All use cases
Use case · LLM pipelines & agents

Workflow automation

Automate document intake, research, extraction and reporting with language models that generalise to documents no template anticipated. They call your tools and take action inside checked pipelines: every output is validated, every action stays within limits, and every step is logged.

4 / 4of our products run LLM components
14-dayrefresh cycle for vnpredict’s AI analysis
~1 minper vnadvise legal brief, each step cited

When you need this

The demo was easy; production is where it breaks. Free-text outputs your systems can’t read. Agents that behave differently every run, with no audit trail. Extraction that quietly corrupts one record in fifty. You want language models working on real workflows (document intake, analysis, entity resolution, reporting) without betting the workflow on a model’s mood.

What we build

  • Clean data, every time: every language-model response is validated against a schema, and anything malformed is rejected and retried, so your systems only receive clean, typed data.
  • A process you can replay: the model reasons inside each step, and the pipeline’s control flow stays explicit, replayable and auditable.
  • Actions within limits: agents act only through approved tools with bounded permissions, with human sign-off where the stakes demand it.
  • Answers tied to sources: analysis and narratives grounded in retrieved documents and web results.
  • Every failure becomes a test: a golden set with model-graded scoring checks prompt and model changes, and each failure found in production is added to it.

How it’s built

Trigger → retrieve context → LLM step (schema-checked) → validate · gate → tool call / write → log & evaluate, end to end

Methods: LLM pipelines & agents · hybrid retrieval

In our products

LLM components run in all four of our products: vnvalue applies a frontier reasoning model to its methodology, stress-testing every revision, and has a language model draft research notes from its own model outputs and financial statements; vnadvise splits each question into sub-queries, reranks what they find, and cites each claim to the passage behind it; vnpredict writes the AI analysis beside its forecasts with live web search at forecast time; and vnappraise, a research preview, filters its training listings with an LLM price-quality check.