Extract the signal.
Destroy the noise.
Don't force your analysts to read 50-page contracts. Deploy the FyBrain synthesis engine to compress massive payloads into dense, actionable intelligence in milliseconds—without losing critical data vectors.
Defeat the token limit.
Retain total context.
Infinite Context Window
Bypass standard LLM token limits. We deploy semantic chunking to process 10,000+ page documents with zero context degradation.
Multi-Doc Aggregation
Don't just summarize one file. Inject a folder of 50 contracts and extract a unified intelligence brief comparing terms across all of them.
Verifiable Citations
Hallucinations are unacceptable in enterprise environments. Every synthesized point includes direct coordinate mapping back to the source text.
Deterministic Schemas
Control the exact shape of the output. Instruct the engine to extract only financial risks, formatting the summary as a rigid JSON array.
Destroy the token limit.
Eliminate hallucinations.
Program the output.
Demand citations.
1import fybrain23# Initialize the Context Engine4client = fybrain.Client(api_key="fb_live_*******************")56# Inject payload and define strict extraction parameters7response = client.synthesis.extract(8 source="s3://acme-corp/legal/enterprise_msa_v4.pdf",9 query="Extract all financial liabilities and renewal dates.",10 output_schema={11 "type": "array",12 "items": ["liability_cap", "renewal_date", "penalty_fees"]13 },14 require_citations=True,15 temperature=0.116)1718print(f"Synthesis queued. Trace ID: {response.trace_id}")Synthesize intelligence
across any sector.
Compress the
enterprise noise.
Stop skimming endless PDFs. Deploy the neural synthesis engine to distill thousands of pages into dense, actionable, mathematically-cited intelligence.