Proven Across Real-World Legal Workflows
45+
Unique Matters
561K+
Documents
25+
Clients
Modern Review Starts Here
Expert Guided + Human Oversight
Forest is operated by our in-house AI and Analytics consultants, who oversee workflows built for flexibility and defensibility. By combining leading large language models with expert-tuned prompts and human-centered review processes, Forest delivers accurate results legal teams can trust, without compromising precision, oversight, or control.
Cost-Efficiency + Scalability
Forest supports expanding data demands by allowing review workflows to scale without equivalent increases in staffing or duration. Through a balanced combination of AI intelligence and human oversight, clients achieve reduced costs, deeper insights, and shorter timelines across even the most demanding matters.
Security + Compliance
Forest is deployed entirely within Innovative Driven’s governed environment, protecting client data with strict privacy controls and no retention after processing. With FedRAMP Authorization since October 2025, Forest meets the needs of regulated and government matters while preserving client control over access and compliance.
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One Comprehensive Application for Your Complex Legal Data Challenges
AI for Document Summarization
Transform long-form documents into clear, actionable summaries tailored to your guidance. Forest surfaces key facts, identifies tone or sentiment, and supports cross-border matters with concise and accurate insights – even across multiple languages. Key documents can also be automatically slotted into timeline views, giving reviewers and attorneys instant chronological context.
AI for Privilege
Accelerate privilege log drafting through structured, AI-assisted narratives aligned with your existing protocols. Forest can be more cost efficient than other tools with potential savings of up to 40% over traditional methods, even when undertaking a 100% human review of the Forest-drafted log lines. With additional built-in efficiency, Forest supports stringent sampling workflows to reduce touchpoints while maintaining accuracy.
AI for Review
Classify documents based on case-specific background, key players, and relevancy issues or RFPs. Forest distinguishes truly responsive content from non-responsive or marginal material by using tailored prompts, sentiment, and reasoning analysis. Edge cases and low-confidence predictions are automatically flagged for human review to establish defensibility even in nuanced or high-stakes matters.
AI for Internal Investigations
Accelerate internal and regulatory investigations with document-level analysis and defensible outputs. Forest evaluates each document against specific questions, providing responsiveness determinations, confidence scores, and narrative rationales to surface high-value material. Forest then generates synthesis memos that consolidate findings across large document populations, including overarching patterns, document citations, and avenues for further inquiry.
Features for Efficiency
Nuanced
Translation & Interpretation
Image Analysis
Photos & Handwriting
PII & Sensitive
Information Detection
Timeline for
Key Documents
Data Security is a Priority
- Forest is integrated with Microsoft Azure’s OpenAI and Google Gemini
- Customer data is stored and utilized only in accordance with ID’s controls.
- Customer data is not used by Microsoft or Google to improve their products and is stored ephemerally only to provide AI services to the customer.
Case Studies
Challenge: A national law firm was preparing a privilege log for a fast-moving investigation with over 5,000 privileged documents. Traditional manual logging would have required significant attorney time and risked challenge to the formulaic descriptions.
Solution: Using Forest, the review team entered their privilege review protocol, designated attorney names and roles, and specified the output format required by the court. Forest then generated modular, fielded log entries including:
- Document type
- Privileged action and description
- Legal entities involved
- Subject matter (“regarding”)
These components were automatically structured into separate fields, enabling mass editing and batch updates. When one of the listed attorneys changed her last name midreview the team updated a single field — and Forest automatically applied the change across all relevant entries, eliminating hundreds of manual edits.
Results:
- 70% reduction in time-to-log versus manual drafting
- Consistent, diverse, and defensible descriptions across all entries
- Streamlined quality control and rapid responsiveness to necessary changes
Challenge: A litigation team was facing a tight deadline: 1,700 key documents had been identified for review ahead of a high-stakes deposition cycle. Senior attorneys needed to understand each document’s significance — and how they fit together chronologically — without reading every page.
Solution: Using Forest, the team generated structured summaries for each document, guided by matter-specific instructions and issue framing. The output included:
- A concise, three-sentence summary of each document
- Highlighted key facts and quoted language
Forest then populated a timeline view using these summaries, allowing attorneys to quickly explore the document set by date, theme, or custodial source. No embeddings or training were required — just consistent prompt logic.
Results:
- 1,700 documents summarized in less than a day
- Chronological and thematic context delivered in an intuitive, web-based timeline with links back to the source documents
- Enabled senior attorneys to spot gaps, contradictions, and key admissions before depositions began
Challenge: In high-volume matters where manual responsiveness review is time- and cost-prohibitive, Forest enables a prompt-driven, AI-assisted relevance workflow modeled on traditional TAR 1 principles — but without the need for training sets or model tuning.
Solution: Forest was deployed to classify documents for responsiveness based on case background, custodian information, and specific RFPs or issues. The workflow included:
1. Prompt Iteration
Forest’s template prompt was refined through iterative testing and feedback from senior reviewers, incorporating reasoning, issue context, and language tone calibration.
2. Control Set Validation
A randomly selected control set of human-coded documents was held back and used to validate Forest’s output, measuring agreement rates, identifying edge cases, and assessing success.
3. Edge Case Triage
Documents flagged as borderline or low-confidence were automatically routed for human review, preserving cost savings while mitigating risk.
Results:
- Delivered TAR-like defensibility without the overhead of continuous training
- Reduced review volume by 60–80% while maintaining alignment with senior reviewers
- Created a clear audit trail of prompt iterations and validation results to support production decisions
Clients Depend on Forest for Faster, More Accurate Reviews
Forest is reviewing with more accuracy than the 1LR human reviewer.
I’d like to give a huge thank you to you and your team for a really cool project. The client was thrilled with the output.
Our review team said that Forest is doing a great job. They are particularly impressed with the handwriting transcription and document summaries. Thank you all SO MUCH for your help on this, this is truly a fun project