AI Data Products
When the records are clean, the intelligence layer earns its keep.
Extraction and enrichment give you structured data. This is the layer on top — AI that turns those records into search, resolution, extraction, summaries, and alerts your team can actually act on.
Why this exists
Structured data is the floor, not the ceiling.
Most data work stops at clean records in a database. That's necessary, but it's not where the value is. The value is in the questions your team asks of that data — and whether the system can answer them in seconds instead of a SQL ticket and a two-day wait.
AI data products are the layer we build on top of your structured data: natural-language query, intelligent matching, document understanding, and continuous monitoring. Each is grounded in your data, with source citation and confidence — not a generic model guessing.
What we build
Five intelligence layers over your data.
Semantic Search
Natural languageVector-embed your database and expose conversational query. "Find all contractors in Florida with a current ratio above 1.5 and an EMR below 1.0" becomes a question, not a join — built on pgvector, Pinecone, or Weaviate.
Entity Resolution & Dedup
MatchingAI-assisted matching for records that refer to the same entity without sharing a key. "John Smith, CEO, Acme Inc." vs "J. Smith, Acme International" — trained fuzzy matching plus LLM resolution. Critical for CRM dedup, data rooms, and research.
Document → Database
Doc intelligenceExtract structured fields from unstructured documents at scale — financial statements, applications, contracts, invoices, records. OCR plus AI extraction (AWS Textract, Azure Form Recognizer, GPT-4 field mapping) produces database records, not PDFs.
AI Summarization & Digests
Decision supportTurn large datasets — news feeds, regulatory filings, market data — into natural-language summaries for human decision-makers, delivered on a schedule via email, Slack, or API (GPT-4 / Claude, LangChain).
Anomaly Detection & Alerting
MonitoringStatistical models on time-series feeds surface anomalies — price spikes, volume drops, fraud signals, operational outliers — via scikit-learn, Isolation Forest, LSTM, and custom CloudWatch metrics.
Proven in production
Patterns we run today.
These intelligence layers ship in real InWork systems — across financial, surety, automotive, and industrial workloads.
Conversational financial intelligence
A natural-language query layer over portfolio, research, and CRM data for a US investment firm — every answer cites its source document and extraction date, with role-based access to data subsets.
Underwriting document extraction
Financial-statement and application intelligence for surety underwriting — OCR plus AI extraction produces normalized records and ratio analysis, with a full audit trail (SOC2-aligned data handling).
Market digests at scale
Scheduled, AI-generated briefings over pricing, review, hiring, and news signals — the summarization layer inside InWork MarketPulse.
How we keep it honest