Your judge has a record.
Read it before sentencing.

This judge. This offense. This quantity.

Guidelines give you a range. They do not tell you where this judge actually lands.

Michelen.ai builds a statistical profile for every active SDFL federal judge from real sentencing history: median, minimum, and maximum sentences broken down by offense type. For drug cases, the data goes further. You see sentencing patterns by drug type (cocaine, heroin, fentanyl, methamphetamine) and by quantity bucket. Your client is facing a 500-gram-to-1.5-kilogram cocaine charge? You see what this judge has done with that specific exposure, not the guideline range, not national averages.

Fraud cases work the same way. Sentencing patterns by loss amount tier, from under $9,000 to over $1.5 billion.

This is the data you need before you decide whether to fight, plead, or try to move the court. It exists. It was always in PACER. Michelen.ai surfaces it.

The prosecutor across the table has a record. Read it.

No tool on the market gives you per-AUSA criminal intelligence. Michelen.ai does.

For every SDFL prosecutor, you get aggregate sentencing data drawn from their case history: plea count, trial count, average sentence, top offense types, and recent case outcomes. You know going into a negotiation whether this AUSA takes cases to trial or resolves them. You know their pattern with defendants charged with the same offense as your client.

Michelen.ai extracts it from PACER and CourtListener RECAP data and presents it as a profile you can read in two minutes.

The cases you should be citing. Ready before you write.

Every sentencing memorandum needs comparators. Finding them takes hours of PACER searching, citation-checking, and judgment calls about what qualifies as similar.

Michelen.ai automates that. Give the system a defendant, an offense type, and a judge. It returns the most factually similar sentenced cases from its database, tiered by match quality. Same judge, same offense, similar sentence range comes first. If the pool is thin, it widens to the same offense family, then district-wide.

The output is a list of specific, citable cases you can use in a Section 3553(a) argument. Not a summary. Not a suggestion. Actual cases with actual outcomes that mirror your client's exposure.

Know what the district is doing before your clients tell you.

Michelen.ai pulls new charges and sentencing outcomes from PACER, CourtListener, and DOJ press releases as they come in. The dashboard shows 7-day and 30-day counts of new charges and sentencings, broken down by offense family. You see charging trends. You see what is moving.

Every Friday, a formatted Weekly Intelligence Digest lands in your inbox: SDFL charges and sentencings from the past seven days, with offense types and case numbers. Included with every plan, starting on day one of your free trial. If you want to stay current on the district without building a monitoring routine, it comes to you automatically.

The Press Release Archive covers all USAO-SDFL and national DOJ press releases, fully searchable by defendant name, AUSA name, investigating agency, offense type, and date. Not raw PDFs. Structured, queryable records.

24 years of federal sentencing data. Filtered to what you need.

The Sentencing Search covers the full SDFL federal sentencing database, filterable by judge, AUSA, investigating agency, offense type, and date range. You can scope it to SDFL or go national. Every filtered result set returns a median sentence for the query.

Behind it sits US Sentencing Commission data from FY2002 through FY2025. 24 years of national sentencing records, integrated with the 2025 USSG Manual.

The Charge and Indictment Search covers all federal charges in the system. Multi-defendant cases collapse to show co-defendants and case captions, so you can read a case as a case rather than as a list of individual docket entries.