Custom AI systems is Naevum's build path for leaders whose work no rented tool fits, so that their folders, memory and model use become one stack they own.
Keep people, roles and delegation sources distinguishable.
Selected roles, accounts and responsibility sources.
Keep people, roles and delegation sources distinguishable.
Selected people, accounts and roles.
Delegation and responsibility sources, not inferred authority.
A relationship or inferred role does not create authority.
Make inclusion, exclusion, access and freshness explicit.
Actual source inclusion and access; not a UI inspection event.
Independent dated observation.
Make inclusion, exclusion, access and freshness explicit.
Explicitly included sources and exclusions.
Dated observation, source and access state.
Opening an explanatory panel does not include a source or grant access.
Retain facts, claims, decisions and corrected interpretations with their lineage.
Source-bearing observation with time and scope.
Selected relevant role and responsibility.
Separately supplied evidence correcting an earlier inference.
Attributed action/consequence history and retained uncertainty.
Retain facts, claims, decisions and corrected interpretations with their lineage.
Evidence, time and unresolved contradiction.
Current inference differs from source observation; correction preserves ancestry.
An inference remains distinguishable from its source observation.
Relate selected assets and dependencies to observations and responsible operators.
Selected assets, capabilities and dependencies.
Relate selected assets and dependencies to observations and responsible operators.
Asset, dependency and operator relationships.
Known capability, unavailable integration and unknown state.
A known capability or dependency is not an execution grant.
Relate context to declared aims while keeping forecasts and inferred habits distinct.
Known capabilities and operator/dependency relationships; no permission.
Explicit objective, commitment or corrected preference.
Evidence connected to the declared objective and role.
Relate context to declared aims while keeping forecasts and inferred habits distinct.
Explicit objectives, commitments and instructions.
Inferred pattern and explicit correction.
Possible futures are forecasts, not observed facts.
Possible futures remain forecasts rather than observed facts.
Relate proposed work to existing authority and independently observed results.
Possible next action grounded in corrected context.
Actual applicable decision, when supplied; context grants none.
Independent record of what actually occurred under the proposal identity.
Later observed consequence, distinct from the decision and action record.
Relate proposed work to existing authority and independently observed results.
Bounded proposal and pending decision.
Supplied authority, actual action record and independent consequence when present.
The exact proposal, applicable decision, actual action and later consequence retain distinct evidence.
Selected roles, accounts and responsibility sources.
Keep people, roles and delegation sources distinguishable.
Selected people, accounts and roles.
Delegation and responsibility sources, not inferred authority.
A relationship or inferred role does not create authority.
Actual source inclusion and access; not a UI inspection event.
Independent dated observation.
Make inclusion, exclusion, access and freshness explicit.
Explicitly included sources and exclusions.
Dated observation, source and access state.
Opening an explanatory panel does not include a source or grant access.
Source-bearing observation with time and scope.
Selected relevant role and responsibility.
Separately supplied evidence correcting an earlier inference.
Attributed action/consequence history and retained uncertainty.
Retain facts, claims, decisions and corrected interpretations with their lineage.
Evidence, time and unresolved contradiction.
Current inference differs from source observation; correction preserves ancestry.
An inference remains distinguishable from its source observation.
Selected assets, capabilities and dependencies.
Relate selected assets and dependencies to observations and responsible operators.
Asset, dependency and operator relationships.
Known capability, unavailable integration and unknown state.
A known capability or dependency is not an execution grant.
Known capabilities and operator/dependency relationships; no permission.
Explicit objective, commitment or corrected preference.
Evidence connected to the declared objective and role.
Relate context to declared aims while keeping forecasts and inferred habits distinct.
Explicit objectives, commitments and instructions.
Inferred pattern and explicit correction.
Possible futures are forecasts, not observed facts.
Possible futures remain forecasts rather than observed facts.
Possible next action grounded in corrected context.
Actual applicable decision, when supplied; context grants none.
Independent record of what actually occurred under the proposal identity.
Later observed consequence, distinct from the decision and action record.
Relate proposed work to existing authority and independently observed results.
Bounded proposal and pending decision.
Supplied authority, actual action record and independent consequence when present.
The exact proposal, applicable decision, actual action and later consequence retain distinct evidence.
An illustrative context landscape relating sources, responsibilities, capabilities and possible futures.
Additional mechanisms are described in the technical reference.
Illustrative first engagement: a procurement lead needs to explain changed supplier commitments using a bounded set of approved policy and supplier documents.
The procurement lead selects the documents, the recurring question and the decision that remains theirs. Record access rights, excluded material, freshness and the deployment boundary.
A supplier revises a delivery date. The working slice recovers the earlier answer from the earlier evidence and proposes a current planning update from the revised source.
Deliver the bounded application, deployment/recovery instructions and acceptance evidence. Test historical answers, excluded-source refusal, correction propagation and the agreed interrupted-work case.
Connected sources become structured context. Observations keep their origin; inferences remain open to correction. This is the architecture of a dedicated engagement, shaped around the world you choose to connect.
Preserve source-connected history with two distinct time axes.
Original or changed policy with source and both time axes.
Preserve source-connected history with two distinct time axes.
Source, holder, validity and recorded-at time.
Earlier records and operation ancestry.
Valid time and recorded-at time remain distinct.
Apply the actual semantics of revision, contest and historical use.
Claims and typed operations.
Validity time, knowledge time and scope.
Apply the actual semantics of revision, contest and historical use.
Supersession, contest, suppression and retraction differ.
Validity question and knowledge cutoff.
Supersession, contest, suppression and retraction have different historical meanings.
Assemble relevant evidence without confusing derived lookup with canonical memory.
Historically appropriate or contested evidence.
Rebuilt derived structures; canonical history remains.
Assemble relevant evidence without confusing derived lookup with canonical memory.
Derived lookup and canonical-source link.
Scope/byte bounds and explicit abstention.
A derived lookup does not replace canonical source memory.
Relate the selected evidence to the right responsibility and objective.
Bounded source-bearing selection or abstention.
Selected role/objective and explicit corrections.
Relate the selected evidence to the right responsibility and objective.
Selected identity, role, infrastructure and purpose.
Corrected inference with its lineage.
A corrected inference retains its source and responsibility context.
Annotate, test and retain useful derived structures without rewriting history.
Canonical records and typed retention requirements.
Measured harms, utility and remaining uncertainty.
Annotate, test and retain useful derived structures without rewriting history.
Duplicates, conflict and typed retention/forgetting.
Proposed generalization remains distinct from promoted instruction.
A proposed generalization does not silently become an instruction.
Keep actual utility, failures and subsequent-use evidence available.
Actual retrieval use, not automatic future utility.
Independent useful/failing reuse evidence.
Keep actual utility, failures and subsequent-use evidence available.
Measured failure and caution/release basis.
Attributed retrieval use and independent future utility.
Claims of future usefulness need independent later-use evidence.
Original or changed policy with source and both time axes.
Preserve source-connected history with two distinct time axes.
Source, holder, validity and recorded-at time.
Earlier records and operation ancestry.
Valid time and recorded-at time remain distinct.
Claims and typed operations.
Validity time, knowledge time and scope.
Apply the actual semantics of revision, contest and historical use.
Supersession, contest, suppression and retraction differ.
Validity question and knowledge cutoff.
Supersession, contest, suppression and retraction have different historical meanings.
Historically appropriate or contested evidence.
Rebuilt derived structures; canonical history remains.
Assemble relevant evidence without confusing derived lookup with canonical memory.
Derived lookup and canonical-source link.
Scope/byte bounds and explicit abstention.
A derived lookup does not replace canonical source memory.
Bounded source-bearing selection or abstention.
Selected role/objective and explicit corrections.
Relate the selected evidence to the right responsibility and objective.
Selected identity, role, infrastructure and purpose.
Corrected inference with its lineage.
A corrected inference retains its source and responsibility context.
Canonical records and typed retention requirements.
Measured harms, utility and remaining uncertainty.
Annotate, test and retain useful derived structures without rewriting history.
Duplicates, conflict and typed retention/forgetting.
Proposed generalization remains distinct from promoted instruction.
A proposed generalization does not silently become an instruction.
Actual retrieval use, not automatic future utility.
Independent useful/failing reuse evidence.
Keep actual utility, failures and subsequent-use evidence available.
Measured failure and caution/release basis.
Attributed retrieval use and independent future utility.
Claims of future usefulness need independent later-use evidence.
An illustrative method for retaining canonical evidence and answering scoped historical questions.
Additional mechanisms are described in the technical reference.
Your environment connects the sources and capabilities you choose.
People, organizations, roles, accounts and delegated responsibilities.
Inspect the active identity, who may act for whom and the source of an attribute.
Selected documents, repositories, communications, applications, devices and services.
Choose connected sources, exclusions, freshness requirements and access permissions.
Claims, decisions, episodes, contradictions and the intervals when they apply.
Follow the evidence, correct a claim and define retention and deletion behavior.
Assets, services, dependencies, capacity and operating conditions.
See the topology, current observations, unknowns and responsible operators.
Objectives, priorities, obligations, success criteria and resource limits.
Change what the environment is working toward and how success will be judged.
Your explicit preferences and supported patterns in authorized work.
Correct an inference and distinguish it from an instruction you deliberately gave.
Forecasts, horizons, uncertainty and alternative actions.
Examine assumptions and keep a scenario distinguishable from an observation.
Permitted actions, consent, effect identities, results and recovery obligations.
Inspect what may happen, what happened and what remains uncertain; stop or revise the work.
Observations keep their sources. Inferences remain open to correction. You can inspect the model and change what it is allowed to do.
Explore memory & knowledgeWhat the research says
Leaders expect agents in their workforce, few European firms use AI at all, and the rented route is priced per seat.
Intent is high and practice is thin: leaders expect agents while most EU firms still use no AI at all. The gap sits in the system around the model, so a custom engagement builds that system first, around one decision your team already makes.
Your systems contain much of what your organization knows. The difficult part is connecting that knowledge to the work that needs to happen next.
We design a dedicated environment around your sources, people, infrastructure and objectives. A versioned world model connects identity and digital footprint to memory, reasoning and permitted action.
Hyperpersonalization comes from that inspectable context: the responsibilities the system respects, the preferences you can change and the tasks by which you judge its usefulness. Each engagement selects and evaluates the capabilities it needs.
An answer needs its source and the time when it applies.
Choose systems and records within an agreed access boundary. Monitoring cadence and freshness remain visible.
Distinguish observations from inferences. Define correction, retention and deletion across source-connected indexes.
Turn a named objective into an admitted workflow with a result, operator controls and a recovery path.
A chief digital officer decides which work the stack covers and which part Black Box Factory builds. Illustrative, with a synthetic regional distributor.
Systems behind it
Institutional memory & attention
Vagus keeps each decision the system touches with its date, its authority and the sources it rested on.
Private computation & model orchestration
Synapse decides which part of your material an outside model may see, and records each send.
Persistent personal & industrial agency
Manna carries a goal between sessions and holds any effect above its limit until a person decides.
Governed application foundation
Agency Engine gives the new system its approvals, permissions and record from the first day.
AI systems & software development
Black Box Factory builds the part no product covers, and a separate reviewer checks it before your team runs it.
Your business has diagnostic skill, accepted decisions and failed experiments whose meaning depends on its own operating context. A dedicated learning study can examine how that experience should inform the next task.
We can scope a study around one recurring task. An engineering team might bring expert-corrected diagnostic drafts and the observed results of interventions; an R&D team might bring a reproducible method and the variants that failed.
The study chooses what should change: source-connected memory, a procedure, a calibrated estimate or a trainable model. Targeted adaptation is considered when the selected model supports it and the examples are permitted for that use.
We agree a no-update baseline and a source-grounded retrieval comparison, then evaluate new work, retained capabilities, data exposure and total cost. An accepted candidate needs its own version, promotion decision and recovery path. The evaluation determines the next operating decision.
Retain expert corrections, outcomes and negative findings with their source, time and permitted use.
Compare memory and procedure changes with targeted model adaptation on the same task and evidence basis.
Reserve unseen cases and earlier accepted tasks. Evaluate useful behavior, retained knowledge, cost and exposure before promoting a candidate.
The first task is to map the systems and workflows you already use. The architecture determines which integrations are useful and where a new component is justified.
Source, data custody, deployment, intellectual-property terms, support and operating responsibilities are agreed for the engagement. Discovery makes those choices concrete.
Temporal context around actual work.
Works withControl what context reaches an observer.
Works withWork that can persist between instructions.
Built onDomain workflows and interfaces.
Delivery systemBuild and hand over the selected architecture.
Six ways to turn a difficult operating problem into a system you can use.
Knowledge and action are scattered across private systems.
Named tasks completed correctly with current context, appropriate permissions and recoverable results.
Teams repeatedly reconstruct decisions and lose the reasons behind them.
Correct evidence recovered and useful decisions supported on the agreed task set.
Work needs to continue across events, tools and interruptions.
Accepted completion, recovery and intervention rate under stated conditions.
Early signals are available but do not become timely decisions.
Calibration, lead time, false alarms and adjudicated intervention outcomes.
A business needs a complete product with its own domain workflows.
Accepted user journeys, upgrade and recovery behavior, and maintainable integrations.
AI use depends on custody, location, capacity, energy or inference constraints.
Named boundary tests, workload measurements and independently checked physical assumptions.
A clear chain from the work you need to the system you can operate. Each stage leaves something concrete to inspect.
Explore the development systemMap the workflow, sources, people, constraints and current baseline. Decide what a useful result would change.
Build one representative slice through the intended integrations or an agreed test environment. Examine failure cases before expanding the scope.
Implement the selected scope with review, acceptance and operational responsibilities attached to the exact release.
Agree monitoring, support, recovery and change control. Evaluate improvements against actual tasks and retained baselines.
Connected sources. Choose systems and records within an agreed access boundary. Monitoring cadence and freshness remain visible.
Correctable understanding. Distinguish observations from inferences. Define correction, retention and deletion across source-connected indexes.
Useful action. Turn a named objective into an admitted workflow with a result, operator controls and a recovery path.
An illustrative custom-development journey from a recurring operational problem to handover.
Follow the storyWhich decision does your team make every week that no product on the market fits?
Discuss your first slice