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By implementing Aden’s Context-Aware Routing, Lextract.ai slashed LLM inference costs by 42% on high-volume legal audits while maintaining 100% accuracy for critical due diligence reporting.
Lextract.ai, a leader in AI-powered legal due diligence, faced a critical profitability hurdle: the immense computational cost of processing thousands of documents in Virtual Data Rooms (VDRs). Legal accuracy demands intensive computes from Large Language Models (LLMs), but standard "per-token" pricing was eroding margins on large enterprise deals. By implementing Aden’s Cost Control Agents, Lextract transitioned from a flat-rate processing model to Context-Aware Routing, reducing inference costs by 42% while maintaining 100% accuracy on critical "Red Flag" reports.
Legal Tech / M&A Automation
o accelerate legal reviews by using AI to instantly identify risks, red flags, and missing clauses in deal documentation.
Unlike a chatbot, a legal AI cannot hallucinate. Missing a "Change of Control" clause in a merger agreement could cost a client millions, meaning Lextract defaults to the most powerful (and expensive) models available.
In M&A due diligence, 80% of documents are standard (boilerplate) and 20% contain the critical risks. However, Lextract’s initial architecture treated every sentence with equal weight.
Indiscriminate Compute
Processing a standard NDA required the same expensive compute power as analyzing a complex IP assignment agreement.
Uncapped Client Uploads
A law firm might upload 5,000 pages of irrelevant "bulk data" into the system. Lextract was paying to process this "noise" before knowing it was irrelevant.
Margin Variance
A "clean" deal was profitable; a "messy" deal with thousands of scanned, unstructured pages became a loss leader due to OCR and token re-processing costs.
"We were paying GPT-4 prices to read standard 'Governing Law: New York' clauses that haven't changed in 20 years. We needed a way to use a scalpel instead of a sledgehammer."
Lead Machine Learning Engineer
Lextract integrated the Aden Agent SDK to act as an intelligent router and budget warden within their document processing pipeline
Aden analyzes the "perplexity" (complexity) of a text segment. If the text is standard boilerplate, it routes it to a cheaper, faster local model. If the text is complex or ambiguous, it escalates to the flagship LLM.
Agents that track spend per Data Room. If a specific deal's processing costs hit 80% of the subscription fee, the Agent alerts the user or throttles non-essential background tasks.
An Agent dedicated to identifying and skipping duplicate or non-legal documents (e.g., blank pages, cover sheets) before they hit the expensive inference layer.
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Traditionally, hitting a rate limit returns a generic error. Alpha Vantage used Aden to analyze why the limit was hit and offer an upsell path via headers.
Deploy Aden’s instrumented proxy for strict legal environments to govern high-volume document processing pipelines with fail-closed security.
Map document classification and deal-based metadata to every inference call to enable granular cost attribution across massive unstructured data sets.
Activate Context-Aware Routing to automatically triage boilerplate text to cost-efficient models while reserving premium compute for complex legal analysis.
Implement real-time budget enforcement for interactive features like LexChat to prevent runaway query costs and trigger automated upgrade prompts when session limits are reached.
By routing boilerplate text to cheaper models, Lextract stopped "burning cash" on standard clauses. The expensive models are now reserved exclusively for complex legal reasoning.
With Aden's guardrails, Lextract could confidently offer flat-rate pricing to law firms, knowing that the "Noise Filtering" and "Routing" agents would protect them from abusive usage patterns.
The "Triage Agent" meant that 70% of documents were processed by lighter, faster models, reducing the "Time to First Insight" for lawyers by 3x.
For Lextract.ai, Aden served as the bridge between technical capability and commercial viability. By making their software "cost-aware," they ensured that their AI could read every page of a merger agreement without reading their own bank account down to zero.
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