Overview

When you create an Order, Ollang can look at the source content together with the context assets you selected (Memories, Glossaries, Style Guides, guidelines) and tell you, before checkout, how much of the work is already covered by approved material. After the Order is translated, the same signals are recorded per segment as evidence, which drives the review classification and the human-review estimate shown on the Order details page. This page explains every number you will see along the way:

Full (exact) match and fuzzy match against a Memory
Glossary coverage and Style guide availability
The Translation readiness score and the suggested review policy
Segment classification (auto-approved, review recommended, flagged)
Assets utilized, evidence, and the billable / words for review estimate

Translation readiness is advisory. It never blocks checkout, never changes the price you are quoted, and never rewrites your workflow’s review settings by itself. Use it to decide which assets to attach and how much human review to plan for.

Readiness scoring, the context asset picker, and the Assets utilized card are feature-gated. If you do not see them on the New Order or Order details pages, contact your Ollang account manager to enable context management for your workspace.

Context assets

A context asset is any piece of approved material Ollang can ground a translation on. Each asset carries a type, a scope level, and a selection source that explains why it was used for a given Order.

Asset type	What it holds	Scope levels
`memory`	Translation memory: previously approved source/target pairs	Workspace
`glossary`	Approved terminology, including forbidden terms	Organization, workspace, project
`style_guide`	Document-wide style rules (tone, formatting, register)	Workspace, project
`instruction`	Global custom instructions	Workspace, project
`guideline_doc`	Folder- or project-level guideline documents	Project
`subtitle_config`	Subtitle formatting configuration	Project

Selection source	Shown in the UI as	Meaning
`explicit`	Selected	You picked the asset in the New Order form (or passed its ID to the API).
`resolved`	Auto-resolved	Ollang attached it automatically from the folder / project / workspace scope and language pair.
`legacy`	Legacy	Attached through an older mechanism (for example project guidelines defined before context management).

Memories are never auto-attached: a Memory is only used when it is explicitly selected for the Order. Glossaries and Style Guides are auto-resolved from the project and workspace scope (glossaries additionally filtered by source language) unless you make an explicit selection, in which case your selection is authoritative. For how to create and manage these assets, see Memory, Guidelines, and Custom Instructions.

Memory matching

Matching is performed per segment. A segment is one subtitle cue, one document paragraph / cell, or one line of the source text, depending on the Order type. Before comparing, both the source segment and every Memory entry are normalized:

lowercase
trim leading / trailing whitespace
collapse repeated whitespace to a single space
remove the punctuation characters  . , ! ? ; :

So "Save changes?" and "save changes" are treated as the same text.

Full (exact) match — “TM exact”

A segment is a full match when its normalized text is identical to the normalized source text of an entry in one of the selected Memories.

Only active Memory items are considered; removed items are ignored.
Each exact match is recorded as deterministic evidence of kind tm_exact with a match score of 1.0.
TM exact coverage = matched segments ÷ all non-empty source segments, as a percentage.

A full match is the strongest signal Ollang has: the segment already has an approved translation, so it needs the least (often no) human attention.

Fuzzy match — “TM fuzzy”

Segments that did not get a full match are then compared against Memory entries using token Dice similarity: the two normalized texts are split into unique words and

similarity = 2 × |A ∩ B| / (|A| + |B|)

where A and B are the word sets of the source segment and the Memory entry. A segment counts as a fuzzy match when its best candidate reaches the 70 % similarity bar (0.7).

Source : "Click Save to keep your changes"
Memory : "Click Save to keep the changes"

A = {click, save, to, keep, your, changes}   (6 words)
B = {click, save, to, keep, the,  changes}   (6 words)
A ∩ B = {click, save, to, keep, changes}     (5 words)

similarity = 2 × 5 / (6 + 6) = 0.83  →  fuzzy match (≥ 0.70)

TM fuzzy coverage = fuzzy-matched segments ÷ all non-empty source segments.
Exact and fuzzy coverage are disjoint: a segment is either exact, fuzzy, or unmatched, so the two percentages can be added.
If no segment reaches the bar, the readiness card explains why, for example “No segment reached the 70% similarity bar — closest matches averaged 41%.”

A fuzzy match means an existing translation is close enough to be reused with post-editing. It is not a finished translation, which is why it receives partial credit in the readiness score (see below).

Matching scans a bounded number of Memory entries and source segments per Order. Very large Memories or source files may report the tm_scan_capped caveat, meaning coverage is a lower bound rather than an exhaustive count.

Glossaries

Glossary terms are matched as whole words or phrases inside each source segment (so add does not match inside address), with Unicode-aware boundaries so that non-Latin scripts and scripts without spaces (Chinese, Japanese, Thai, …) match correctly. Two different glossary percentages exist, and it helps to know which one you are looking at:

Metric	Definition	Where it is used
Glossary (segment coverage)	Share of source segments that contain at least one approved glossary term	Readiness score (`glossary` component)
Term coverage	Share of the glossary’s terms that appear somewhere in the document	Diagnostic shown on the readiness card

Each term hit is recorded as deterministic evidence of kind glossary_term, including the source term, the target term, and whether the term is forbidden. Forbidden terms are terms your glossary marks as do not use. When a forbidden term is present in the source:

the readiness report adds the forbidden_terms_present caveat, and
every segment containing it is always flagged for review, regardless of any other score.

Style guides

A Style Guide contains rules that apply to the whole document (tone, register, formatting, capitalization, …) rather than to individual segments, so there is no meaningful per-segment coverage percentage for it. Readiness therefore only reports availability:

a Style Guide is attached → the style component is 100 %
no Style Guide is attached → Not available (0 %)

Guideline documents, custom instructions, and subtitle configuration are resolved and listed alongside Style Guides in the Assets utilized card, but do not contribute to the readiness score.

Translation readiness score

The Translation readiness card on the New Order form combines all the signals above into a single 0–100 confidence score. It is computed asynchronously: the card creates a readiness report, polls until it is completed, and then shows the breakdown.

Inputs

Component	Weight	Value used
Translation memory	0.55	combined TM coverage = TM exact % + TM fuzzy % × 0.6 (clamped to 0–100)
Glossary	0.15	glossary segment coverage %
Style guide	0.10	100 if a Style Guide is attached, otherwise 0
Historical automation	0.20	100 − average human-edit % on your previous Orders (defaults to 50 until at least 3 completed Orders with edit data exist)

readiness = 0.55 × (exact% + 0.6 × fuzzy%)
          + 0.15 × glossary%
          + 0.10 × (styleGuide ? 100 : 0)
          + 0.20 × historicalAutomation

The result is rounded and clamped to 0–100. Worked example — the card above: 5 source segments, 2 exact matches (40 %), no fuzzy matches (0 %), 3 segments carrying an approved glossary term (60 %), no Style Guide, no order history yet:

TM combined  = 40 + 0.6 × 0           = 40
readiness    = 0.55 × 40              = 22.0
             + 0.15 × 60              =  9.0
             + 0.10 × 0               =  0.0
             + 0.20 × 50              = 10.0
             ------------------------------
                                       41  →  "Needs attention"

Attaching a Style Guide would add 10 points; one fuzzy match (20 %) would add 0.55 × 0.6 × 20 ≈ 6.6 more. The fuzzy credit of 0.6 reflects that a fuzzy match is reusable but still needs post-editing. The card also shows the combined TM figure so that the breakdown reconciles with the headline score.

Bands and suggested review policy

The score is mapped onto the same classification thresholds that are later used for segment classification, so what you see before the Order and after the Order line up:

Score (defaults)	Label	Suggested review policy
95 – 100	Ready	`ai_only_spot_check` — AI translation with a spot check
66 – 94	Review recommended	`review_recommended` — standard human review after translation
0 – 65	Needs attention	`full_review` — full human review after translation

Thresholds are configurable per workflow (see Review Gates); the readiness report records the thresholds that were actually used. The suggested policy is a recommendation only. Ollang does not change your review nodes automatically.

Caveats

The report lists caveats that qualify the number:

Caveat	Meaning
`no_source_text`	No text could be extracted from the source, so TM and glossary matching could not run. The score is forced to 0 even if assets are attached.
`tm_scan_capped`	The Memory / source scan hit its size cap; coverage is a lower bound.
`forbidden_terms_present`	The source contains glossary terms marked as forbidden.

The report also records its content availability: full_text when source segments were available for matching, counts_only when only source document URLs were supplied and the text still had to be fetched and parsed server-side, and none when nothing could be read (which produces the no_source_text caveat). The card also tells you what to add to raise a low score (for example attach a Memory for this language pair, or a Style Guide).

Segment classification after translation

Once the Order is translated, each segment is scored from the evidence recorded for it and classified into one of three review buckets.

Evidence on the segment	Segment score
Exact Memory match (`tm_exact`)	100
Fuzzy Memory match (`tm_fuzzy`)	the fuzzy similarity (e.g. 83)
Glossary term only (`glossary_term`)	70
Glossary term in addition to a non-perfect TM / AI signal	+5 bonus, capped at 100
AI-reported confidence only (`ai_reported`)	the AI confidence for that segment
Forbidden glossary term present	always flagged

Segment score (defaults)	Classification
≥ 95	Auto-approved
66 – 94	Review recommended
< 66	Flagged

The classification thresholds are the same autoApproveMinScore / reviewMinScore pair used for the readiness bands. The counts of segments in each bucket are what the Assets utilized card summarises as the review classification roll-up.

Assets utilized

The Assets utilized card on the Order details page (Project Management → Order) shows, after translation, which context assets actually influenced the Order and how much of the content is grounded in them.

For each asset it lists:

Name and type (Memory, Glossary, Style guide, Instruction, Guideline document, Subtitle config)
Version of the asset at the time of the Order, and its scope level
Selection source: Selected, Auto-resolved, or Legacy
Evidence count: how many individual matches were recorded against this asset
Grounded segments: how many distinct segments have at least one match from this asset

Below the assets you see the usage summary:

total evidence records and the number of segments with any evidence
evidence by kind (tm_exact, tm_fuzzy, glossary_term, style_rule, instruction, ai_reported)
the review classification roll-up: auto-approved / review recommended / flagged counts
the overall AI confidence reported by the translation pipeline and the context score

Every segment can be opened to inspect its evidence: which Memory entry or glossary term matched, the match score, and the AI justification where available.

Deterministic evidence (tm_exact, glossary_term) is produced by Ollang’s matching services and is exact. ai_reported evidence is the confidence the translation model reported for that segment; it is stored separately, is not counted as an “asset”, and is shown with its justification so you can judge it.

Human review estimate and billable words

The card ends with a Human review estimate that translates the classification roll-up into words:

words for review = round( totalWords × (1 − autoApproved / totalClassified) )

and displays it as <total> total → <words for review> billable. For example, an Order of 10,000 words where 6,200 of 8,000 classified segments were auto-approved shows:

10,000 × (1 − 6,200 / 8,000) = 2,250 words for review
(10,000 total → 2,250 billable)

How to read this:

It is a proportional estimate: the auto-approved share of segments is applied to the word count; it does not sum the exact word counts of the non-auto-approved segments.
“Billable” here refers to the words you should plan to pay a human reviewer for (in-house or LSP). It is not the Order’s price: the Order was quoted and charged at checkout, and this estimate does not change that quote, the credits consumed, or any invoice.
Use it to size a Review Gate assignment or an LSP purchase order, and compare it against the pre-order readiness score to check that your assets are paying off.

Putting it together

Attach context

On the New Order form, select the Memories you want to reuse. Glossaries and Style Guides for the project and language pair are auto-resolved; adjust the selection if needed.

Read the readiness score

Check TM exact / fuzzy coverage, glossary coverage, and style-guide availability. Follow the card’s suggestions (attach a Memory, add a Style Guide) if the score is low. Readiness is advisory and does not block checkout.

Choose a review policy

Use the suggested review policy to configure the workflow’s Review Gate: spot check for “Ready”, standard review for “Review recommended”, full review for “Needs attention”.

Review the Assets utilized card

After translation, confirm which assets were used, inspect flagged segments, and use the human review estimate to plan reviewer effort.

Feed results back

Approved reviews grow your Memories, which raises exact/fuzzy coverage, the historical-automation component, and future readiness scores.

Quick reference

Term	Short definition
Full / exact match	Normalized source segment identical to a Memory entry; score 100
Fuzzy match	Best Memory entry with token Dice similarity ≥ 0.70; score = similarity
TM combined coverage	exact % + 0.6 × fuzzy %
Glossary coverage	% of segments containing an approved glossary term
Term coverage	% of glossary terms appearing in the document (diagnostic)
Style guide availability	100 % if a Style Guide is attached, otherwise not available
Historical automation	100 − average human-edit % over previous Orders (default 50)
Translation readiness	0.55·TM + 0.15·Glossary + 0.10·Style + 0.20·History, 0–100, advisory
Auto-approved / Review recommended / Flagged	Segment classification at ≥ 95 / ≥ 66 / < 66 (configurable)
Assets utilized	Assets with recorded evidence on the Order, with version, scope, selection source, evidence and segment counts
Words for review (“billable”)	totalWords × (1 − autoApproved ÷ totalClassified); a review-planning estimate, not the Order price

​Overview

​Context assets

​Memory matching

​Full (exact) match — “TM exact”

​Fuzzy match — “TM fuzzy”

​Glossaries

​Style guides

​Translation readiness score

​Inputs

​Bands and suggested review policy

​Caveats

​Segment classification after translation

​Assets utilized

​Human review estimate and billable words

​Putting it together

​Quick reference

Overview

Context assets

Memory matching

Full (exact) match — “TM exact”

Fuzzy match — “TM fuzzy”

Glossaries

Style guides

Translation readiness score

Inputs

Bands and suggested review policy

Caveats

Segment classification after translation

Assets utilized

Human review estimate and billable words

Putting it together

Quick reference