Every client book, one platform

Advisors consolidate client accounts by aggregating position and transaction data from every custodian, broker, and platform a client uses into a single reconciled view per client, so the review reads one book rather than a stack of statements. Quant automates this aggregation with read-only connections where the custodian supports them and document upload where it doesn't: its AI reads the holdings out of a statement or export, and the advisor confirms them.

Consolidation in Quant goes beyond a per-client net figure. Because every fund and wrapper is resolved to its underlying holdings, each client's true asset allocation, sector and currency exposure, and single-name concentration are measured on what they actually own, and the same analysis rolls up across the whole practice, so the advisor can see aggregate exposure across every book at once. Returns and attribution are calculated the same way at both levels, consistent and explainable.

Manual consolidation is the largest hidden cost in an advisory practice: hours of assembly before every review, error-prone rekeying, and analysis that thins out as you move down the client list. Automated daily consolidation makes the full depth available for every client at no marginal effort.

An independent advisor should look for five things in wealth management software. Consolidation across every custodian and account type a client might hold, including the alternatives that sit outside standard platforms. Look-through that resolves funds and wrappers to their underlying holdings, so concentration and overlap are measured, not assumed. Performance calculation that handles client cash flows properly and stands up to benchmark comparison and attribution. Client-ready reporting generated from the data the analysis runs on, so the numbers in the pack always match the numbers on screen. And strict per-client data segregation with read-only account access.

Quant is built around exactly this list. Every client's book is consolidated and looked through in one platform, analysed to institutional depth regardless of account size, and reported with every number traceable to its source. Access is read-only by design, each client's data stays its own, and the whole practice is queryable through the advisor's own AI assistant, so the platform answers questions instead of just storing data.

The practical test is whether the software can take a new client's messy reality, say three custodians, an old pension, and a fund platform export, and produce the same quality of analysis and reporting the practice's best-served client gets, without a spreadsheet in between.

Yes. Quant generates performance, exposure, and attribution reports for any client and any period, on demand or on a schedule. Review packs, committee papers, and audit-ready statements all draw from the same governed data layer the live analysis runs on. There is no separate reporting pipeline to reconcile: the number in the report is the number in the platform, and every figure is traceable back to the source account and transaction.

For a practice, this changes the economics of the review cycle. Reporting stops being an evening of exporting, formatting, and cross-checking per client, and becomes a by-product of data that is already consolidated, looked through, and reconciled daily. Reports stay consistent across every client, with the same methodology, definitions, and depth, which is what makes them defensible in front of a client, a committee, or an auditor.

Quant is built around the segregation an advisory practice requires. Each client's book is kept strictly its own: consolidated across that client's custodians and accounts, analysed in isolation, and never mixed with another client's data in any view, report, or export unless the advisor is explicitly working at the practice-wide level. Data is encrypted, never sold, and never pooled into any other product.

Account access is read-only by design: Quant reads positions and transactions in order to consolidate and analyse them, and it can never move money or place a trade in any client account. Connecting a custodian grants visibility, not control, which keeps the software outside the chain of custody entirely.

Rather than locking client books inside a walled platform, Quant lets the advisor query their own data through their own AI assistant over the Model Context Protocol (MCP): a model the practice controls, on data that stays within the practice's control, with every reported number traceable to its source holdings.

AI helps advisors most where the work is retrieval and assembly rather than judgement: pulling a client's real exposure before a meeting, checking which books hold a name that just moved, summarising what changed in a portfolio since the last review, or drafting the commentary that accompanies a report. Those tasks consume hours across a practice, and they are exactly what a language model connected to consolidated portfolio data does well. The judgement, and the advice itself, stays with the advisor.

Quant makes this concrete through the Model Context Protocol (MCP): the practice's consolidated, looked-through data is exposed to the advisor's own AI assistant, which can then answer plain-English questions across one client or every book at once, like "what's this household's real US tech exposure?", "which clients hold this fund?", or "how did the model portfolios do against benchmark this quarter?". Because the AI reads from the same governed, reconciled data as every report, its answers carry the same numbers the client will see in their pack.

What makes this workable for a client-facing practice is control: the assistant is the advisor's own, the data stays within the practice, and nothing is pooled or shared. AI becomes a faster way to work the book the practice already owns, not a third party the client data is handed to.