Audience: the full Precise team for the 2026-08-06 company discussion.
Meeting outcome: everyone leaves with one operating point of view for Precise, a clear role for the harness and eval system, a clear Konstant/Boombox boundary, and the first customer proof. Everyone knows what changes in daily product work and what remains unchanged.
Everyone here knows the founder story. I have spent months working out how Precise research, Konstant (Company Advocates, organizational intelligence, Boombox, the capability network), Blockdaemon, AliceNet, and customer-owned deployment fit into one AI-native operating model. The pieces are now coming back together.
Every team is part of it. That is why I continued financing Precise, kept building Konstant, aligned the companies, and kept Konstant independent. None of the pieces worked as well alone. Without Precise, Konstant would have stopped at organizational intelligence: context, but not the differentiated capability that gives the network gravity. Without Konstant and Boombox, Precise would remain powerful but hard to move across companies; without the network to think in, Precise would have ended up building dashboards. Without the capability network, the blockchain work would lack the agentic economy it was built to support.
The economic alignment is structural, more than participation: the network consumes the Precise primitive everywhere it runs, so as the network wins, Precise wins. That claim cannot be proven yet, so I backed it personally until the evidence lands. Private company records control the exact mechanics and remain outside this document. Each company owns a distinct part of the thesis, and each team's work strengthens the whole without becoming somebody else's work.
I have been thinking about versions of this model since high school: how independent people and systems coordinate, how contribution becomes legible, how value moves without one permanent owner, and how a network learns without flattening its participants. Much of the last ten years of blockchain work came from the same question. I built and financed pieces before I had the final diagram. In many ways, this is my life's work behind the scenes.
I made consequential decisions before I had language that made the whole system legible to everyone else. The language and software have caught up with the instinct.
AI-native does not mean adding a chatbot to the existing company. A true AI-native organization can represent its context—including its software, differentiated capabilities, operating knowledge, intent, authority, relationships, reason for existing, value propositions, evidence, and economic goals—in a form its people and Advocate agents can use. A Company Advocate is a Boombox-operated agent fueled by that authorized context and Konstant's selection and routing methods. It turns what the company knows, makes, and does into qualified primitives that authorized agents can discover, compose, and use in new contexts. When it considers a Precise capability, it uses Precise ablation, eval, contribution, and outcome evidence. It proposes, composes, and operates only within the customer's mandate; the mandate, never the agent or its score, is action authority. The company keeps its identity, data, authority, relationships, and economics throughout.
A true AI-native organizational network extends that operating state across companies. Its Boombox-operated Company Advocates and inter-company agents can use authorized shared reality, find the right independently owned capability, propose a relationship, and improve the next decision from inspected outcomes. People and company mandates authorize the relationship and every action class.
The breakthrough is that depth can now travel without turning one company into every vertical. Before capable agents, taking a deeply vertical workflow into a new industry meant another implementation team, another integration estate, another product translation, another sales motion, and another operating model. At Lookbooks, Ryan and I could see the same workflow abstractions across industries. Konstantin will remember the argument because we fought about it then. The pattern was reusable; a single company could not carry it economically into every market.
Now the agent can. A small expert company makes its software, method, and operating knowledge an addressable, qualified capability. An authorized agent understands a new company's context, composes that capability with local software and other provider capabilities, operates it through Boombox, and returns inspected evidence. Precise stays deep in decision learning instead of first becoming a billion-dollar horizontal company and then rebuilding itself one vertical at a time. The network carries the depth.
The layers have distinct jobs:
Precise is the first featured capability. It makes contribution reality legible, turns that understanding into an answerable bet, scores and decomposes what happens next under declared evidence law, and books only the learning the evidence supports. That is why I continued financing Precise and kept it going. Precise is more than a media application. It gives Company Advocates and inter-company agents a rigorous representation of reality from which they can evaluate, attribute value, and improve the capabilities that follow.
Taylor did far more than see the same importance. She saw early that Precise belonged inside a network and started applying its contribution, ablation, and evidence ideas to AI workflows. She recognized that system as a foundational primitive in any serious capability network and therefore as one of the most important assets Konstant can carry, distribute, and learn around. Her instinct showed why Konstant had to become more than organizational context and why Precise is essential to its value. That is how our partnership works: each of us changes what the other can see next. The partnership compounds the thinking, not only the execution.
The games work makes the full system visible. THE IRIS HOTEL (the game) and Doors (our application SDK) put the same customer workflow on a faster clock: connect permitted data, explain the current result, recommend or apply a reversible change, measure the outcome, and improve the next model or recommendation. It shows how a Precise capability enters a different product context, retains its decision and evidence meaning, and moves through the capability network without becoming the game. Precise is Boombox's first featured capability provider, design partner, and power user. My operating and economic alignment with Precise gives Precise first access to the network, direct influence over its shared contracts, a distribution path for every qualified Precise capability, and participation in the wider value the featured capability helps create.
Precise contributes two connected capability families. Decision Learning makes the whole workflow answerable: baseline result and contribution analysis, recommendation, actual action, new outcome, comparison, and the next authorized model or recommendation. Evidence-bound selection makes the candidate comparison answerable: Precise freezes the eligible models, methods, strategies, policies, and Surfaces, applies its eval and ablation law, floors, and research court, and returns a winner, null, or refusal. Konstant solves a different selection problem. It routes an organization to the capability, provider, and composition that fit its context, authority, relationships, custody, timing, and commercial terms. Neither selector silently acquires the other's authority.
Precise gives the network its first featured capability and proves the complete customer loop. Independently valuable capabilities, rigorous evals, authorized experience, and evidence make a network useful. The operating and evaluation records created through Boombox give Konstant a compounding capability and experience graph without requiring Konstant to own the capabilities it carries.
Konstant makes Precise portable, distributable, and compounding. Precise research, products, and local customer proofs run independently; Boombox is the shared production and network road from Precise infrastructure to separately admitted customer infrastructure and authorized downstream relationships. That path turns repeated bespoke integration into reusable network capability while preserving Precise's ability to keep customer work moving behind the same typed ports.
This is the answer:
One AI thesis. Distinct companies. One operating model. Precise is the first featured capability. Konstant builds the independent network that lets Precise and other great capabilities travel.
We no longer have to force the whole thesis into either company. Precise creates and owns its capabilities and customer products. Konstant creates and owns the independent network, shared ontology, operating infrastructure, and routing intelligence that let those capabilities work across companies. The private company instruments align Precise economically with the network value it helps create while preserving Konstant's independence; those instruments control the legal mechanics.
I can explain the system clearly now. The companies have a useful boundary and an executable way to work together. Now we run.
Precise is a research and product company becoming one coherent decision-learning capability company.
Precise has already created strong methods, models, analyses, applications, collectors, recommendation systems, contribution systems, and research machinery. The next step is not to replace that work. It is to connect it around one product unit: a consequential customer decision that starts from contribution analysis on both sides, carries its forward bet on top of that analysis, is served by the right qualified method and surface, operated under explicit authority, scored and decomposed after its evidence matures, and allowed to improve the next decision only when the evidence supports learning.
Precise research becomes a product system. It learns, deploys, and compounds.
The company-level promise becomes:
Precise builds each customer a private decision-learning product system, owned by that customer, in which every decision, refusal, and outcome becomes evidence for improving the customer and the capabilities serving them.
"Private" matters. Customer evidence, data, models, and organizational context stay inside the rights and custody the customer has granted. The network carries bounded capabilities, qualified releases, authorized results, recomputable claims, and payload-free receipts. It never creates an unrestricted pool of customer data.
As this gets bigger, one piece grows in importance faster than everything else: selection. It runs at two layers that split exactly along the company line. Konstant selects context, with its own selection algorithms and patents: what a decision gets to see, chosen to make the next bet more likely to be right. TikTok makes the primitive legible: its decisive act is selecting the next video from a vast candidate set using context and an objective. Konstant generalizes that act for organizations, selecting the next evidence, capability, provider, collaborator, composition, or experiment under the company's declared intent and authority. Precise selects the other half: the pieces that produce the planned outcome, the method, the move, the next dollar. Konstant chooses what the cycle can know. Precise chooses the product method and makes the bet answerable. The observed outcome and its decomposition improve both systems under their separate learning authority. Konstant consumes the Precise capability as a licensee.
Our thesis is that AI becomes a network of companies, people, agents, and independently owned capabilities. The scarce assets are context, evidence, authority, relationships, and the economic learning left behind after work.
Precise creates and proves decision capabilities and owns its products, customers, research, and judgment. Konstant (Company Advocates, organizational intelligence, Boombox, the capability network) builds the independent rails those capabilities travel on. AliceNet is the neutral protocol floor for records that must outlive any one vendor. The separation preserves the network as an independent product, not the captive platform of one capability provider. The rest shows up in how the work runs.
No serious independent capability provider joins a network owned by another capability provider. Konstant's neutrality is an admission requirement. The three current Precise pitches—Polymarket, Mediaocean, and iHeart—already depend on this shape: Precise leads with its product and customer relationship; Konstant supplies the portable deployment, customer-owned context, qualified capability path, and downstream network distribution that make the product compound beyond one installation.
Company A's agent uses Company B's capability. Later, both sides, and sometimes a third party neither controls, must prove things about what happened without anyone opening their books. The protocol keeps three questions distinct: authority (who was allowed to act), provenance (what was decided and frozen before the outcome), and contribution when the commercial relationship requires it (who created the value under which declared comparison). A hyperscaler answers by becoming the trusted middleman. That is the company town. The target proof stack answers with portable, independently checkable records:
The trust ladder runs from signed-local records to Merkle-committed exchange between contracted parties to chain-anchored proof for open-network and regulated work. The Precise/Konstant adapter binds Decision Learning episode records to that ladder, and the counterparty verifier checks the bounded commitment without opening the underlying evidence. ALICENET.md defines the proof boundary, and the AliceNet note for Matt defines the connection.
Precise's first commercial advantage is in large, legacy-heavy media enterprises whose installed estates are part of the business. iHeart and Mediaocean are archetypes: enormous flow, mixed old and new systems, human approvals, relationship-driven procurement, warehouse and file workflows, and important processes that teams still assemble manually at quarter end.
We win by improving those estates progressively. We do not require a clean-sheet replacement before Precise can create value.
Polymarket is the deliberate fast-data exception and production-pressure test. It forces the general research, physical-plan, workload, recovery, and receipt system to survive scale and long-running work. It does not define the default media product or load into every Precise process.
Acero is a prospective design-partner pitch, not a customer or deployment today. It is Taylor's father's company and gives us a specific way to explain how a Precise capability becomes valuable in machining, training, quality, and supply-chain decisions while the industrial context stays local. It is a grounded opportunity for the network thesis, not current proof and not a reason for Precise to abandon its near-term media focus.
A game makes the Precise workflow visible in one evening: connect permitted data, explain the current result and supported drivers, recommend or apply one reversible change, measure and decompose the new outcome, and improve the next model or recommendation. Games run this workflow on a fast clock. Media remains the commercial center and proves enterprise value on the slow clock.
The game and SDK make the workflow concrete while keeping the game intact. The same Precise capability retains its meaning when it operates through Boombox in the product owner's environment.
The product insight is simple: analytics records what users did. Precise explains the current outcome, recommends a change with an expected effect, and measures what changed on the other side. Internal episodes retain the complete baseline, recommendation, action, and result. A compatible set of complete customer learning episodes above the declared support floor reveals how calibrated the method or workflow is for that question class. The same capability travels into media, industrial, or other authorized settings without pretending a result from one domain transfers into another.
Polymarket is two things at once, and we hold both in the room.
The first is what we are in there for today: the media work. Their question is direct, how does Precise help us make better media decisions, and the answer below is concrete.
The second is the bigger play, and it is why this account matters beyond its budget: Doors (our application SDK, connecting product outcomes to media decisions), the network, and the pattern underneath this sales model. We show up as the new vendor. The customer uses the network as its tool. Functions that outside vendors charge rent for today become governed capabilities on the customer's own context tomorrow. Doors and the network are part of the account strategy, not a separate idea.
There is also a first-principles reason Doors matters: the analysis improves with the right data. An outcome is never one event. Between the media spend and the first deposit sits the whole product: onboarding, discovery, the ticket, the notification, dozens of variables downstream of the ad that decide whether the spend worked. The more we can observe in the context of the outcome, the further it decomposes into its pieces, and every evidenced piece becomes another addressable place to test or refuse a contribution claim. The decomposition can go all the way down. Finer grain does not manufacture causal credit; it makes the nodes, relations, unknowns, and evidence ceilings explicit. Doors instruments the journey so Precise can analyze contribution at the grain the evidence actually supports.
The customer-owned Outcome Map joins both sides of the problem at one root: upstream media and acquisition inputs, and downstream onboarding, product use, retention, and value. The same book runs on both sides: media rows reweight spend across partners, app rows reweight the journey across its screens, every bet under paired health guardrails and a signed never-touch list enforced in code. The Outcome Window selects one authorized subtree or depth without breaking lineage to that root. The recommendation operates on one window. Internally, the Call records its expected effect before action. The next outcome analysis can keep or move the window.
Precise connects each ad-spend decision to what acquired users actually do after the ad. Most media reporting stops at delivery, clicks, sign-ups, or a coarse conversion. Polymarket can judge channel, creative, audience, bid, allocation, and message decisions against declared downstream outcomes: onboarding completion, activation, first useful market interaction, trading quality, return behavior, category breadth, or another outcome Polymarket names. Paired guardrails prevent a cheap acquisition metric from hiding a worse customer result.
connect permitted media, wallet, market, and app-usage data
→ explain current outcome components and supported drivers
→ recommend a change and expected effect (recorded internally as a Call)
→ record the actual action under its comparison and guardrails
→ measure and decompose the new outcome after the declared window matures
→ evaluate both the intervention effect and decision-process contribution
→ update the authorized learning history and next model or recommendation
The wallet and market observability work supplies the prior state: market context, category behavior, liquidity, resolution timing, product use, and the contribution picture available before the media decision. Doors carries that analysis into a Call that binds the chosen channel, creative, audience, bid, allocation, landing path, or onboarding promise and names the forecast, confidence, outcome, horizon, guardrails, and evidence stream. Attribution plus downstream app-usage evidence score and decompose the new outcome. The Decision Learning Board connects that cycle to the media learning history while the engagement is running.
That creates better ad-spend decisions across the whole acquisition path:
Polymarket also lets us show where this expands without adding that work to the current media scope. The same observability and SDK machinery can attach Calls to the onboarding, discovery, ticket, notification, and maker-incentive choices that shape acquisition quality. That lets Precise help Polymarket improve not only where it spends, but the connected app journey that determines whether the spend worked. Off-chain product events show what interface and behavior occurred; on-chain market and settlement references make selected outcome facts independently re-checkable. A chain reference does not prove that an intervention caused an outcome, so every causal Call still carries its comparison, support, interference, horizon, and evidence law.
The longer product idea fits because Polymarket's users already make predictions against resolved outcomes. With explicit rights and enough compatible episodes, the same scoring machinery can produce category-specific reliability histories and calibration surfaces that distinguish forecast skill from profit and loss. That is an expansion concept to show in the 2026-08-05 Polymarket conversation, not a claim about the current engagement or an automatic use of wallet identity.
Games supplies the fast, intuitive demonstration. Polymarket observability and the SDK feed directly into better media decisions. The connected onboarding and app usage view is the immediate expansion: the same machinery pointed at the journey after the ad. Decision Learning cycles and calibration as a customer surface show how the same Precise capability expands. Boombox then operates the qualified capability first in Precise infrastructure and, through a separate admission, in Polymarket infrastructure. On-chain references, Boombox operational Receipts, and Precise Calls remain connected but distinct.
The customer product is the workflow that connects data, explains the current result and its drivers, recommends or applies a change, measures the outcome, and improves the next recommendation.
Within that workflow, Precise keeps four questions separate:
The first two explain the current result. The third is prospective. The fourth evaluates the decision process after the new outcome exists. They remain connected without becoming interchangeable.
A Decision Learning episode is the internal learning unit that keeps one pass through the workflow coherent. The Call is its internal forward-bet record, not the root primitive or the user story. The Decision Learning Board is the operating surface over the workflow. The Call Sheet is its focused internal accountability view, available when an operator needs to inspect the six fields and their refs.
A Call is the internal append-only record of the forward bet. It holds six stable fields: what was known, what was chosen, what was predicted at what confidence, what later happened under the declared evidence law, what contribution the declared comparison supports, and what the result taught the system. The first three fields are recorded before action. Score closes or refuses the Call against mature evidence. Contribution records only the credit the declared comparison and evidence identify. Book admits only the lesson that the evidence, product-learning authority, and applicable capture rights authorize. The original opening never changes.
The six fields remain stable through the cycle while their authorities stay separate. Known carries the prior analysis available to the decision. Chosen and Predicted state the forward bet. Happened and Contributed describe the new outcome and the credit supported by its declared comparison. Taught records only the authorized change to the next weights, policy, or action.
Explain the current result. Improve the next decision. Measure and repeat.
The technical episode, Call, evidence, evaluation, and operational Receipts remain separate internal authorities underneath the customer workflow:
prior outcome and contribution picture
→ decompose contributing inputs and outcome components
→ declare comparison, evidence, horizon and authority for the next cycle
→ frozen Precise candidate set across models, methods, strategies, policies and Surfaces
→ evidence-bound court selects one, returns null, or issues an explicit refusal
→ Call records known, chosen, predicted and confidence: the forward bet
→ immutable pre-action episode with the complete declared denominator
→ recommendation, approval, activation or refusal under granted authority
→ signed evidence over the frozen maturation window
→ new outcome scored as supported, refuted, inconclusive or unobservable
→ new outcome decomposed and contribution appended only to the identified evidence ceiling
→ prior state, prediction and new state compared
→ append-only restatement when later evidence changes the view
→ lesson booked and next weights updated only where learning is authorized
→ customer learning history updated only with complete compatible episodes above its support floor
→ next recommendation and customer workflow cycle
The append-only customer learning history retains typed evidence and data refs, versions, baseline analysis, recommendation, actual action, outcomes, contribution, uncertainty, refusals, and corrections under explicit rights. Raw customer data stays in its declared custody. Precise uses the authorized history to train and evaluate better domain models and recommendations over time. Konstant receives only the authorized routing and experience subset; it does not train or own the Precise domain model.
The important distinction is between making an answer and earning the right to learn from what followed. A selected-only outcome cannot teach a selector that its unchosen alternatives were inferior. A host failure is not a refutation of the method. A pending or censored outcome is not a final grade. A refusal is a product result, not an embarrassing exception to hide.
Every scoreable mature Call attempts two separate evaluations. Either one can support a lesson, remain inconclusive, or refuse. The first concerns the intervention: what the frozen design supports about movement in the declared outcome. The second produces a forecast score and determines whether the Call can enter a compatible calibration history. Calibration requires a collection above the declared support floor with the same scoring rule, question class, context, horizon, evidence design, and guardrail policy. Individual-person scoring requires explicit personnel governance; the default Precise product view is method, workflow, and qualified-release calibration.
The two selection systems remain separate all the way through. Konstant's organizational structural selection routes a company to the capability, provider, and composition that fit its context and authority. Precise's evidence-bound selection operates inside the Precise decision problem and compares the declared models, methods, strategies, policies, and Surfaces under frozen evidence. A Konstant route never decides the Precise court. A Precise court never grants a company relationship or chooses a provider for the customer.
"Master eval" means a common lineage and operating grammar, not one universal score or one central grader. Different questions require different evidence and different owners.
| Layer | Question | Authority |
|---|---|---|
| Core and harness conformance | Does this implementation obey its contract and floors? | Precise capability owner |
| Evidence-bound selection, ablation, and research court | Did an eligible model, method, strategy, policy, prompt, evidence treatment, or Surface outperform under a frozen comparison and declared floors, or did null/refusal remain correct? | Precise research program |
| Decision Learning episode | What baseline did the current-result analysis establish; what was recommended and actually done; what outcome followed; what did the intervention and decision process contribute; what learning can improve the next model or recommendation? | Precise product semantics |
| Surface eval | Did the user experience preserve the decision, evidence, uncertainty, refusal, and authority correctly? | Precise product and design judgment |
| Release qualification | Does this exact immutable release deserve admission or promotion for this product purpose? | Precise verifier plus target-specific Boombox admission |
| Network evaluation lineage | Can an authorized party inspect, reattach, restate, and verify the Call through a common language without Konstant becoming the domain grader? | Konstant canonical ontology and Boombox lifecycle; Precise domain extension and judgment |
| Reliability claim | What does a compatible, rights-authorized history support for this capability, release, context, and population? | Precise derivation; Boombox transports the bounded reference |
This separation prevents a platform uptime failure from becoming a negative product grade and prevents a model metric from silently becoming deployment, training, commercial, or activation authority.
Konstant owns and stewards the open common ontology beneath these layers: Product,
Capability, Surface, Release, Deployment, Run, Receipt, and the canonical
evaluation envelope. That envelope carries subject, claim, case, case_set,
context_bindings, configuration, execution, observations, judgment,
outcome, comparison, decision, lineage, custody, capture policy, and explicit
product_learning_authority_ref. Precise extends that language with media and
decision-learning concepts, including Calls, Scores, Contributions, Books, Track
Records, evidence streams, evidence rules, candidate sets, ablation specifications
and results, research courts, evaluators, floors, selection/null/refusal decisions,
and product judgments. A Precise Call maps onto those common identities; it never
redefines the Boombox Receipt. The extension adds product meaning without redefining
common identity, tenant, authority, custody, provenance, or lifecycle semantics.
That shared language allows a second provider or downstream customer to build a different evaluator, product, agent, or analytic view over the same interoperable lineage without surrendering its domain IP or forking the network grammar.
The strategic Konstant learning loop is:
authorized HomeBase company context
+ Capability Experience Graph
+ Evaluation Atlas
→ better capability routing and product plans
→ the smallest useful next-eval suggestion
The suggestion remains a suggestion. It cannot invoke a capability, qualify or promote a Precise release, publish a Product, or invoice a customer. Precise and its customers share only the refs, summaries, aggregates, and experience records allowed by the applicable custody rules and Learning Grant.
A mathematically correct result can still create a bad decision if the surface hides uncertainty, changes the comparison, implies authority the user did not grant, presents an extrapolation as evidence, or makes a principled refusal look like a broken application.
A surface eval therefore goes beyond screenshot QA. It asks whether the complete customer experience preserves the Precise claim:
Precise owns these domain eval semantics. The surface evaluation maps into
Konstant's canonical subject, claim, case, case_set, context_bindings,
configuration, execution, observations, judgment, outcome, and decision
fields. Boombox provides product-neutral lineage, durable custody, recovery, target
binding, and receipts. It never decides whether a Precise screen or recommendation
is scientifically good.
For every meaningful capability seam, the harness provides:
one canonical core
+ named runtime contract
+ describe/index position
+ success and refusal evals
+ honesty floors
+ evidence and outcome bindings
= one executable Precise capability
Applications and agents are clients of this capability. Product interfaces remain essential, but they do not become independent sources of scoring, grading, routing, or evidence truth.
This is progressive, not a monorepo rewrite:
Old React applications do not need to become Svelte merely because a new rich workflow standard uses SvelteKit. Independent historical projects do not need one framework version merely for aesthetic consistency. We standardize the next meaningful seam, not every old directory.
The browser consumes stable Precise product objects, not BigQuery itself.
product surface
→ thin Precise BFF / product edge
→ named, bounded read projection
→ canonical harness command
→ Boombox run or lifecycle observation when work is durable
Each named analytical read carries runtime schemas, tenant and acting-for context, source time, freshness, scan budget, materialization policy, job identity, bytes processed, and any product evidence refs. Repeated screens receive compact read models instead of rescanning a warehouse on every render.
GraphQL earns its place when several independently owned capability schemas compose repeatedly across real product journeys. It is not the starting authority, a replacement for bounded reads, or the meaning of federation. Resolvers would call the same named projections and commands beneath a BFF.
The rich new workflow shell is a Precise-owned portable application. Boombox native surfaces remain useful for bounded operational views. Customer branding occurs through a verified presentation contract rather than arbitrary customer code forks or hidden evidence/refusal UI.
The ordinary path is:
build and prove the capability locally
→ operate the qualified release in Precise-owned infrastructure
→ separately admit the same release into customer infrastructure
→ separately authorize any downstream customer relationship
Precise keeps the product semantics constant. Each target receives its own identity, policy, trust binding, residency, custody, billing, mutable state, and receipt chain. Authority never arrives merely because two companies are adjacent in a tenant tree.
When customer work exposes a reusable identity, deployment, workload, recovery, receipt, presentation, or capability-travel need, Precise states it through the typed Boombox requirement channel. Konstant builds the reusable primitive. Precise uses a bounded adapter behind the final port to keep customer work moving, with an explicit condition for retiring it.
Jordan and other developers say:
Put this workflow on Boombox. Reuse the harness and frontend already here. Get the Precise-hosted version running, then show what changes for customer infrastructure. Ask only for decisions the repository cannot determine safely.
The package-pinned onboarding skill and MCP inspect the repository, map the local application, workload, data, eval, and presentation seams, and compile requirements without credentials or side effects. A fresh authenticated Precise-tenant builder context performs live operations and keeps durable requirement-return threads. The developer does not need to recite hidden Konstant architecture.
The operating model enters through the work already in hand: name the consequential decision, preserve its evidence and refusal, use the canonical core and harness, and return reusable platform needs through the typed seam. The proofs, evals, and deployment path become part of delivering the product rather than a parallel process. The conversation is what this means for each current customer journey:
This is our operating mandate from here.
Blockdaemon is the first Konstant operating laboratory. It establishes the design-partner pattern: a Blockdaemon-owned product surface and one bounded capability that can travel to a downstream wallet company. Acero provides the corresponding industrial pattern. What matters for this room is how Precise sits in those networks: the featured decision and contribution capability, licensed into them through the same customer workflow, reliability records, and operating rails.
I need every person at Precise to understand the capability-network thesis and explain it in plain language. We turn customer workflows into qualified Precise capabilities; use our own evidence-bound selection and ablation courts to choose or refuse models, methods, strategies, policies, and Surfaces; operate the qualified result through Boombox; return common platform needs to Konstant; and help customers become AI-native without surrendering their data, relationships, or infrastructure choices.
The team also sells the design-partner model. We show expert companies how to turn their differentiated product or workflow into a provider-owned capability that can operate through their customer network. Precise goes first, exercises Boombox through demanding product cases, and demonstrates why my time and economic alignment across the companies increase Precise's influence, distribution, learning, and upside.
Precise becomes a primary daily user of Gary, HomeBase, Boombox, the Build Chronicle, the Evaluation Atlas, and the Capability Experience Graph. We use Gary and HomeBase to express Precise capability demand, develop and qualify releases, route them, operate them through Boombox, inspect evals and authorized customer experience, and improve the next capability route. That daily pressure creates better Precise capabilities and a better network.
We use Precise's customer work to make the network real. We use the network to make every Precise capability easier to deploy, license, improve, and distribute.
I keep the thesis, economic alignment, company boundary, licensing strategy, and joint commercial direction coherent. I make the cross-company decisions explicit before ambiguity turns into resentment or duplicated work.
Large legacy vendors own value because they sit inside a customer's integrations, workflow, data path, approval process, and downstream distribution. Replacing them all at once is slow and usually unnecessary.
We land one capability where it is already useful. That foothold grants no general right to the customer's data or business. We learn only the workflow and outcome structure the customer authorizes us to inspect: which handoffs exist, which proofs matter, where work stalls, and which outcomes close the loop. That bounded learning reveals the adjacent SaaS tollbooths the customer can replace with governed agentic capabilities: verification, log reconciliation, approval, reporting, and other functions that become cheaper and more accountable when they operate under the customer's mandate.
In crypto, a vampire attack drains an incumbent network by luring its participants away all at once with better terms. Our vampire attack works differently:
The customer becomes AI-native when its Boombox-operated Company Advocate can use the authorized company context, find and compose qualified capabilities, operate them under standing mandates, inspect what happened, and improve the next route from real outcomes. The customer mandate remains the action authority throughout.
Our attack pattern follows the lesson of large media estates: the customer keeps the installed business, integrations, workflow, and relationships while an independent market improves one function at a time. Hyperscaler-owned media such as YouTube, Prime Video, and Apple TV+ illustrates the company-town alternative. AI brings the same fork to every industry, and we build the independent path: entry through capability, on rails that are not captive to one capability provider.
Concrete ad tech examples: fraud detection and tag management. Both sit in the supply chain charging rent on functions a customer's own network can run as governed capabilities. Fraud checks become priced, graded calls on the customer's own data. Tag management becomes first-party instrumentation the customer owns. Neither vendor needs to be fought head-on; the function simply moves home, one seat at a time.
The attack targets closed distribution and rent, not another company's data or IP. Customers keep their estates and relationships. Capability creators keep their products. Konstant and Precise win because the better network becomes the place where the work happens.
Precise's advantage is not that it will own one model forever. Its advantage is that it can repeatedly identify the customer's real decision, freeze the legitimate candidate set, determine which model, method, strategy, policy, or Surface earns use under evidence, or choose null or refusal, and learn only what the result supports.
The harness makes that discipline executable. Surface evals ensure it survives contact with the user. Boombox lets the resulting capability operate first with Precise, then with the customer, and eventually through authorized downstream relationships. Konstant's independence gives that network room to work across industries, while the private company instruments align Precise economically with the value it helps create. Those instruments, not this meeting document, control the legal mechanics.
The team keeps doing research and building product. The system turns that work into products that learn, deploy, and compound.
The Precise team has the complete strategy and technical context: