Human judgment. Informed prioritization.
AI-assisted document review helps a legal team organize and prioritize electronically stored information (ESI). It can direct attention toward potentially relevant material while reviewers make and check the decisions that matter.
Forensic Discovery helps law firms and businesses plan AI-assisted eDiscovery review around the documents, issues, deadlines, and available platform features. The goal is a useful review workflow with measurable checks, rather than a promise that software will find everything.
This service is part of our eDiscovery services. For processing, culling, early case assessment, and conventional review operations, see eDiscovery assessment and hosted review.
Choose the method for the question
Analytics, technology-assisted review, and generative AI perform different jobs. Availability depends on the licensed features, configuration, and data accepted for the matter. Confirm those details before selecting a workflow.
Concept search and clustering can group related language beyond an exact keyword match. Email threading and near-duplicate grouping can reduce repeated reading where supported. Communication analysis can highlight patterns to examine. Sentiment scores or unusual patterns are review leads, not findings of misconduct, intent, or fraud.
Technology-assisted review (TAR) uses reviewer input to help classify or rank documents. Predictive coding learns from coded examples. In a continuous active learning workflow, additional coding can update the ranking as review proceeds. Consistent instructions, suitable examples, and checks on reviewer disagreement remain essential.
Generative AI may draft summaries or suggested classifications when an authorized feature supports them. These outputs can omit details or invent statements. Reviewers must check them against source documents. Image labels, transcripts, and machine translations also need source checks; they do not replace original images, recordings, or language expertise.
Before using media or multilingual tools, confirm supported formats, transcription and translation languages, output quality, and how exceptions will be handled. Conversation context, attachments, speaker identification, and document families can change the meaning of an isolated result.
A ranked review queue may help the team reach potentially responsive material earlier. Plan reviewer capacity, rolling deliveries, validation, and exception handling together. Adding automation does not remove the time needed for privilege review, redactions, quality checks, and production preparation.
Concept search and clustering can help counsel explore topics beyond an initial search term list. Examine surrounding communications and source records before drawing conclusions. A visually prominent cluster or a high score does not establish relevance, authorship, or a person’s state of mind.
Chat exports, images, audio, and multilingual documents can require different preparation and review methods. Confirm what context the export retains and test a representative sample. Our resource on reviewing modern data for eDiscovery explains the broader review challenge.
Counsel may need to explain the review population, coding instructions, validation approach, and treatment of exceptions. We can support the technical discussion. Counsel determines negotiation positions, disclosure obligations, and any proposed agreement with other parties.
Smaller or tightly defined collections may fit standard hosted review. If relevant sources have not been acquired, start with data preservation and collection; review technology cannot repair missing source material.
| Approach | How material reaches reviewers | Cost drivers | Checks to plan |
|---|---|---|---|
| Linear review | Reviewers work through the defined collection or assigned batches. | Volume, reviewer time, complexity, and quality control. | Coding consistency, sampling, and exception checks. |
| Keywords plus review | Search terms select material for human assessment. | Search development, hit volume, revisions, and reviewer time. | Search testing, missed concepts, family context, and sampling. |
| TAR with human review | Examples coded by reviewers help rank or classify the review population. | Setup, training, licensing, review, and validation. | Sample design, coding quality, validation results, and stopping criteria. |
These approaches can be combined. Prioritization may reduce time spent on material of lower value, but setup and validation can outweigh savings on a small or unsuitable collection. Neither a keyword hit nor a model score is counsel’s final responsiveness decision.
Start with prepared data from the assessment and hosted review workflow. Record what is included, unavailable, excluded, or unsuitable for the chosen method.
Counsel defines the legal issues and review decisions. Confirm feature access, authorized users, data use, validation design, and how changes will be documented.
Knowledgeable reviewers apply the instructions. Resolve disagreements and test how the model handles representative documents before expanding reliance on its rankings.
Reviewers assess ranked material and relevant context. Track coding changes, model runs, exceptions, and any additions to the review population.
Use the agreed sampling method and decision criteria. Where appropriate, estimate recall, precision, and elusion, state uncertainty, and investigate errors before counsel decides next steps.
Complete privilege, redaction, and production checks. Deliver the agreed review records and methodology documentation, including known limitations and unresolved exceptions.
Measure the work, document the limits
Estimates based on samples carry uncertainty and depend on sound reference coding.
A control set can support evaluation where the protocol calls for one. Its selection, separation from training, and later use need care. Low elusion alone does not prove high recall. No sample establishes that every responsive document has been found.
Depending on scope, deliverables may include coded or ranked sets, review instructions, model and workflow records, sampling results with stated assumptions, exception reports, and agreed production files. Confirm load file fields, native or image formats, redactions, and receiving system requirements before export.
Methodology declarations and testimony support require separate scope and a qualified person able to explain the work. See our expert witness and testimony services.
Forensic Discovery connects review planning with the handling of the underlying data. Discuss what the team needs to decide, how source context will be retained, and which records will explain the review later. A documented process gives counsel a basis to assess the work; it does not guarantee court acceptance.
We work with law firms, internal legal teams, review managers, and businesses. Forensic Discovery does not accept direct individual consumer matters. If a law firm represents an individual, the firm may contact us about the matter. See our offices and service locations.
Tell us the deadline, approximate volume, data types, languages, review issues, expected users, and production requirements. Costs depend on preparation, licensed features, hosting duration, training, review staffing, media handling, validation, and exports. Ask for the proposed scope, deliverables, assumptions and pricing in writing before authorizing work.
It is a group of methods that help organize, rank, classify, or interpret a review collection. Analytics can reveal related topics; TAR learns from reviewer coding; generative tools can create proposed text or classifications. They require different checks. Our resource on the advantages of AI in eDiscovery provides background. The appropriate method depends on the actual collection and review task.
Reviewers code examples using instructions for the matter. A model uses those examples to score or classify other documents. Continuous active learning can update prioritization as more coding becomes available. The exact workflow varies by tool and configuration. Training labels can contain mistakes, and a model can repeat them. Review consistency, testing, and validation matter throughout the process.
Acceptance depends on the matter, applicable requirements, protocol, and how the work was performed and explained. No vendor can guarantee it. Counsel decides what must be agreed or disclosed. Documentation should explain the population, instructions, sample design, results, and limitations. Recall, precision, or elusion estimates support evaluation within that context; a score alone does not establish defensibility.
There is no dependable percentage for every matter. Potential savings depend on volume, prevalence of responsive documents, data quality, coding consistency, and the amount of human review still needed. Licensing, setup, validation, and rework also cost time and money. Evaluate a representative sample and compare the proposed total workflow with a conventional approach before relying on projected savings.
It may, subject to supported formats, available features, and preparation. A sample should establish whether conversation context, attachments, timestamps, and speaker information survive ingestion. Image labels, transcripts, and translations can be wrong or incomplete. Keep the originals available and arrange appropriate checks. Modern-data review considerations can help frame the initial discussion.
Costs can include preparation, hosting, feature licenses, model setup, reviewer time, validation, and production. Media, language work, repeated data additions, and unusual output requirements can change the estimate. Ask what is included, what triggers additional charges, and who performs each task. Ask for the proposed scope, deliverables, assumptions and pricing in writing before authorizing work.
Yes. Counsel sets legal criteria and remains responsible for responsiveness, privilege, disclosure, and production decisions. Reviewers apply coding instructions and check results. Model rankings and generated summaries do not replace source review or professional judgment. Involve technical support early when the collection, proposed method, sampling, or production requirements create questions the review team cannot resolve alone.
Before uploading data, confirm the specific platform, enabled tools, access roles, encryption, audit records, retention, deletion, subprocessors, and transfer arrangements. Establish whether prompts, documents, or outputs may train models or leave the review environment. Do not assume model training is limited to the matter or that an AI label guarantees confidentiality. Our hosted-review security resource offers questions to discuss; current settings and terms need separate verification.
Compatibility depends on the source export, receiving system, and agreed specifications. Confirm supported load files, metadata fields, identifiers, family relationships, redactions, natives, and images. Test a sample before committing to the transfer. Review labels, model scores, audit history, and generated annotations may require separate handling and may not transfer in the same form.
A small collection, narrow issue, or established search term protocol may not justify model setup and validation. Processing and early assessment may already reduce the collection to a manageable size. Assessment and conventional hosted review may be the practical starting point. Select the method for the actual work and constraints rather than the label attached to the software.
Tell us what the legal team needs to review and when. We can discuss the collection, available methods, validation questions, and a written scope for AI-assisted hosted review. Use the form for a general inquiry; arrange an appropriate transfer method before sharing sensitive documents.
"*" indicates required fields
(866) 458-4993 
