Models
GPT-4.1 family, Claude 3.5 Sonnet and successors, Gemini multimodal, Llama 3.x / Qwen when self-hosting matters.
Services · 01
Unstructured data processing for production systems
Unstructured data processing is how you turn PDFs, email threads, attachments, audio, images, and video into structured records you can query, govern, and reuse in analytics, automation, and RAG. Databotiq builds ingestion through entity resolution for teams that need evidence, not another data lake science project.
At a glance
- Practice
- Unstructured Data Pipelines
- Best fit when
- critical facts live in email, attachments, and PDFs your core systems never see.
- Typical Rapid POC
- 14 days, fixed scope.
Problems we solve
The pains buyers describe to us first.
Critical facts live in email and attachments, not in your core systems.
OCR alone gives text without reliable fields, relationships, or confidence.
PII and sensitive payloads need redaction paths before downstream use.
The same entity shows up with different spellings, IDs, and addresses across sources.
Approach
Our approach.
We start with the decisions your operators already make manually. Those decisions define the schema, the quality bar, and the acceptable error modes. Then we build a pipeline: ingest, normalize, extract, validate, link entities, and route exceptions to human review when confidence drops.
Technical depth
Technical depth you can inspect
For documents we combine layout-aware vision-language models with deterministic checks (sums, dates, IDs, and cross-field rules). For audio we pair transcription with segmenting and summarisation where needed. For images we extract structured attributes and tie them back to work orders, claims, or asset records depending on your domain.
Tech (May 2026)
Named tools, not vague acronyms.
Specificity earns trust. The choices below reflect what we ship today, and they will evolve as new models and tools clear our internal evaluations.
Orchestration
Typed pipelines, queue workers, and idempotent stages, not one giant prompt.
Storage
Object stores, warehouses (Snowflake, BigQuery, Databricks), and vector indexes when retrieval is part of the product.
Where this fits
Industries and roles we ship for.
Insurance
FNOL artifacts, adjuster correspondence, medical bill attachments.
Healthcare operations
Faxed and scanned paperwork adjacent to EHR workflows.
Manufacturing and industrials
Photos, PDFs, and supplier email tied to assets.
Case pattern
From adjuster email to structured claim intake at scale
This pattern is for carriers where adjusters and third parties send facts as email threads and attachments, not as clean ACORD feeds. The goal is reliable structured records for routing, reserving, and downstream fraud checks, without asking adjusters to retype what they already wrote.
Read the case patternOutcome
What this means for you.
You stop treating “unstructured” as a permanent excuse. Your teams query the same entities your agents act on, and your audits can trace an extracted field back to a source page, timestamp, and model version.
Further reading
Related practice notes and patterns.
FAQ
Questions buyers ask about unstructured data pipelines.
Specifics on accuracy, deployment, integration, and the proof path. If something isn't covered here,ask us directly.
Do you only work with text documents?
No. We routinely combine PDFs, email, images, audio, and tabular extracts in one pipeline, as long as the business outcome is clear and we can measure quality on your samples.
How do you measure accuracy?
We agree field-level precision targets on a labelled evaluation set from your environment, then track drift weekly after launch. For some fields, recall matters more than precision, and we tune thresholds accordingly.
How fast can we see value?
A Rapid POC is the fastest honest path: you get working extraction and linking on a bounded slice of real traffic, plus a written assessment of what production would require.
What about PII?
We classify sensitive segments, apply redaction or tokenisation where appropriate, and restrict access with your identity stack. Formal compliance claims depend on your deployment model, and we document what is true for your program.
Will this replace our data team?
No. It removes repetitive parsing work so analysts and engineers focus on higher judgment problems: policy, modeling, and exception design.
How do you integrate?
We ship connectors and webhooks to CRMs, ticketing, data warehouses, and internal APIs. Integration is not an afterthought. It is how the pipeline proves value.
See it on your data in 10 days.
We run a sandboxed Rapid POC so you can evaluate outputs, integrations, and risk before you fund production.