Wolf&Grayson EditorialArtificial intelligence (AI) has been pointed at nearly everything that carries commercial weight: drug discovery, scientific research, enterprise productivity, the long tail of white-collar busywork. What it has largely not been pointed at is the group of people whose lives would change most if it were. Foster youth aging out of the system at 18. Seniors dependent on Medicaid. Survivors of domestic violence who left home without their paperwork. Human trafficking victims. These populations are small in number, which is precisely why they get overlooked in drug discovery, in product roadmaps, and in the design of public systems. Anu Ramraj, who leads Vaultzy Inc, argues that the gap is not technical. The capability already exists. What is missing is the decision to aim it at people who do not represent a lucrative market, and the design discipline to build something they can use.
The Support System Nobody Codified
Most individuals navigate bureaucracy with invisible scaffolding. A parent who mentions a deadline. A friend who has filed the form before. A colleague who knows which office to call. Strip that scaffolding away and the same tasks become nearly impossible, not because they are intellectually hard, but because nobody is tracking them. Ramraj frames this bluntly: the people she is describing lack the support system the rest of us take for granted. Nonprofits step into that role, guiding foster youth or trafficking survivors through the maze of benefits for which they may be eligible. But those organizations are constrained by headcount. There is a hard ceiling on how many people one caseworker can shepherd.
That ceiling is where Ramraj sees AI doing its most useful work: by codifying guidance into assistants that supplement what human staff provide rather than replacing them. An agent that knows which benefits a person qualifies for, when the eligibility windows open and close, and what documents each one requires. The same logic extends backward in time, toward preparation rather than recovery. Getting people document-ready before the crisis, as Ramraj puts it, means guiding a foster kid toward what they will need before their 18th birthday, helping households in fire-prone or flood-prone areas assemble records in advance, or helping a domestic abuse survivor put paperwork in place before she walks out the door. Language access compounds the effect. Large language models operate across more than 90 languages with high fluency, which means the guidance can arrive in the language the person speaks.
Deadlines Are The Barrier, Not Complexity
The failures that derail these lives are rarely dramatic. They are missed dates. Ramraj points to the Free Application for Federal Student Aid (FAFSA) – the financial aid application a foster student must file every single year to stay in college. For decades, college completion has remained an elusive milestone for young Americans who have experienced foster care. A systematic review published in April 2025 found that only 8% to 12% earn a two- or four-year college degree, highlighting the persistent barriers these young people face. Part of that stagnation traces to something as unremarkable as an application nobody reminded them to submit. Foster kids face homelessness at record numbers, and someone trying to secure a roof is not thinking about student aid deadlines or community college enrollment windows.
An assistant that flags what is due and when, then helps complete the application, is addressing the failure point. Systems can now autofill forms using data already on hand, which matters enormously for someone who is language challenged or simply overwhelmed. Ramraj describes the role as a life assistant: tracking expirations, surfacing eligibility timelines, or nudging someone to renew a driver’s license before it lapses and takes a job opportunity with it. None of this is technically sophisticated. It is the mundane maintenance work that functional support networks perform for free, rebuilt as software for people who do not have one.
Equity Lives In The Design, Not The Algorithm
The industry conversation about AI fairness tends to fixate on model bias, which is the wrong frame for this problem. Ramraj’s concern is more concrete: a system built for vulnerable populations must not impose a technology burden on them. Some of the people for whom she is designing have no mobile device. Others have a phone but no computer. A domestic violence survivor may face access restrictions that make certain channels unsafe. The platform has to function on any device, on any connection, and it has to accommodate disability and language barriers rather than treating them as edge cases. Equity, in her telling, is a design constraint, not a model-tuning exercise.
Her LifeScore concept applies the same thinking to the output. Think of it as a credit score for life readiness, tracking three things at once: whether your documents exist and remain current, whether your coverage is adequate, and whether human support structures are in place. The insurance distinction matters. After the California fires, some homeowners discovered their policies were active but would not cover rebuilding. A LifeScore accounts for powers of attorney, executors, trusts and wills, and whether at least one family member could step in. Let documents lapse and the score drops, the same way a missed payment hits credit. And privacy is non-negotiable, because this data in the wrong hands endangers a trafficking survivor more than it inconveniences anyone else. The data never gets sold.
The shift Ramraj sees arriving is records moving from being static, whether paper or digital, to actionable and live, with a human in the loop prompting the right step at the right moment. The complication is that the same technology makes records easier to fake. A convincing college transcript is now trivial to generate, which means AI-powered access will also mean AI-powered fraud. Complementary mechanisms such as blockchain anchoring, proving a document was issued by a specific party on a specific date, may become the only way to verify anything. Leaders building in this space should assume both futures arrive simultaneously.
Follow Anu Ramraj on LinkedIn for more insights on AI, digital identity, and access to vital records.
