Workshop 5: Artifact Types

This workshop decides how Experio treats each kind of document. An artifact type (the admin guide calls it a content type) tells the classifier how to recognise a document family, and tells extraction which part of the ontology to fill from it. Two decisions carry most of the accuracy: how you tell look-alike documents apart (a SOW from an MSA from an amendment), and which entities a document may create versus only match. You make both decisions with real documents on screen, using Test Ingestion as you go.

At a glance


Purpose	Define each document family: classification instructions, extraction subset, processing type per entity, extraction policy, folder filters
When	Phase 2 Model, week 3–4, after Workshop 3 and Workshop 4
Duration	3 hours for 5–6 families; add 30 minutes per extra family
Attendees	Experio: FFE (drives the admin screens), Experio SMEs as needed. Client: PM, super user, the SME who owns each family (BD, Legal, HR, PMO), IT admin for folder structure
Inputs	Saved ontology and taxonomies; data inventory with folder structure; 3–5 real sample documents per family, including awkward ones
Outputs	One artifact type per family, tested; a spec sheet per family; folder filters mapped to allowed types; extraction policy per type
Admin pages	Model & Define > Artifact Types, Connect > Data Sources (folder filters), Process > Conflict Resolution (classification reviews)

Before the workshop

FFE and Experio SMEs

From the data inventory, list the document families and where each lives. Propose a first grouping.
Create a draft artifact type per family with a name and one-line classification instruction, so the workshop starts with something to test.
Upload the sample documents to a File Upload data source and run a job, so they are parsed and available to the AI Assistant.

Client homework (SMEs)

Collect 3–5 real documents per family: a typical one, an old-format one, a short one, and one that is easy to confuse with another family (for example an SOW that contains full contract terms).
Note how you tell the family apart from its look-alikes. “Our SOWs always reference an MSA and have a project code in the header” is exactly what the classifier needs.

Agenda

Time	Segment	Output
0:00	How classification and extraction work; the 0.8 threshold	Shared understanding
0:15	Group the corpus into families; decide what is out of scope	Family list, “None” list
0:35	Per family: classification instructions; Test Ingestion Classify Only	Instructions that separate look-alikes
1:30	Break
1:40	Per family: entities, attributes, relationships, processing types	Extraction subset
2:20	Test Ingestion Classify & Extract on samples	Corrections
2:40	Extraction policy per family; folder filters	Cost/accuracy decisions, filter map

Running the workshop

1. Group the corpus into families

A family is a set of documents that serve the same purpose and answer the same questions. Group by purpose, not by file format or folder. Ask: “Which golden questions does this kind of document answer?” A family that answers no question does not need an artifact type; it will be classified as None and skipped.

2. Write classification instructions

The classifier scores every allowed artifact type from 0 to 1 against its classification instructions. A document is assigned a type only if its best score reaches 0.8. This threshold is fixed. Below it, a classification review goes to Process > Conflict Resolution and the file waits for a person to choose. A result of None, or a type not allowed for that folder, means the file is skipped. Good instructions have three parts:

What it is: purpose and typical titles.
How to recognise it: headings, clauses, layout, who signs it.
How to tell it from look-alikes: name the confusable families and the deciding feature.

Run Classify Only on each sample as you write. If a sample scores 0.6 for two types, the instructions do not separate them yet. Ask the SME: “What is the first thing you look at to tell these apart?“

3. Choose what to extract

Pick the subset of the ontology this family can actually supply. Ask: “If this document were the only source, which golden-question facts would it give us?” Extract only those.

Select the entities and, per entity, the attributes. Extraction instructions default from the ontology; override them here when this family words things differently.
Select the relationships between the chosen entities, with their attributes (for example role).
Mark attributes required only if the document always has them.

4. Decide the processing type per entity

Processing type	What it does	Use when
Create Only	Always creates a new node	Things that exist only in this document (an obligation clause)
Create or Match	Runs matching; merges with an existing node or creates a new one	Things that may already exist from other documents or structured loads (clients, people)
Match Only	Updates an existing node; never creates one	Things a system of record owns (projects from finance)
Attach Document	Links the document to this entity; one entity per artifact type	The entity the document is “about” (the contract, the person)

Load structured master data first (Workshop 6), then let documents Create or Match or Match Only against it. Matching detail is Workshop 7. Mark a relationship Required upstream dependency when the child should resolve in the context of its parent. The relationship’s source is the parent, and the parent type needs a vector index (set in Workshop 3).

5. Set the extraction policy

The extraction policy is a cost and accuracy trade-off per family.

Setting	Options	Guidance
Mode	`full` / `metadata_and_snippet` / `metadata_only`	`full` for families that answer questions; snippet or metadata for high-volume, low-value files
Model tier	large / medium / small	Large for contracts and anything legal; medium often suffices for resumes and case studies
Validation pass	on / off	On where missing an entity is costly (obligations); off for simple, short documents
Excel override	same as default, or a different mode	Usually `metadata_only` for exports and pricing workbooks

In the two metadata modes Experio skips entity extraction. It writes one shell node of the attached (or first) entity type, named after the file, plus the snippet when there is one, and no relationships. The parsed text is still stored on the Document node for chat. A cost guard also drops very large files to metadata-only. See Extraction Policy.

6. Map folders to allowed types

Each data source filter can list the artifact types allowed for the files it matches. The allowed set is the union across matching filters, or every type if no filter lists any. Narrowing the set is the cheapest accuracy gain there is: a file in the Resumes library cannot be misclassified as an MSA. Ask the IT admin: “Is each folder dedicated to one kind of document, or mixed?”

Worked example: Northbridge Consulting

Artifact type	Entities (processing type)	Key relationships	Policy	Folder filter
Proposal	Proposal (Create or Match, Attach), Client (Create or Match)	SUBMITTED_TO	full, large, validation off	Engagements
Statement of Work	Contract (Create or Match, Attach), Client (Create or Match), Project (Match Only)	WITH_CLIENT, GOVERNS, AMENDS	full, large, validation on	Engagements
Master Services Agreement	Contract (Create or Match, Attach), Client (Create or Match), Obligation (Create Only)	WITH_CLIENT, IMPOSES (required upstream)	full, large, validation on	Engagements
Resume	Employee (Create or Match, Attach), Skill (Create or Match)	HAS_SKILL	full, medium, validation off	Resumes
Case Study	Project (Match Only, Attach), Client (Create or Match)	FOR_CLIENT	full, medium, validation off	Engagements
Status Report	Project (Match Only, Attach)	none	metadata_and_snippet, small; Excel metadata_only	Engagements

Everything else (templates, invoices, internal memos) is classified as None and skipped. The Resumes library filter allows Resume only; the Engagements filter allows Proposal, Statement of Work, Master Services Agreement, Case Study and Status Report.

Spec: Statement of Work

Helen Park brought four SOWs, one amendment and one MSA. The first draft scored the amendment 0.85 as a SOW and the MSA 0.7 as a SOW, so the room added look-alike rules.

A Statement of Work (SOW) defines the scope, deliverables, timeline, staffing and fees
for one engagement under an existing agreement. Titles include "Statement of Work",
"SOW", "Work Order" and "Change Order". Northbridge SOWs usually show a project code
such as NB-2024-117 in the header and state that they are issued under a Master
Services Agreement.

Classify as SOW:
- Amendments and change orders that modify a SOW's scope, dates or fees.

Do NOT classify as SOW:
- Master Services Agreements: general terms (liability, indemnity, confidentiality)
  with no single project scope, even if they include a sample SOW as an exhibit.
- Proposals: written before award, offering work rather than agreeing it; no
  signature blocks for both parties.

Extraction for the SOW family:

Entity / relationship	Attribute	Extraction instruction (override where shown)
Contract	name	”Title and number of the SOW, for example ‘SOW 3 – Cloud Migration’.”
Contract	contract_type	Override: “SOW for a statement of work; amendment for an amendment or change order. One of: SOW, amendment.”
Contract	effective_date, expiration_date	From ontology (Format: YYYY-MM-DD)
Client	name	”The client party, not Northbridge. Use the legal name in the preamble.”
Project	project_code	”The Northbridge project code in the header, format NB-YYYY-NNN.”
Contract AMENDS Contract		”Only for amendments: the SOW being amended.”

Project is Match Only: a SOW must never create a project that finance does not know. Northbridge is kept out of Client automatically, because the firm name and its synonyms from Client Configuration are sent with every extraction.

Spec: Resume

Sam Whitfield’s samples came in three layouts. The classification instruction was short, because the Resumes folder allows nothing else:

A resume or CV describing one Northbridge employee's experience, skills, education
and certifications. Usually has the person's name at the top and sections such as
"Experience", "Skills" and "Education". Not a project team list or staffing plan,
which names several people.

Entity / relationship	Attribute	Extraction instruction
Employee	name, title	”The person the resume is about. Use the name at the top.”
Employee	email	”Work email if shown; leave blank otherwise.”
Skill	name	”Each technical or domain skill. Choose one value from @Skills.”
Employee HAS_SKILL Skill	proficiency	”Expert, Advanced or Working, from the resume’s own wording. Leave blank if not stated.”

The room decided not to extract projects from resumes. Resume project names (“Lakeshore cloud work”) cannot be matched to project codes, and assignments.csv already holds the truth for who worked on what.

Accelerate with AI

Available today: Artifact Types > AI Assistant. Open an artifact type, open the AI Assistant, and choose up to 10 sample files that a job has already parsed. It drafts the extraction schema against the existing ontology, and you refine it by chatting (“drop Obligation, add liability_cap”). It needs an ontology first. Use it to produce the first draft before the workshop, then correct it with the SMEs using Test Ingestion.

In development — availability depends on your release.

The knowledge-model copilot takes sample documents plus the golden questions (questions.md) and proposes artifact-type changes as cards, which a person reviews, applies and saves on the Artifact Types page. The manual path always works.

Entering it in Experio

Create the artifact type

Go to Model & Define > Artifact Types and create a type. Enter the name and classification instructions, and set Ingestion extraction (mode, model tier, validation pass, Excel mode).

Add entities and relationships

Add each entity with its processing type and, for one entity, Attach document to this entity. Choose attributes and override extraction instructions where needed. Add relationships, their attributes and any required upstream dependency.

Test

Click Test Ingestion, upload a sample, and run Classify Only first, then Classify & Extract. Review classification scores, entities, relationships and raw JSON. Use Max Pages to test long documents quickly.

Map folder filters

In Connect > Data Sources, edit each filter and choose the artifact types allowed for the files it matches.

Detail: Content Types, Extraction Policy, Data Sources.

Common pitfalls

Instructions that only describe. Without “how to tell it from X”, look-alikes score close together and land in review.
Testing only typical samples. Bring the awkward ones; they are what fails in production.
Extracting everything. Each extra entity is cost and a chance of noise. Extract what the questions need.
Create where Match Only belongs. Documents that create clients or projects freely produce duplicates that structured data then cannot fix.
No folder filters. Every file is scored against every type; accuracy drops and reviews pile up.
Metadata mode on a family that answers questions. No entities or relationships are extracted in those modes.

Exit criteria

Every document family is an artifact type or explicitly out of scope (None)
Every sample classifies to the right type at 0.8 or above in Classify Only
Classify & Extract results reviewed with the SME for at least three samples per family
Processing type agreed for every entity, and the Attach Document entity chosen (one per type)
Extraction policy set and justified per family
Folder filters list the allowed artifact types
Artifact types show valid in Model & Define > Compatibility

Workshop 6: Data Mapping maps the structured exports that documents will match against.

Start Here

Plan

Workshops

Validate & Launch

Reference

At a glance

Before the workshop

Agenda

Running the workshop

1. Group the corpus into families

2. Write classification instructions

3. Choose what to extract

4. Decide the processing type per entity

5. Set the extraction policy

6. Map folders to allowed types

Worked example: Northbridge Consulting

Spec: Statement of Work

Spec: Resume

Accelerate with AI

Entering it in Experio

Common pitfalls

Exit criteria

Next

​At a glance

​Before the workshop

​Agenda

​Running the workshop

​1. Group the corpus into families

​2. Write classification instructions

​3. Choose what to extract

​4. Decide the processing type per entity

​5. Set the extraction policy

​6. Map folders to allowed types

​Worked example: Northbridge Consulting

​Spec: Statement of Work

​Spec: Resume

​Accelerate with AI

​Entering it in Experio

​Common pitfalls

​Exit criteria

​Next

At a glance

Before the workshop

Agenda

Running the workshop

1. Group the corpus into families

2. Write classification instructions

3. Choose what to extract

4. Decide the processing type per entity

5. Set the extraction policy

6. Map folders to allowed types

Worked example: Northbridge Consulting

Spec: Statement of Work

Spec: Resume

Accelerate with AI

Entering it in Experio

Common pitfalls

Exit criteria

Next