Eliminate Manual Errors in Your Firm's Time Tracking and Billing

Practice management software like Clio or MyCase has built-in time tracking, but these tools require disciplined, structured data entry. Attorneys must manually start and stop timers or enter detailed notes in specific formats. The system fails because attorney workflow is often unstructured; a critical thought is jotted on a legal pad, not entered into a time tracker. This forces a paralegal to manually decipher and translate notes, leading directly to lost billable hours.

Consider a 5-attorney firm where lawyers track time in three different ways: dictated audio notes, daily summary emails, and a shared spreadsheet. At the end of the month, a paralegal spends three full days trying to consolidate these sources. They chase down attorneys to clarify cryptic entries like "call w/ opposing counsel" or "research re: precedent." This process is frustrating for everyone and introduces a significant risk of under-billing or creating invoices with vague descriptions that a client might dispute.

The structural problem is that off-the-shelf software is architected for structured data input, not unstructured text interpretation. These tools are databases with a clock attached. They cannot read a block of text and extract the client, matter, task, and duration. Without a natural language processing layer to bridge the gap between an attorney's raw notes and the billing system's rigid format, firms are stuck with manual, error-prone data entry that costs them 5-10% of their revenue.

The first step is a data audit. Syntora would analyze 3 months of your firm's raw time entries, including emails, spreadsheets, and scanned notes. This process identifies the unique patterns, abbreviations, and jargon your attorneys use. Based on this audit, you would receive a scope document detailing the proposed parsing logic and the confidence score required for an entry to be automated versus flagged for human review.

The technical system would be a FastAPI service using the Claude 3 Sonnet API. This AI model is well-suited for parsing legal text and following complex instructions. The service processes each raw note, extracts the client, matter, task description, and duration, and structures the data using Pydantic models. The entire process would run on a schedule using AWS Lambda, which keeps monthly hosting costs under $50 and processes each note in under 2 seconds.

The delivered system provides the paralegal with a daily draft billing report. This report shows the original note alongside the AI-parsed entry and a confidence score. Entries above a 95% confidence score could be automatically sent to QuickBooks or Clio via their APIs. You receive the full Python source code, a runbook for maintenance, and complete documentation. Attorneys do not need to change their workflow or learn new software.