This is the full argument for what MIRA is and why it is different. Not a pitch. An explanation.
The difference is not cosmetic.
MIRA reads the brief. It picks the right skills from a library of 215. It orders them into a plan. It reasons through each step. It verifies its own reasoning against a layer we built specifically to catch its mistakes.
Then, and only then, it produces the output. Every deliverable arrives with its reasoning chain attached, so a senior lawyer can trace exactly how the work was done.
That is not an answer. That is intelligence. And that is what has changed this quarter.
The simplest way to think about MIRA is to think of a human legal assistant you hire and pay by the month. When you hire a real person, you are paying for their skills, nothing more.
An analyst tracking 50 stocks does not need tax skills. What she needs is daily news on those 50 companies, any SEBI circular that touches them, and movements at the sector level.
Maybe 15 skills out of the 215, not all of them.
Picks skills around bail applications, charge sheet analysis, evidence admissibility, sentencing mitigation, witness preparation. Not IP. Not M&A. Not tax.
Picks daily news tracking, SEBI circular monitoring, sector level movement alerts, company filings parsing. Perhaps 15 skills out of 215.
Picks tax notice response, transfer pricing, advance ruling, GST litigation, ITAT appeals. The skills of a focused tax practice, not a generalist.
A senior lawyer can trace exactly how the work was done. Not a black box. An audit trail.
Imagine opening a draft and seeing every step that led to it. Which skills fired. Which sources were consulted. What the meta reasoning layer flagged and what it cleared. Why this precedent was chosen over that one.
That is what every MIRA deliverable carries. The output, and the chain behind it. A partner can skim the chain in minutes, drop into any step to interrogate it, and sign off with confidence, or mark a specific branch for rework.
That is the thing that makes legal AI trustable, and the thing that makes it accountable.
Large reasoning models have crossed the threshold where step by step legal reasoning is affordable at enterprise scale. What cost 50 USD per matter two years ago costs cents today.
Our 14 million document Indian legal corpus, curated and metadata structured over decades, is the substrate. Without it, any legal AI, no matter how smart, fails at grounding.
The meta reasoning layer, the thing that checks the answer before you see it, is what turns a research assistant into a workforce. That is what shipped this quarter.
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