How institutional investors evaluate a token's liquidity before they invest.

Founders preparing a raise tend to rehearse the entry story: the mechanism, the roadmap, the team, the total addressable market, the comparison to whichever precedent token performed well in the last cycle. Investment committees at institutional funds read that material, but the question that actually determines whether capital moves is different and comes later in the process. It is simply this: assuming the position performs as modelled, how does the fund convert it back into a base currency without moving the price against itself in the process. That question is a liquidity question, not a narrative question, and it is asked of every allocation above a threshold size regardless of sector.

This ordering surprises founders who have spent months on tokenomics decks built around emission curves, utility loops and community distribution, because none of that material speaks to exit mechanics directly. A fund with a mandate to deploy in digital assets is nonetheless still a fiduciary vehicle answerable to its own limited partners, and its own limited partners will ask, at the point of redemption or reporting, how a position was valued and whether that valuation could actually have been realised in the market on the day it was marked. That accountability chain runs backwards through the fund and lands, uncomfortably for founders, on the depth of the specific token they are asking the fund to buy.

The practical effect is that liquidity due diligence now happens earlier in the process than most founders expect, often before term sheet discussions rather than after, because a fund that cannot construct a credible exit path will not spend further partner time on the opportunity. Founders who treat liquidity as an operational matter to be handled after the round closes, using proceeds from the raise itself, have the causality backwards. The liquidity structure that exists at the point of the raise is itself an input to whether the raise happens and on what terms, which is why this document treats liquidity as a fundraising instrument rather than a downstream administrative task.

It is worth being precise about what allocators mean by exit, because the term covers several distinct scenarios that are underwritten differently. There is orderly exit, where the fund sells down a position over weeks in line with a pre-agreed schedule and normal market conditions persist throughout. There is stressed exit, where the fund needs to reduce or close a position inside a short window because of a redemption request, a risk limit breach, or a change in the token's own fundamentals. There is also partial exit, where a fund trims a position to rebalance without fully closing it. Each of these draws on a different part of the liquidity profile, and a serious data room addresses all three rather than only the first.

Founders sometimes respond to this by pointing to committed lock-ups: if the fund cannot sell for eighteen months, why does current depth matter today. The answer is that lock-ups shift the timing of the exit question without removing it, and in most cases they raise the stakes rather than lowering them, because the token's liquidity profile has to be modelled forward to the unlock date rather than assessed at the point of investment, under assumptions about float growth, competing unlock supply from other holders, and market conditions that are inherently less certain the further out the horizon runs. A fund that is comfortable with a two-year lock will often ask harder liquidity questions than one buying into an unlocked position, not softer ones.

The consequence for founders is a change in sequencing. Liquidity structure, venue selection, market maker mandates and float modelling need to exist, in a form a fund can read and verify, before the raise is pitched, not as a promise to be delivered once proceeds land. A fund reading a data room that says liquidity will be arranged after the close is reading a document that has already answered its own exit question, and the answer is unfavourable. This is general information, not investment, legal or tax advice, and any specific liquidity or fundraising strategy should be tested against the relevant jurisdictional and regulatory framework before it is relied upon.

A common founder assumption is that a token with a market capitalisation of several hundred million units of currency should comfortably absorb an allocation in the low single-digit millions, on the logic that the allocation represents a small percentage of the total. Institutional risk desks do not size positions this way, because market capitalisation is a function of the last traded price multiplied by circulating supply, and it says nothing about how much of that supply can actually be sold, over what period, before the price the calculation relies on stops being representative. Sizing against market capitalisation is a rookie error that experienced allocators specifically screen for and treat as a signal about the counterparty's own sophistication.

The metric that actually governs position size is absorption capacity, which is an estimate of how much size the market can take on a given side, over a given time horizon, without moving the mid-price beyond an acceptable tolerance. This is derived from measured order book depth and historical trade impact rather than from supply and price alone. A token can carry a market capitalisation in the hundreds of millions while its true absorption capacity, measured honestly, supports selling only a small fraction of a typical institutional ticket size per day without material slippage, and funds size against the latter number, not the former.

Absorption capacity is calculated per venue and then aggregated, because liquidity does not pool automatically across exchanges; a token listed on several venues may show a healthy combined order book while any single venue, taken alone, could not absorb the position without severe impact, and a fund selling in a stressed scenario is realistically constrained to the venues where it can actually route flow and settle within its operational and compliance perimeter. This is one reason venue-by-venue analysis, not a single blended figure, is the standard institutional approach, and why founders who only ever look at combined market-wide volume figures are working with a metric their counterparty will not use.

Time horizon materially changes the answer. A position that can be exited over thirty trading days without appreciable impact may be entirely unsuitable for exit over three days, and funds explicitly model both a base case and a stressed case with a compressed timeline, because mandates, redemption terms and internal risk limits can force a compressed timeline regardless of what would otherwise be optimal. A liquidity profile that only works under the leisurely base case is, in institutional terms, an unhedged tail risk, and it is priced or declined accordingly rather than accepted on the strength of the base case alone.

Founders can influence their own position sizing outcomes by understanding this mechanism rather than contesting it. A token with modest but well-measured absorption capacity, clearly documented and verifiable, will often receive a larger and better-priced allocation than a token with an impressive headline market capitalisation and an unmeasured or opaque liquidity profile, because the fund's underwriting confidence rises with data quality independently of the raw numbers. Founders who commission an independent depth and absorption study before a raise, and put it in the data room, are effectively doing the fund's own risk desk's job for it, which speeds the process and tends to improve the terms offered.

It follows that the founder-facing question is not how do we make our market capitalisation look larger, which is the wrong lever entirely, but how do we increase measured absorption capacity at the venues and depths a fund will actually check. That is a structural question about resting liquidity, market maker mandates and venue selection, addressed later in this document, and it is answerable months before a raise if the work starts early enough. Founders who leave this until the term sheet stage are negotiating from a position that could have been strengthened well in advance at comparatively low cost.

When a founder is asked for liquidity data, the instinct is often to send a screenshot of the order book at a moment when it happens to look healthy, or a link to a data aggregator's depth chart. Institutional counterparties treat this as close to useless, because the number that matters is not depth at an instant but depth sustained over time, and the two can differ enormously for a token whose liquidity is thin and easily manipulated around specific moments such as reporting dates or influencer-driven attention spikes. A single observation tells a risk desk almost nothing about what will be available when they actually need to trade.

The standard institutional approach measures resting order size within defined basis-point bands from the mid-price, typically at bands such as fifty, one hundred and two hundred basis points from mid, sampled repeatedly across a trading period rather than once, and reported as a distribution rather than a single figure. This shows not just how much size sits close to the touch on a good day, but how that figure behaves across a full trading cycle including quieter periods, weekends, and times when whoever is providing resting liquidity might reasonably be expected to step back. A depth figure that only holds during active market maker hours understates the risk a fund actually carries.

This measurement has to be repeated across every venue where the token trades with meaningful volume, and it has to be repeated for both sides of the book, because a token can show healthy bid-side depth while offer-side depth, which matters less to a fund buying but a great deal to a fund building a short-term hedge or unwinding a long, is materially thinner. Bid-ask asymmetry of this kind is common in tokens where liquidity provision is subsidised or mandated on one side only, and a fund that only checks the side relevant to its entry will be blindsided at exit if nobody checked the other side in advance.

Spread behaviour is measured alongside depth and is treated as a distinct signal, because a tight spread with thin depth behind it is a different risk profile from a wider spread with substantial depth behind it, and the two are frequently confused by less experienced counterparties. What a risk desk actually wants to see is spread stability across time and across order sizes, meaning the spread a market order of a realistic institutional size would actually pay, not the quoted spread on a one-unit order that most retail-facing dashboards display by default and that has no relationship to the cost a fund would face.

Quote uptime, meaning the proportion of trading time during which resting liquidity within the measured bands is actually present rather than absent, is a further distinct metric and one that is frequently overlooked by founders because it requires continuous monitoring rather than a point-in-time check. A market maker mandate that produces excellent depth for twenty hours a day but disappears for the remaining four, often overnight in a particular time zone or during known low-volume windows, creates a liquidity gap that a fund operating across time zones or needing to execute urgently will eventually be exposed to, and institutional monitoring is specifically designed to catch this pattern.

The output a fund actually wants to see, and the output founders should be commissioning independently rather than waiting to be asked for, is a time series covering several weeks at minimum, broken down by venue, by side, by basis-point band and by time of day, with quote uptime reported separately. This is a materially more demanding deliverable than a screenshot or an aggregator link, but it is also the specific artefact that converts a vague liquidity conversation into a documented, defensible answer that a fund's own risk committee can sign off against, which is precisely the leverage a founder wants going into a negotiation.

Producing this measurement independently, ahead of any specific fund's request, also changes the tenor of diligence conversations. A founder who arrives with a multi-week depth and spread study already in hand is signalling operational maturity that extends beyond the liquidity question itself, and funds routinely read that signal into their broader assessment of the team's execution capability, which can influence outcomes on matters entirely separate from liquidity, including governance and reporting expectations post-investment.

Trading volume is the single most misleading metric in a token's public data profile, because it is the easiest figure to inflate, the figure most aggregators surface most prominently, and the figure founders are most often advised by less careful counsel to optimise directly. A high twenty-four-hour volume figure feels like evidence of a healthy market, and it is frequently presented that way in pitch materials, but volume measures how much has changed hands, not how much size could change hands without moving price, and the two can be almost entirely unrelated for a token with wash-like trading activity or volume concentrated in trades too small to be relevant to an institutional ticket.

Funds with any degree of sophistication run their own volume-quality checks before taking a headline number at face value. These checks typically look at trade size distribution, since genuine two-sided institutional liquidity produces a distribution with a meaningful tail of larger trades, whereas wash-like or incentivised volume tends to cluster around uniform, small trade sizes repeated at high frequency. They also look at the relationship between reported volume and measured order book depth, because real volume executing against real depth leaves a visible fingerprint in the book, in the form of depth being consumed and replenished, that purely artificial volume does not produce.

The disqualifying effect of detected wash-like activity is worth stating plainly, because founders sometimes assume the worst outcome is that the fund discounts the volume figure and moves on. In practice, once a fund's diligence process flags volume inflation, the effect is broader and more damaging than a discount, because it recasts every other number in the data room as potentially unreliable and shifts the entire diligence posture from verification to suspicion. A fund does not selectively distrust one metric while trusting the rest; it reasonably asks what else has been shaped to look better than it is, and that question can end a process that was otherwise progressing well.

This dynamic means that inflating volume, whether through direct wash trading, incentivised low-quality volume programmes, or simply failing to question a market maker's own reporting, is close to strictly negative expected value for a founder raising institutionally, even though it may improve superficial rankings on public aggregators that retail-facing audiences still respond to. The audiences are different and the metric that serves one actively damages standing with the other, which is precisely why liquidity strategy for an institutional raise has to be designed separately from, and sometimes in tension with, liquidity strategy aimed at retail visibility.

A related and subtler problem arises with market maker arrangements that are not overtly manipulative but that generate volume as a side effect of an aggressive quoting strategy designed to satisfy a volume-based key performance indicator in the mandate itself. If a market maker is paid or incentivised against a volume target rather than against depth, spread and uptime targets, the resulting activity can technically be genuine two-sided trading while still not producing the resting depth a fund actually needs, because the mandate was never designed to produce that outcome in the first place. Mandate design, covered later in this document, is where this gets fixed structurally rather than through vigilance alone.

The founder-facing takeaway is to treat every volume figure in outward-facing materials with the assumption that a sophisticated counterparty will independently verify it, and to prefer being conservative and transparent about volume quality over presenting an inflated headline that invites a harder look. A data room section that proactively addresses volume quality, including an honest breakdown of trade size distribution and a statement of how volume was generated, tends to build more institutional confidence than a section that simply cites a large aggregate number and hopes it goes unquestioned.

Once a fund has decided a token is otherwise investable, its risk or portfolio construction function typically builds an explicit exit model rather than relying on the qualitative liquidity impression formed during diligence. This model takes measured average daily volume and depth figures as inputs and produces an estimate of days-to-liquidate for a position of a given size, along with an expected slippage curve describing how much price impact should be expected as the position is worked down, under stated participation-rate assumptions such as never exceeding a fixed percentage of measured average daily volume on any given day.

Participation-rate assumptions are a core input and one founders rarely see directly, because they sit inside the fund's internal risk framework rather than in any document shared externally. A conservative participation rate, common for funds with fiduciary reporting obligations, might assume no more than a low single-digit percentage of measured daily volume can be sold on any one day without materially affecting the achieved price, which for a thinly traded token can extend the modelled exit horizon to weeks even for a moderate position size, materially changing the fund's own liquidity classification of the investment.

The slippage curve that emerges from this modelling is typically non-linear, meaning the marginal cost of selling each additional increment of size rises rather than staying constant, and for thin markets this curve can steepen sharply well before the full position is liquidated. Funds translate this curve into an expected average execution price across the full unwind, and the gap between that expected average price and the current quoted price is the liquidity cost the fund effectively prices into the investment before it is made, independently of any view on the token's fundamental prospects.

This liquidity cost does not stay inside a spreadsheet; it is typically expressed to the founder, if at all, only indirectly, through a lower headline valuation, a request for a larger discount to the round price, additional protective terms such as pro-rata rights specifically justified by exit constraints, or a reduced allocation size relative to what the fund would otherwise have deployed. Founders who do not understand this mechanism sometimes experience it as an arbitrary or unexplained toughening of terms during negotiation, when in fact it is the direct, mechanical output of the liquidity model working exactly as designed.

Because the model is mechanical, its inputs can be improved deliberately, and improving them ahead of a raise is one of the highest-leverage actions a founder can take. Widening measured depth at tighter basis-point bands, improving quote uptime, and diversifying liquidity across additional credible venues all flatten the slippage curve and shorten the modelled days-to-liquidate for the same position size, which flows directly through to a smaller liquidity discount and, in practice, better terms, without requiring any change to the token's underlying fundamentals or narrative.

Founders preparing for a raise should therefore ask, before term sheet discussions begin, what days-to-liquidate and slippage figures a sophisticated counterparty would independently derive from current public data, using conservative participation-rate assumptions, and should treat any unfavourable answer as a fixable pre-raise workstream rather than an unavoidable feature of the token's size or category. A founder who has already run this exercise, and improved the inputs where practical, arrives at term sheet negotiation with materially less room for the fund to justify a large, unexplained liquidity discount.

It is worth adding that no adviser, including Xavion, can guarantee that a given depth or slippage improvement will translate into a specific valuation outcome, since every fund applies its own internal risk framework and every negotiation involves factors beyond liquidity. What can be said with confidence is that unmeasured, unaddressed liquidity risk is priced conservatively by default, whereas measured, well-structured liquidity gives a fund the basis to price more favourably, and that difference is squarely within a founder's control before a raise begins.

A token's circulating supply on the day of a raise is rarely the supply a fund is actually underwriting, because vesting schedules for team, investor and treasury allocations mean the float will expand at defined points over the following months and years, and a fund's exit horizon frequently extends past one or more of those points. Institutional diligence therefore does not stop at current depth; it explicitly models forward, overlaying the unlock calendar against a projected absorption capacity at each future date, and asks whether the market as it is likely to exist at each unlock can absorb the additional supply without depressing price beyond a tolerable level.

Cliff-based vesting, where a large tranche unlocks in a single event after a defined period rather than unlocking gradually, is scrutinised particularly closely, because a cliff concentrates supply risk into a narrow window in a way that gradual linear unlocks do not. A fund modelling an exit that happens to fall near a known cliff date has to assume both its own selling and the unlocking cohort's potential selling are competing for the same absorption capacity at the same time, which materially worsens the slippage curve for that specific window relative to a smoother schedule, even if the total unlocked amount over the full vesting period is identical.

Founders sometimes argue that unlocking parties are typically long-term aligned holders unlikely to sell immediately, and this may often be true, but institutional risk modelling generally cannot rely on an assumption about another party's future behaviour that it cannot verify or enforce, particularly for team and early investor allocations where individual liquidity needs are opaque to an outside fund. In the absence of binding, verifiable commitments beyond the contractual vesting schedule itself, prudent modelling assumes a portion of newly unlocked supply becomes available to the market and sizes the exit plan accordingly, which is a materially more conservative assumption than founders often expect their own token to be judged against.

This is one area where founders can meaningfully change the outcome through structuring rather than through liquidity operations alone. Smoothing a cliff into a linear or stepped unlock, extending vesting duration for larger allocations, or introducing lock-up extensions tied to measured market conditions rather than fixed calendar dates, all reduce the peak supply-versus-absorption mismatch a fund has to model, and funds notice and price this favourably when it is presented as a deliberate design choice rather than discovered independently during diligence.

Absorption capacity itself is not static and should not be modelled as a single fixed number projected flat into the future; it typically grows over time as a token matures, gains additional venue listings, deepens its market maker relationships and builds a longer trading history, but it can also shrink if a market maker mandate lapses, if a major venue delists the token, or if broader market conditions deteriorate. A credible forward model therefore presents a range rather than a point estimate, and explains the assumptions behind both the optimistic and conservative cases, which funds find considerably more trustworthy than an unqualified single projection.

The practical deliverable founders should build ahead of a raise is a combined unlock-and-absorption model that plots the full vesting calendar for every allocation category against a projected absorption capacity range at each material date, flags any dates where projected unlock supply could plausibly exceed a defined percentage of projected absorption capacity, and sets out what mitigating structure, whether smoothing, staggering or additional market maker commitment, addresses each flagged date. This model, built and owned by the founder rather than reverse-engineered by the fund during diligence, is one of the more persuasive single documents in an institutional data room.

It should be stated clearly that no forward model of this kind can guarantee future market behaviour, holder conduct, or price outcomes, and any model presented to investors should say so explicitly rather than implying certainty it cannot deliver; the value of the exercise lies in demonstrating that the risk has been identified, quantified under stated and reasonable assumptions, and structurally addressed where possible, not in producing a forecast that will necessarily prove accurate.

Founders frequently treat the appointment of a market maker as a box that has been ticked once a contract is signed, and mention the arrangement in fundraising materials as a single line confirming that a market maker is in place. Institutional diligence goes considerably further, because the terms of that specific arrangement determine whether it produces the liquidity a fund needs or merely produces the appearance of liquidity for a period, and funds with experience in this asset class specifically request and review the mandate itself rather than accepting the fact of its existence as sufficient.

The most common structure funds scrutinise closely is the token loan combined with a call option or warrant, under which a market maker borrows tokens to provide liquidity and receives the right to purchase additional tokens later at a favourable price, often with no explicit, measurable obligation to maintain any particular depth, spread or uptime in exchange. This structure aligns the market maker's economic interest with the token's price appreciation over the option period far more than with the quality of liquidity provided day to day, and a market maker under this structure has limited incentive to maintain deep, tight, reliable markets once the option value has been secured or once conditions turn unfavourable for continued quoting.

Funds specifically ask whether the mandate includes measurable, binding key performance indicators, typically covering minimum resting depth within stated basis-point bands, maximum spread under stated conditions, and minimum quote uptime, together with a monitoring mechanism and defined consequences for breach, which might include fee reduction, mandate termination rights, or replacement provisions. A mandate without any of these elements, however well the market maker is regarded reputationally, is treated as unenforced and therefore unreliable, because reputation is not a contractual remedy and cannot be relied upon in a stressed scenario.

Independence of monitoring is a further point of scrutiny that founders sometimes overlook. If the only party assessing whether the market maker is meeting its obligations is the market maker itself, through self-reported statistics, a fund has no independent verification and will typically discount the arrangement's credibility accordingly. Independent, third-party monitoring of the actual KPIs, using the same depth, spread and uptime measurement methodology described earlier in this document, converts the mandate from a private arrangement between the founder and the market maker into a verifiable input a fund can rely on in its own modelling.

Notice periods and contingency planning are also examined, because a market maker relationship that can be terminated by either party on short notice, with no contingency market maker or transition plan in place, exposes the token to an abrupt liquidity gap if the relationship ends for any reason, whether commercial, reputational or operational on the market maker's side. Funds ask what happens to depth, spread and uptime in the days immediately following a hypothetical termination, and a founder who has no answer, because no contingency exists, is signalling a fragility that a well-prepared founder with a documented contingency plan does not carry.

Concentration risk within the market making arrangement itself compounds these concerns when a token relies on a single market maker across all venues, since any disruption to that single relationship then affects the entire liquidity profile simultaneously rather than only a portion of it. Funds generally view a structure with more than one credible liquidity provider, or at minimum a documented and tested contingency provider, more favourably than a single-provider structure, even where the single provider is well regarded, because the diligence question is about structural resilience rather than about the quality of any one relationship in isolation.

The founder-facing conclusion is that mandate design deserves the same rigour as legal and financial terms elsewhere in a company's structuring, because a fund's confidence in a token's forward liquidity is, in large part, confidence in this specific contract and its enforcement mechanism rather than in the market maker's general reputation. A properly designed mandate, with measurable KPIs, independent monitoring, defined remedies and a contingency plan, converted into a clear summary for the data room, addresses one of the most commonly raised objections in institutional diligence before it is even raised.

Spot depth is only one component of the liquidity picture a sophisticated fund constructs, and for funds that use hedging as part of their risk management, the availability and quality of derivatives markets referencing the token can be as important as the spot order book itself. A fund that can hedge directional exposure through a liquid perpetual futures contract or an options market is able to hold a larger spot position with a materially smaller net risk footprint than a fund with no hedging avenue, and this changes the position size the fund is willing to take at the outset of a raise, not only its behaviour at exit.

Diligence on derivatives markets covers similar ground to spot diligence: open interest, funding rate stability, depth in the derivatives order book measured at defined bands, and the number of credible venues offering the instrument, since a derivatives market concentrated on a single, thinly regulated venue is of limited use to a fund with counterparty and venue-risk restrictions in its own mandate. A token with no derivatives market at all is not automatically disqualified, but it removes an entire risk management tool from the fund's toolkit and typically results in a smaller position size than an otherwise comparable token with liquid, well-distributed derivatives coverage.

Borrow availability, meaning whether the token can be borrowed for short-selling or for constructing hedged positions through established lending venues, is a related and often overlooked factor. Its absence is not usually treated as a critical flaw for a straightforward long-only allocation, but funds that would otherwise consider more sophisticated structures, including basis trades or hedged entry strategies that can support a larger overall commitment, are constrained when no borrow market exists, again narrowing the practical size and structure of the investment the fund is prepared to offer.

Fiat and stablecoin off-ramp considerations sit at the far end of the liquidity chain and are frequently underweighted by founders relative to their actual importance. A fund's exit is not complete when a token position is converted into a stablecoin; it is complete when the fund can convert that stablecoin, at scale and without material friction, into the fiat currency it ultimately needs to report and distribute in. A token trading only against a single, thinly liquid stablecoin pair, on venues with limited fiat on and off-ramp infrastructure, introduces a second-stage liquidity bottleneck that a headline spot depth figure does not capture at all.

This second-stage bottleneck matters disproportionately for funds operating within regulated banking relationships that impose their own scrutiny on the source and route of incoming funds, since a stablecoin that has passed through a venue or counterparty outside the fund's approved perimeter can create a compliance obstacle entirely separate from any market liquidity constraint. Founders sometimes assume that once a position is converted to a widely used stablecoin the liquidity question is solved, when in practice the fund's own banking and compliance architecture can reintroduce friction at exactly this stage if it has not been considered in advance.

The practical response for founders is to ensure the token trades against more than one major stablecoin pair on more than one credible, well-regulated venue, and to be able to speak specifically to the fiat off-ramp infrastructure available at those venues, including any known limitations by jurisdiction or transaction size, since institutional counterparties will differ in which banking rails and venues sit within their own approved perimeter. This is an area where general market data is less useful than direct, specific answers, and founders who can provide those specific answers when asked demonstrate a level of operational grip that materially shortens diligence timelines.

Liquidity diligence does not stop at the token's trading markets; institutional counterparties also examine the issuing entity's own treasury, because a fund is effectively underwriting the balance sheet standing behind the token as well as the market the token trades in, and a treasury holding a large proportion of its reserves in its own token, without an independent runway in a stable, liquid asset, introduces a reflexive risk that a purely market-side liquidity study would miss. If the entity's own operating runway depends on selling its own token into the market it is simultaneously trying to keep orderly for investors, that dependency itself becomes a liquidity risk.

Funds specifically ask what proportion of the treasury is held outside the native token, in what form, under what custody and banking arrangement, and over what period that reserve is expected to cover operating expenses without requiring token sales. A treasury with a short runway in stable, liquid assets and a heavy reliance on future token sales to fund ongoing operations is read as a structural pressure that could force sales into the market at exactly the times, such as periods of weak price performance, when the market is least able to absorb them without material impact, compounding rather than diversifying the fund's own exit risk.

Treasury diversification therefore functions as a liquidity signal in its own right, separate from any measurement of the market itself, and founders who can point to a treasury policy that separates operating runway from token-denominated reserves, held with appropriate banking relationships and with a documented policy for any planned token sales, including notice and sizing constraints designed to minimise market impact, present a materially stronger liquidity case than founders who have not considered this dimension at all. This is also an area where formal treasury banking architecture, arranged in advance through established institutional relationships, demonstrably reduces the perceived risk relative to an informal or ad hoc treasury setup.

Governance over treasury decisions is examined alongside the treasury's composition, because a fund wants assurance that decisions to sell treasury-held tokens into the market are subject to some form of process, whether board approval, a documented policy, or pre-agreed sale windows, rather than being at the unilateral discretion of a single individual who could reasonably respond to short-term pressure by selling into thin markets without regard to the impact on other holders, including the fund itself. Documented governance around treasury sales is a comparatively low-cost structural fix that materially improves how this risk is perceived.

OTC execution capability is a further treasury-adjacent consideration, since a treasury that needs to move meaningful size, whether to fund operations, to rebalance reserves, or to execute a planned sale under its own policy, benefits from access to over-the-counter execution channels that can transact large blocks away from the public order book, reducing the market impact that routing the same size through the visible order book would cause. A treasury with established OTC relationships alongside its exchange listings is demonstrably better equipped to manage its own liquidity needs without disturbing the market the fund is relying on, and funds view this capability as a meaningful positive during diligence.

Taken together, these treasury and governance considerations mean that a founder preparing for an institutional raise should treat treasury structuring, banking relationships and sale governance as part of the same liquidity workstream as market depth and market maker mandates, rather than as a separate finance function unrelated to the fundraising conversation. Funds increasingly draw this connection explicitly during diligence, and founders who have already drawn it themselves, with documentation to show for it, tend to move through this stage of diligence considerably faster.

Most of the diligence areas covered in this document culminate in a single practical recommendation, which is that founders should assemble a dedicated liquidity annex to their data room rather than scattering liquidity-relevant information across a market overview slide, a tokenomics appendix and answers given informally during calls. A liquidity annex is a self-contained section that a fund's risk or portfolio team can review largely on its own, without needing extensive follow-up meetings, and its existence signals that liquidity has been treated as seriously as legal, financial and technical due diligence, which it should be.

The core of the annex is the depth, spread and uptime study described earlier, covering every venue with meaningful volume, over a period of several weeks at minimum, broken down by basis-point band and time of day, alongside a clear statement of methodology so a fund can assess whether the measurement approach itself is sound rather than simply accepting the headline figures. Alongside this sits the volume-quality analysis, including trade size distribution, addressing directly and proactively whether reported volume reflects genuine two-sided activity, which pre-empts one of the most common and most damaging lines of institutional questioning before it is raised.

The annex should also contain the unlock-and-absorption model covering the full vesting calendar against projected absorption capacity, flagging any dates of concern and the mitigating structure attached to each; a summary of the market maker mandate or mandates, including the key performance indicators, monitoring approach, notice periods and contingency arrangements, without necessarily disclosing full commercial terms that may be confidential; and a treasury liquidity section covering runway, reserve composition, custody and banking arrangements, and governance over any treasury token sales.

A derivatives and off-ramp section, summarising which venues offer hedging instruments referencing the token, the depth and stability of those markets, and which stablecoin pairs and fiat off-ramps are available and at what venues, completes the market-facing content, and a short forward-looking section addressing what the founder is actively doing to improve liquidity over the following months, whether through additional venue listings, expanded market maker mandates, or treasury diversification, gives the fund confidence that liquidity is being actively managed rather than treated as a fixed, unchangeable feature of the token.

Presentation matters as much as content, because a fund's risk team reads a large number of these annexes across different opportunities and responds well to a consistent, professional format that matches the structure they use internally, typically tables and time series rather than narrative prose, clearly labelled methodology notes, and explicit statements of what has and has not been measured, rather than implying comprehensiveness that has not actually been achieved. An annex that overstates its own coverage and is later found to have gaps does more damage to credibility than an honestly scoped annex with acknowledged limitations.

Founders sometimes worry that assembling this annex before approaching funds delays the raise, but the more common outcome in practice is the reverse, because diligence teams that receive a complete, well-structured liquidity annex at the outset spend materially less time on liquidity-related follow-up requests during the process, and the process compresses accordingly. The annex is, in effect, a way of moving work that would otherwise happen reactively and under time pressure during diligence into a controlled, proactive exercise completed on the founder's own timeline before the raise begins in earnest.

Founders who begin liquidity preparation ninety days before a planned raise, rather than at the point term sheets are being discussed, have a realistic window to change the inputs that actually move a fund's underwriting. The first weeks of that window are typically spent on measurement rather than intervention, commissioning the independent depth, spread and volume-quality study described earlier so that the starting position is understood in the fund's own terms before any structural changes are made, since intervening without a clear baseline makes it impossible to demonstrate improvement credibly later in the process.

The middle portion of the window is where structural work happens, which can include renegotiating or redesigning a market maker mandate to introduce measurable KPIs and independent monitoring where none previously existed, adding a second credible venue or a second market maker relationship where the existing structure is dangerously concentrated, and reviewing the unlock calendar for any near-term cliff that projected absorption capacity could not credibly meet, addressing it through smoothing or through a documented lock-up extension if the fundamentals of the raise allow that conversation with early investors and the team.

The final weeks before outward fundraising conversations begin are typically spent assembling the liquidity annex itself, incorporating the improved measurement data that the intervening work has produced, and preparing the forward-looking narrative around continued liquidity investment that funds will ask about even once the current picture looks sound. This final assembly stage also includes a rehearsal of the specific questions a fund's risk desk is likely to ask, using the framework set out in this document, so that responses in live diligence meetings are consistent with the written annex rather than improvised.

It is important to be honest about the limits of a ninety-day programme, because some liquidity problems cannot be meaningfully fixed on that timeline regardless of budget or effort. A token with genuinely thin, structurally concentrated trading interest, a small and undiversified holder base, and no realistic prospect of additional venue listings in the near term, cannot manufacture credible depth in ninety days without resorting to the kind of artificial volume generation that, as discussed earlier, actively damages institutional credibility once detected, and founders in this position should not attempt to paper over the gap.

In these genuine cases, the more defensible course, though it is rarely the one founders want to hear, is to delay the institutional portion of the raise, use the intervening period to build organic trading interest, additional venue relationships and a longer track record, and approach institutional funds only once the underlying liquidity picture has had time to develop rather than be manufactured. A fund that discovers, during diligence, that a founder attempted to accelerate liquidity metrics artificially ahead of a raise will generally treat this discovery as disqualifying for that fund and often shares that assessment informally within the wider allocator community, which extends the damage well beyond the single lost opportunity.

The more constructive framing for founders facing this situation is that a delayed institutional raise, supported in the interim by a smaller round from investors with a longer time horizon and a lower liquidity threshold, is a legitimate and common sequencing choice rather than a failure, and it is considerably preferable to forcing an institutional conversation before the liquidity structure can support it credibly. No adviser can promise that any given ninety-day programme, or any longer preparation period, will result in a successful raise or in any particular fundraising outcome, and any programme should be scoped against the token's actual starting position rather than against a generic template.

Xavion Capital works with token founders and their finance teams from a starting position that treats liquidity as a fundraising instrument rather than an exchange listing exercise, and the engagement typically begins with a pre-raise liquidity diagnostic produced in the same format an investment committee reads internally, covering depth, spread and quote uptime measured across every relevant venue at defined basis-point bands over a multi-week period, so that the founder sees, in advance, substantially the same picture a fund's own risk desk will construct during diligence, rather than discovering it during a live process.

From that diagnostic, the engagement moves into mandate design and market maker selection where required, run as a structured request-for-proposal process across our network rather than a single-provider negotiation, with measurable key performance indicators covering depth, spread and uptime built into the resulting mandate from the outset and monitored on an ongoing, independent basis for the duration of the relationship, so that founders and their investors alike have verifiable data rather than self-reported claims from the market maker itself.

Alongside market structure work, we build unlock-schedule and float models that overlay a token's full vesting calendar against measured absorption capacity, flag concentration points where projected supply could outrun projected absorption, and set out concrete mitigating structures for each flagged date, delivered as a document a founder can present directly within a data room or adapt for board and investor discussions ahead of the raise itself.

This work is compiled into a liquidity annex built to institutional data room standards, combining the depth and spread study, the volume-quality analysis, the unlock-and-absorption model, the market maker mandate summary and the treasury liquidity section into a single, consistently formatted reference that a fund's diligence team can review largely without further follow-up, shortening the liquidity component of the diligence timeline considerably relative to a founder assembling equivalent material reactively during a live process.

Where the underlying issue is structural rather than purely a market-data question, our broader platform supports entity and issuer structuring across nineteen jurisdictions, so that the legal and tax framework around token issuance, treasury holding and investor onboarding is set up to withstand institutional scrutiny rather than being retrofitted after a term sheet is signed, and treasury banking together with over-the-counter execution architecture across a network of more than one hundred and twenty institutions, giving founders a documented, professional channel for treasury management and large-block execution rather than an ad hoc arrangement assembled under time pressure.

For founders whose liquidity strategy would benefit from additional or better-aligned trading venues, we make introductions through our Institutional Access Program, drawing on established exchange relationships to support venue conversations; every institution and venue in our network makes its own independent decision about listing, market structure participation or account approval, and Xavion does not and cannot guarantee that any specific venue, market maker, investor or price outcome will result from this or any other engagement, since those decisions rest entirely with the counterparties involved.

What we can commit to is the quality and independence of the diagnostic and structuring work itself: measurement methodology that would withstand scrutiny from an institutional risk desk, mandate design that gives founders and investors verifiable rather than self-reported liquidity data, and structuring and banking architecture built by a team with more than ten years in financial services operating across a compliance-first model. This is general information, not investment, legal or tax advice, and any engagement is scoped against the specific token, jurisdiction and raise in question rather than applied as a generic template; founders considering a raise are welcome to discuss where their current liquidity position stands against the framework set out in this document.