Best evaluation framework for document extraction in pension funds (2026)

Pension funds teams need an evaluation framework that can score document extraction on more than just “did the OCR work.” You need to measure field-level accuracy, latency under batch and interactive loads, auditability for compliance, and the real cost of running extraction across years of member statements, contribution forms, beneficiary updates, and legacy scanned PDFs. If the framework cannot tell you where errors happen, how expensive they are, and whether a model change breaks regulatory traceability, it is not fit for production.

What Matters Most

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  Field-level accuracy, not just document-level accuracy

  • •Pension workflows care about specific fields: member ID, contribution amount, employer name, retirement date, beneficiary details.
  • •A framework should support exact-match scoring, fuzzy matching for names/addresses, and per-field weighted metrics.
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  Latency and throughput under real workloads

  • •You will see both low-volume high-value requests and large backfills of archived documents.
  • •The evaluation stack should measure p50/p95 latency per page and per document, plus batch throughput.
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  Compliance-grade traceability

  • •Pension funds operate under strict governance expectations: audit trails, retention policies, access control, and explainability for extracted data.
  • •The framework should log model version, prompt/versioned rules, confidence scores, human overrides, and source spans on the page.
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  Cost per successful extraction

  • •Token spend is only part of the bill. Add OCR cost, post-processing cost, retries, human review time, and storage.
  • •Good evaluation frameworks let you compare total cost per document class: clean digital PDFs vs scanned forms vs handwritten exceptions.
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  Robustness across document types

  • •Pension ops teams deal with statements, forms, letters from employers, identity documents, and legacy scans with poor quality.
  • •Your evaluation must segment by template type and scan quality so one strong doc class does not hide failure in another.

Top Options

Recommendation

For a pension funds company evaluating document extraction in 2026, I would pick DeepEval as the core evaluation framework, paired with a thin layer of custom field-level scoring and audit logging.

Why DeepEval wins here:

• •It fits into CI/CD cleanly.
• •It is flexible enough to evaluate prompt-driven or agentic extraction flows.
• •You can define pension-specific metrics instead of being trapped by generic LLM scores.
• •It works well when your team needs repeatable regression tests every time OCR settings, prompts, or model versions change.

That said, the real win is not the framework alone. The winning setup is:

• •DeepEval for regression tests
• •Custom gold-label dataset with pension-specific fields
• •Structured logs capturing model version, source doc hash, confidence scores, reviewer overrides
• •Storage/search layer like pgvector if you need similarity search over historical cases or near-duplicate detection during QA

If your team wants an opinionated stack: use pgvector over Pinecone/Weaviate/ChromaDB unless you have a clear scale problem. In pension environments, keeping evaluation artifacts close to Postgres simplifies access control, retention policy enforcement, and audit queries. Pinecone is fine when you need managed scale fast; Weaviate is strong if semantic search becomes a bigger product feature; ChromaDB is better suited to prototypes than regulated production.

When to Reconsider

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  You need enterprise observability more than test harnesses

  • •If your biggest pain is tracing failures across prompts/models/reviewers rather than running benchmark suites in CI, LangSmith becomes more attractive.
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  You are mostly monitoring drift after deployment

  • •If extraction is already stable and the main job is detecting quality decay across new statement formats or OCR noise, Evidently AI may be a better fit as the monitoring layer.
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  Your team has very heavy RAG-style retrieval around documents

  • •If extraction sits inside a broader retrieval pipeline with grounding checks and answer validation, RAGAS or TruLens may give better coverage than a pure test framework.

The practical answer: if you are choosing one framework to standardize pension document extraction evaluation now, start with DeepEval. It gives you enough control to encode compliance-sensitive scoring without forcing your team into a research project before production.

Keep learning

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By Cyprian Aarons, AI Consultant at Topiax.