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Data

Document intelligence pipeline

Unstructured files in, validated records out, humans only on the uncertain ones.

Use it when

Invoices, KYC, claims, contracts, forms and emails that must become structured data in a system of record with an audit trail.

Structure

The parts, top to bottom

hover a part to see its job

across every level

Hover or tap any part to see what it does. The light shows the order a request moves through.

Flow

What happens, in order

  1. 1Ingest from email, scan, upload
  2. 2Classify document type
  3. 3Extract with a typed schema
  4. 4Validate against rules & other docs
  5. 5Confidence routing: auto or review
  6. 6Post to system & keep evidence
Tools

What we typically build it with

Gemini / GPT / Claude visionDocument AI / TextractPydantic schemasQueue & review UIERP / LOS APIs

Trade-offs

Accuracy is driven by parsing quality and validation design more than by the model; layout-aware parsers score from about 50% to 85% on document benchmarks, so we benchmark them on your documents. Confidence routing typically sends around 20% of items to humans and decides how much time you actually save.

What we solve

Problems this architecture solves

Generic problem statements with the flow and the outcomes the industry has documented.

Document intelligence pipeline

Documents keyed in by hand

Invoices, KYC files, claims and contracts arrive as PDFs and photos. People retype them, errors slip through, and the backlog grows every month-end.

How the system works

  1. Ingest PDF / image / email
  2. Classify document type
  3. Extract to a schema
  4. Validate & score confidence
  5. Auto-post or route to review
Multimodal LLMStructured outputsReducto / LlamaParseReview queueERP / LOS integration

Outcome: Documented deployments classify documents in under a second, turn day-long claims backlogs into minutes, and reach 85% no-touch invoice processing within six months with a seven-month payback. DXC with Claude; Vic.ai

Architecture
Related

Other data patterns

Data

Lakehouse, semantic layer and analytics copilot

Raw data becomes governed metrics, then answers in plain language.

Use it when: When dashboards disagree, analysts are a bottleneck, or leadership wants to ask questions of the data directly without breaking the definitions.

Flow

  1. 1Ingest batch & streams
  2. 2Bronze → silver → gold tables, partitioned by time / tenant
  3. 3Quality tests & lineage
  4. 4Semantic layer defines metrics
  5. 5Text-to-SQL agent with guardrails

Let's build intelligent systems that drive growth

Tachyon is the engineering partner for teams that need AI in production, not in a deck. Start with a free 60-minute discovery call.

Contact

Tell us the problem, we will map it to the architecture

Every great partnership begins with a conversation. Whether you are exploring possibilities or ready to scale, tell us what you are actually trying to build.

Prefer to talk?

Pick a 60-minute slot. No pitch, just an engineer with honest answers.

Book a call

NDA available on request. We reply within one business day.