Teams cannot find the right evidence fast enough.
The answer may exist somewhere in the claim file, policy binder, medical chart, or case record — but someone still has to dig for it.
Haystac builds domain-specific intelligence from your own content — documents, records, rules, policies, and workflows — so teams can extract facts, ask questions, validate decisions, and act with evidence instead of guesswork.
Coverage applies under §4.2. Loss is within policy limits. Recommend approval.
Claims, loan packets, medical records, contracts, policies, applications, case files, and correspondence drive real business decisions every day. But most of that knowledge is still buried in PDFs, scans, forms, notes, attachments, and legacy repositories. People have to read, compare, copy, validate, route, and explain the same information by hand.
Generic AI does not fix that. It can summarize a document, but it does not automatically know which document matters, which rule applies, which field is missing, which exception should be escalated, or which source supports the answer. That is the gap Domain-Specific Intelligence closes.
The answer may exist somewhere in the claim file, policy binder, medical chart, or case record — but someone still has to dig for it.
A mortgage packet, EOB, prior authorization, fraud note, or government application carries domain-specific meaning. Layout, terminology, relationships, and rules all matter.
In regulated work, the answer is not enough. Teams need to show where it came from, what rule it followed, and why the recommendation was made.
Haystac turns regulated enterprise content into a domain-specific intelligence system. It does not ask teams to trust a generic model. It gives the model the right content, the right context, the right rules, and the right evidence before it answers. So instead of asking AI to guess, teams can use AI to work from the same documents, policies, records, and workflows they already trust.
Know whether something is a claim, contract, medical record, policy, application, invoice, letter, or case file.
Pull fields, entities, clauses, dates, tables, codes, amounts, signatures, and relationships from complex documents.
Ask questions and get answers grounded in verified enterprise content.
Route, escalate, approve, reject, validate, summarize, or trigger the next workflow step with a full audit trail.
Domain-Specific Intelligence is not one model. It is the way OmniSuite™ turns raw content into usable, explainable, and actionable intelligence.
Chicago identifies what each document is, separates mixed document streams, and routes content based on meaning.
Nashville reads complex documents and converts them into structured data your systems can use.
Orion retrieves the right enterprise content before generating an answer, so responses are grounded in source material.
Polaris turns insight into governed action by coordinating models, tools, systems, and human review.
Search across policies, records, contracts, claims, case files, and correspondence using plain language.
Turn PDFs, scans, forms, tables, notes, and attachments into clean data fields.
Compare a claim, application, treatment, loan, contract, or case file against internal policies and requirements.
Identify absent fields, incomplete packets, inconsistent records, unsupported claims, and documents that need review.
Generate answers with source references so users can see which document, clause, table, note, or rule supports the output.
Send clean cases forward, escalate exceptions, notify reviewers, or trigger downstream workflows.
Generic AI is useful when the cost of being wrong is low. Regulated work is different. A claims decision, coverage review, loan exception, compliance finding, medical authorization, or government eligibility decision has to be grounded in the organization’s own evidence. It has to follow the right rule. It has to be explainable after the fact. That requires more than a model that can write. It requires a system that knows what content it is looking at, extracts the right facts, reasons from verified sources, and records what happened.
Classify claim packets, extract facts, compare against policy language, identify fraud signals, and route exceptions to review.
Read loan files, KYC packets, income documents, underwriting rules, and internal procedures with source-backed traceability.
Evaluate prior authorizations, treatment records, payer rules, clinical notes, lab results, and plan requirements.
Process applications, permits, benefits documents, FOIA records, eligibility files, and compliance evidence.
Extract clauses, dates, obligations, exceptions, entities, and renewal terms from long-form legal and operational documents.
Ask which internal policies, procedures, controls, or records are affected by a new regulation, audit finding, or rule change.
Teams no longer start by figuring out what they are looking at. Haystac classifies and separates content before the work begins.
Documents become structured data that can feed case systems, workflow tools, analytics, and enterprise applications.
Answers come with evidence, not just language. Users can trace outputs back to the documents and rules behind them.
The system can identify what is complete, what is missing, what is inconsistent, and what needs human review.
The same rules, sources, and validation steps can be applied across every claim, case, application, or file.
Once documents are understood and decisions are grounded, AI can safely move from answering questions to executing governed steps.
Each part of OmniSuite™ handles a different part of the decision process. Together, they turn documents into intelligence teams can use.
Classifies, separates, and routes content by meaning.
Extracts fields, entities, tables, clauses, and relationships.
Answers questions using retrieved enterprise content and source citations.
Coordinates actions, reviews, escalations, and workflow steps.
Classify the content. Extract the facts. Reason from evidence. Act with control.
The next wave of enterprise AI will not be won by the largest general model alone. It will be won by systems that understand specialized content, retrieve the right evidence, reason inside the right boundaries, and support decisions teams can defend.
That is especially true in regulated industries, where hallucinations are not a quality issue. They are a business risk. Haystac’s approach aligns with the shift toward domain-specific language models and expert AI systems: smaller, more focused, more grounded, and built around enterprise context rather than generic model knowledge.
“General AI can help people write. Domain-specific AI helps enterprises decide.”
Haystac creates domain-specific intelligence by controlling the full path from document intake to decision output. It structures the content before reasoning. It trains task-specific models where needed. It retrieves verified evidence before answering. It records the source, confidence, and action path.
Documents are grouped and routed by meaning, not just layout, templates, or keywords.
Task-specific models learn from customer content and extract the facts that matter for each workflow.
Answers are generated from retrieved enterprise evidence, not unsupported model memory.
Related facts across documents, records, and systems can be connected for more complete reasoning.
Low-confidence outputs, exceptions, and policy-sensitive cases can be escalated to human reviewers.
Sources, confidence, decisions, and actions are recorded so the reasoning path can be reviewed.
“Haystac does not just make documents searchable. It makes them usable for decisions.”
It turns a company’s own documents, rules, records, and workflows into AI that can extract facts, answer questions, validate decisions, and trigger governed actions.
No. Fine-tuning may be part of the system, but Domain-Specific Intelligence is broader. It includes classification, extraction, retrieval, reasoning, workflow orchestration, and auditability.
Claims, loan files, policies, contracts, medical records, applications, case files, correspondence, invoices, forms, tables, scanned documents, and other enterprise content.
It retrieves verified enterprise content before answering. The answer is grounded in source evidence instead of unsupported model memory.
Yes. Outputs can include source references, confidence signals, and the evidence used to support the answer.
It uses the customer’s own content, terminology, document types, rules, and workflows. The system learns how the organization actually operates.
Haystac turns regulated content into domain-specific intelligence your teams can use to extract facts, ask questions, validate decisions, and move work forward.
We enable highly regulated organizations to build, govern, and operate domain-specific models within their own infrastructure and governance frameworks.