Three families of services across the Blockdata stack — from raw document ingest, through the knowledge infrastructure that holds your blocks, to the specialist agents that act on them.
The atomic data layer of Blockdata. Layout-aware parsing, OCR recovery, schema-bound extraction, classification, and the cleanup operations that make downstream agents trustworthy. These services produce blocks — auditable, cited, version-tracked.
Multi-column PDFs, scanned forms, presentations, spreadsheets — parsed into blocks with reading order, headings, tables, and figures preserved.
Recover usable text from low-quality scans, faxes, mobile photos, and handwritten annotations. De-skew, de-noise, and re-flow.
You define columns. We fill them — with source span citations and a confidence score on every value. Low-confidence rows route to review.
Route incoming docs to the right downstream pipeline — MSAs, NDAs, claims, filings, intake forms.
Detect and remove PII, PHI, and customer-defined sensitive entities before downstream processing or sharing.
Structured tables and form fields — including merged cells, multi-page tables, and checkbox detection.
Locate, classify, and verify signatures, seals, and stamps across executed contracts and notarized documents.
Parse and extract across 40+ languages, with optional translation layers and per-language confidence calibration.
Compare contract drafts and policy revisions clause-by-clause. Surface meaningful changes, drop boilerplate noise.
Generate searchable captions for charts, diagrams, and embedded images. Link figures to surrounding text spans.
Bring earnings calls, depositions, and customer recordings into the block stack alongside documents.
Once blocks exist, they need somewhere to live and a shape to answer questions in. These services build and operate the storage, retrieval, and provenance layers — vector stores, knowledge graphs, Postgres schemas, Mongo collections — so your team queries one stack, not five.
Production vector DBs on pgvector, Pinecone, Weaviate, or Qdrant — with embedding strategy, dimensionality, and retrieval evaluation set up against your blocks.
Entity-linked graphs from your blocks. Resolve people, organizations, products, and clauses across the corpus. Queryable via Cypher or graph SDK.
Turn your blocks into a Postgres schema your analysts can join, query, and dashboard against — without learning a new query language.
Document-shaped collections when SQL is the wrong fit. Indexed for the access patterns your team uses.
Vector + keyword + graph traversal in a single retrieval call. Tuned to your domain and evaluated on real queries.
Model choice, chunking, re-ranking — sized to your corpus and budget. Re-runnable when models change.
De-duplicate parties, products, and clauses across your corpus. Reconcile against external authority files.
Every block traces back to its source span, its run, and the schema version that produced it. Exportable for compliance.
For SaaS teams: tenant-scoped storage, retrieval, and audit, with row-level security defaults you can extend.
Keep blocks fresh as documents and source databases change. Incremental, idempotent, observable.
Point-in-time snapshots of the stack. Diff blocks across versions. Reproduce a query the way it ran last quarter.
The application layer of Blockdata. Pre-built and custom agents that know a specific slice of the stack — contracts, claims, filings, patient records — and do the work that used to take a team. Built and orchestrated on Kai, our companion agent platform.
Reads incoming MSAs, NDAs, and vendor agreements. Flags non-standard clauses, computes exposure, drafts redlines for human approval.
Reviews incoming insurance claims at intake. Decides auto-approve, auto-decline, or route-to-adjuster — with cited reasoning.
Continuously audits the stack for policy violations, exposed PII, expired licenses, missing signatures. Files tickets, not reports.
Walks a corpus to surface relevant documents for a legal matter or investigation, with privilege flagging.
Analyst-grade research over 10-Ks, transcripts, and footnotes. Outputs are memos with citations, not hallucinated paragraphs.
HIPAA-aligned. Summarizes intake, surfaces missing labs, maps history to FHIR for the EHR.
Reads internal policies and surfaces conflicts, gaps, and drift between stated rules and operational reality.
Bespoke agent on Kai, scoped to your stack. Discovery, evaluation, hardening, then handoff to your team.
Multi-agent workflows, retries, escalations, and human-in-the-loop checkpoints. Same auth and audit as the stack.
The review surfaces, queues, and SLAs that keep your reviewers in the loop without slowing the agents down.
We map your document corpus, your downstream consumers, and the answers your business needs.
We pick services from the menu, write a measurable success spec, and lock the schema you'll operate on.
Pipelines wired, blocks populated, retrieval evaluated against your real queries — not a benchmark.
You operate the workbench. We're on call. Quarterly evaluation against the spec, plus a model-refresh cadence.
Tell us your documents, your stack, your week-one win. We'll come back with a scoped engagement and a schema.