Document Intelligence
They read twelve documents. Cross-referenced findings. Wrote fifteen pages of narrative. Formatted citations. Checked consistency. Then did it again next week. And the week after that.
You run a professional services firm. Your value is expertise — but your bottleneck is documentation. Every engagement produces a report. Every report requires a human being to read source material, extract the relevant evidence, synthesize it into narrative, attribute every claim, and format it for delivery. It is the most important artifact your firm produces, and it is built the same way it was built twenty years ago.
Your analysts are good. That is not the problem. The problem is that reading, extracting, and organizing evidence is not analysis — it is transcription. Seventy percent of the time spent on a report goes to gathering and structuring information. Thirty percent goes to the thinking that actually matters. You are paying expert rates for clerical throughput.
You have tried templates. You have tried checklists. You have tried hiring faster. None of it addresses the fundamental constraint: a human being must read every document, find every relevant passage, and manually assemble the narrative. The quality ceiling is the analyst’s capacity — and that capacity is consumed by the wrong work.
Every week, reports ship late. Not because your team is slow, but because the process is structurally incapable of meeting the demand. You lose revenue to delivery delays and credibility to inconsistency. Somewhere between document twelve and page eight, the quality variance becomes a business risk.
Six capabilities. One pipeline. Every report.
PDFs, spreadsheets, transcripts, survey results, third-party scoring reports — the system reads everything your analysts would, in seconds instead of hours.
Every finding traced back to the exact document, page, and passage. No hallucinated claims. No unsourced assertions. Every statement is provable.
Raw extractions pass through deduplication, conflict resolution, and thematic clustering. Contradictory evidence is flagged, not hidden.
The system writes in your firm’s style — not generic AI prose. Trained on your existing reports, it produces drafts indistinguishable from your senior analysts.
Same evidence standards. Same narrative structure. Same citation format. Whether it’s your best analyst or your newest, the output quality is identical.
The first draft lands before your analyst finishes their morning coffee. They spend their time on judgment and refinement — not extraction and formatting.
Step 01
Upload everything relevant to the engagement — evaluations, surveys, transcripts, third-party reports, historical data. No pre-processing required. No file naming conventions. No manual tagging. The system identifies document types automatically and routes each to the appropriate extraction engine.
Step 02
The pipeline doesn’t just extract — it thinks. Related findings from different documents are clustered. Contradictory evidence is flagged for human review. Patterns across sources are identified and weighted. By the time your analyst opens the draft, the intellectual heavy-lifting is done.
Step 03
The system generates publication-ready narrative from consolidated evidence. Every claim is attributed. Every section follows your firm’s structure. The output is not a summary — it is a complete first draft that reads like your best analyst wrote it on their best day. Your team reviews, refines, and approves. The machine did the transcription. The human does the judgment.
8 hrs → 45 min
Time to first draft
10+
Document types processed automatically
98%
Narrative consistency across engagements
Your analysts didn’t get into this field to read twelve documents and format citations. Give them back the part of the work that actually requires expertise — and let the machine handle the rest.
30-minute discovery call · No pitch deck