Conquest Planning

The strategy engine is the whole product, and by the company's own account it is not inference. It has been the product since founding in 2018 rather than an addition to a platform that worked without it; remove the strategy engine and no product remains. The company consistently describes it as an expert system and as an auditable, deterministic calculation engine.

Its stated differentiator is precisely that it is rules based and reproducible rather than probabilistic, and encoded human expertise executed deterministically is not inference. Inferential features exist around it, including natural language interaction and generated content, but they are the newer layer rather than the substance.

Supervision is the product's stated selling point rather than a paragraph on a governance page, and three published capabilities back it. Every recommendation is stated to be traceable, and the vendor puts the point sharply: a firm never has to tell an auditor the answer came from the model. A compliance trail is described as built into the expert system rather than layered over it.

And firm leadership receives real time analytics on plan quality, adviser activity and client engagement, which is supervision at the book level rather than the individual output level. Determinism reinforces all three, because a reproducible engine can be re run to show how a recommendation was reached. The question stays live in the self directed channel, where a household receives planning with no human involved at all. There, reproducibility substitutes for review rather than accompanying it.

An architectural answer to model risk is published and no measurement accompanies it. The engine is described as deterministic and auditable, which if accurate removes rather than mitigates a whole class of risk. A reproducible system returns the same recommendation from the same inputs every time and can be re run to show how it got there. Two limits matter. The claim is made in marketing language, with no technical account of what a traceable recommendation actually shows a reviewer.

And the newer generative capabilities layered around the deterministic core, including natural language interaction and generated content, are not deterministic, and nothing published addresses their behavior at all.

Three separate kinds of evidence are published. Named institutional customers come first. Major banks in two countries and a life insurer are identified by name, as are a clearing business, a national brokerage and a digital wealth manager.

There is also a quantified business outcome at customer firms: onboarding of households bringing more than $1 million in investable assets is reported rising from 12 percent to 26 percent, an outcome measured on the customer's business rather than on time saved. Third, several institutions using the platform have also invested in the company, which puts independent money at stake.

They include a major bank, a global custody business, a large retirement provider and a global asset manager's venture arm. A named adviser at a named firm is quoted, more than two million plans have been created, and the company appeared on a global fintech ranking in 2026.

Nothing is published, in either direction, on whether household financial profiles, plans or interactions are retained, used to improve the engine, or used to train, tune or evaluate any model. Two million plans built across three countries is a substantial body of real household financial data. The institutional deployment model raises a question no single tenant vendor faces: whether anything learned from the customers of one bank can inform behavior seen by the customers of another. For an engine described as deterministic the answer may well be that nothing is learned at all. That would be a clean and easily stated position, and it is not stated.

There is no public retention schedule, deletion commitment or set of processing terms, and no account of how responsibility for customer data divides between the vendor and a deploying institution. The question is structurally more complex here than for a vendor selling directly to advice firms. When the engine sits embedded inside a bank's own digital estate, a customer may never know a third party processed their financial profile at all. Nothing published describes how that relationship is disclosed or governed. A privacy policy may cover some of this and is not reflected here.

Published security credentials very probably exist, but none are reflected in this profile: no attestation, information security standard, audit report or dedicated trust page. The gap is almost certainly in this profile's coverage rather than in the vendor. A platform deployed inside major retail banks, a life insurer and a clearing business has by definition passed those institutions' vendor security reviews, which are among the most demanding in any industry.

A software supplier holds no license and requires none, and no penalty attaches. The position deserves care for two reasons. The vendor engages with its customers' obligations deliberately, marketing an auditable trail and the ability to prove plan quality to regulators. And one delivery mode raises a question the others do not.

In the fully self directed channel a household receives suggested strategies with no adviser involved, and whichever institution deploys that channel owns the regulatory characterization of what the customer just received. Nothing published says where the vendor considers the boundary between planning output and regulated advice to sit.

The vendor's own ambition creates the exposure. No fairness evaluation is published, no testing of recommendations across household types, and no account of how strategies are ranked. A single engine is stated to deliver the same planning depth from a mass market saver to an ultra high net worth household, and in the self directed channel it recommends strategies to people with no adviser to sense that something is off.

If the strategy ranking suits some household shapes better than others, the effect lands hardest on exactly the customers reached through the channel designed to widen access. And it lands at the scale of a thousand institutions rather than one advice firm.

No warranty, accuracy commitment or correction obligation is published, and nothing allocates responsibility between the vendor and a deploying institution. The traceability the vendor markets is a defense for the institution rather than a remedy for the household. Showing an auditor how a recommendation was reached establishes that a process was followed and says nothing about whether the outcome was right. The gap matters most in the self directed channel.

A customer who acts on a suggested strategy without an adviser has no professional standing between them and the engine. Nothing published describes what recourse exists, who carries it, or how a person would even raise that a recommendation was wrong for them.

Neither the company material, the funding coverage nor the trade press names a model provider, model family, version or inference host behind the natural language interaction and generated content features. The deterministic core is arguably self disclosing. A rules based expert system built in house has no external model dependency to name, and the vendor says as much by describing what it built. That leaves the newer layer entirely unaccounted for.

A platform that now offers conversational interaction and dynamic content generation is calling something to do it, and nothing published says what. That silence is the more consequential one, because it sits beside such a clear account of the part that is not modeled.

The architecture is built to be embedded rather than connected to, the deeper of the two commitments. An open two way interface lets an institution construct its own client and adviser experience over the planning engine while the engine continues to do the reasoning. The product can sit invisibly inside a bank's own digital estate rather than appearing as a separate application.

Data gathering is designed to draw on what the institution already knows about a customer before asking them anything. The installed base is the evidence that this works: more than a thousand financial institutions, including organizations whose integration and procurement requirements are as demanding as any in financial services. An enumerated integrations directory, if one exists, is not reflected here, so the account stays architectural rather than a list of named systems.

Residency is a live question for this vendor rather than a formality. The company is headquartered in Canada, has most of the Canadian adviser market, and operates in the United States and the United Kingdom. Those three jurisdictions differ materially in where customer financial data may sit, and Canadian institutions in particular frequently require domestic residency. No hosting region or residency commitment is public, and neither is a per market data location statement or any account of deployment topology. A security or architecture page may say more.

No rate, tier or per adviser figure is published, and no account of how enterprise licensing is structured. The model is enterprise sales to institutions, where an absent public price is the norm rather than an evasion, since a thousand seat bank deployment and a small advice firm are not comparable purchases. That may change. The company announced a direct channel for independent advisers in 2026, and a self serve channel of that kind usually arrives with published pricing where the enterprise business never did.

Coverage is broad along four independent dimensions. Named users include major retail banks in two countries and a life insurer, plus a clearing and custody business, a national brokerage and a digital wealth manager, which is a different buyer set rather than a size range within one. The platform serves three national markets with different tax systems and retirement regimes, and has a substantial majority of the adviser market in its home country.

The same platform is stated to deliver equivalent planning depth from a mass market saver to an ultra high net worth household. And one engine runs adviser led, hybrid and fully self directed delivery. More than 60,000 advisers across over 1,000 institutions is the scale evidence behind it.