Why legacy automation fails in private markets

AI-Dynamic Pricing Strategies

01

Traditional OCR: slow to deploy, fragile to maintain

For years, document automation in private markets has relied on OCR systems built for predictable, structured documents.

That approach breaks down in reality.

Legacy OCR requires:

  • Rigid templates
  • Extensive data-mapping exercises
  • Constant rule updates whenever documents change

Private markets documents are the opposite of predictable. Formats vary by GP, fund, vintage, and even quarter. As a result:

  • Accuracy degrades quickly
  • Maintenance costs compound over time
  • Systems break whenever formats shift

Even so-called “AI-enhanced OCR” still relies on rules and templates that require constant tuning — making these systems slow to deploy and fragile to maintain at scale.

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Wearable app development

02

Pre-LLM machine-learning pipelines don’t adapt

More recent approaches replaced OCR rules with traditional machine-learning models. While an improvement, these systems still:

  • Require training on historical data
  • Struggle with edge cases and novel formats
  • Depend on batch retraining cycles

They work best when documents are stable. Private markets are not.

This is why many LPs remain locked into periodic or quarterly processing — not by choice, but by tooling limitations.

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The Tamarix approach

Tamarix applies LLM-powered intelligence as part of a controlled operating system — purpose-built for private markets.

01 LLMs understand meaning, not coordinates

LLMs understand meaning, not coordinates

Modern LLMs fundamentally change how documents are processed. Instead of matching coordinates or predefined patterns, they understand language, structure, and intent — the same way a private markets professional reads a report.

This allows Tamarix to:

  • Adapt automatically to new layouts and formats
  • Handle free-form text, tables, and narrative disclosures
  • Extract meaning even when documents evolve quarter to quarter

No templates. No remapping projects. No brittle rules.

02 Continuous, adaptive extraction — with domain intelligence

Continuous, adaptive extraction — with domain intelligence

As GP documents arrive, Tamarix:

  • Identifies and categorizes the document - (capital account statements, notices, reports, and more)

  • Determines which KPIs and data points matter - Using private-markets-specific logic and context

  • Extracts, structures, and validates the data - Applying domain rules and automated consistency checks

Validated data feeds back into the system, creating continuous feedback loops that improve extraction quality over time.

This is not batch processing. It is always-on, adaptive intelligence.

03 AI with guardrails: validation, auditability, and control

AI with guardrails: validation, auditability, and control

AI does the heavy lifting — but Tamarix never treats outputs as unverifiable.

Every data point is:

  • Source-linked to the original document

  • Traceable through a full audit trail

  • Checked against private-markets-specific consistency rules

Validation workflows are flexible:

  • Fully automated
  • Reviewed by LP teams
  • Or managed end-to-end by Tamarix

Speed is maximized — without sacrificing institutional trust.

04 Model-agnostic by design

Model-agnostic by design

Tamarix is LLM-agnostic by architecture.

Rather than locking into a single provider, the system is designed to:

  • Select the best model for each task
  • Evolve as models improve
  • Avoid dependency on any single vendor or approach

This ensures Tamarix remains future-proof as AI technology advances.

05 Enterprise-grade data isolation

Enterprise-grade data isolation

Tamarix works with AI providers that support:

  • Strict data isolation
  • Zero-data-retention (ZDR) policies
  • No cross-customer model training

Customer data is never used to train shared models and remains fully isolated — aligning with institutional security and compliance expectations.

(Additional details are available on the Data Security page.)

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means for LPs

What this means for LPs

In practice, Tamarix acts like a private markets professional working 24/7.

Tamarix:

  • Understands the language and logic of private markets
  • Knows what data matters — and why
  • Structures information the way an LP team would
  • Never gets tired, misses updates, or waits for quarter-end

The result:

  • Dramatically less manual work
  • Lower operating costs
  • Fewer errors and reconciliations
  • Continuously current portfolio data

LP teams spend less time processing information — and more time using it.

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with a request body that specifies how to map the columns of your import file to the associated CRM properties in HubSpot.... In the request JSON, define the import file details, including mapping the spreadsheet's columns to HubSpot data. Your request JSON should include the following fields:... entry for each column.

AI that works the way private markets do

Tamarix combines modern LLM intelligence with domain-specific rules, validation, and workflow integration — delivering automation LPs can trust operationally and strategically.

Schedule a call

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markets

FAQ — Our AI Approach

Answers to the most common questions about Tamarix.

No. Tamarix does not require templates, mapping projects, or ongoing rule maintenance. The system adapts automatically as documents change.

Legacy OCR and traditional ML rely on fixed patterns and retraining cycles. Tamarix uses LLMs that understand meaning and context, enabling continuous, adaptive extraction across changing GP formats.

No. Tamarix is model-agnostic and designed to use the most appropriate models for each task, evolving as AI technology advances.

Tamarix works with providers that support strict data isolation, zero-data-retention policies, and no cross-customer training. All data remains isolated and auditable.

Documents are processed continuously, with structured data typically available in minutes rather than days or quarterly cycles.

Heading 1

with a request body that specifies how to map the columns of your import file to the associated CRM properties in HubSpot.... In the request JSON, define the import file details, including mapping the spreadsheet's columns to HubSpot data. Your request JSON should include the following fields:... entry for each column.