state, so Colorado copy and Oklahoma copy diverge automatically. The model never decides what is legal; it drafts inside the lines your counsel already drew.

We are a SaaS company adding our first AI feature. What separates a feature users keep from one they try once?

Three properties, in our experience scoping these builds. Grounding: features that answer from the customer's own data (their docs, their tickets, their records) with visible citations get trusted; features that answer from the model's general knowledge get screenshot once and abandoned. Latency honesty: streaming output with a clear in-progress state keeps users engaged through a ten-second generation; a frozen spinner does not. And an escape hatch: every AI output needs an obvious edit-or-dismiss affordance so users feel assisted rather than overridden. We design those three in from the start, then let your usage analytics argue about the rest.

What does it cost to run an LLM feature across a customer base of a few thousand accounts?

Less than most Denver SaaS teams budget, if the routing is sane. A drafting or summarization feature averaging twenty uses per account per month, served by a small model with prompt caching, typically lands in the hundreds of dollars a month range across a few thousand accounts; the frontier model handling only flagged-complex requests adds modestly to that. The numbers go wrong in two ways: sending every request to the most expensive model out of caution, or letting prompts accumulate context until each call carries ten times the tokens it needs. We instrument cost per feature and per account from launch, so pricing and packaging decisions run on data instead of fear.