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The Custom AI Build is a proprietary AI system engineered to the operator's specification. Price range $10,000 to $50,000. Scope is determined after the AI Marketing Audit and a dedicated scoping call. Duration 6 to 12 weeks. The operator owns the system outright on handoff. Stan Consulting LLC.

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F6.5 · AI Operator Lane · Top-tier engagement

The requirement exists. The off-the-shelf tool does not.

A proprietary AI system, built to your specification, tested against your requirements, and documented for the internal team that will maintain it. From $10,000. Scope set after audit and scoping call.

Scope the Custom Build · From $10,000 Back to AI Operator Lane

02

Quick answer

The Custom AI Build is a proprietary AI system engineered to the operator's specification. It is for funded operators who have identified a specific AI requirement that available tools cannot meet: a fine-tuned model, a content intelligence system, a custom attribution integration, or an AI agent that does not exist as a product. Price runs from $10,000 to $50,000 depending on scope, set after the AI Marketing Audit and a dedicated scoping call. Duration is 6 to 12 weeks. The operator owns the system, the model weights, and the documentation outright on handoff.

Off-the-shelf tools are the right answer until they are not. This engagement begins where they stop.

03

When custom engineering is the answer

Five categories of work that off-the-shelf tools cannot do.

These are not edge cases. They are the categories that consistently appear when a funded operator's AI requirement runs past what workflow configuration can deliver.

01

Custom model fine-tuning

A foundation model that does not know your product category, your nomenclature, your compliance constraints, or your output format requirements. General models produce general outputs. Fine-tuning produces a model that behaves consistently inside your domain.

Example use cases

Ad copy in a regulated category (financial services, pharmaceutical, alcohol) · product description generation for a catalog with 40,000 SKUs and proprietary taxonomy · customer support response generation calibrated to your brand's tone and escalation policy

02

Proprietary content intelligence

A system that reads your content corpus and surfaces patterns, gaps, or signals that would take a human team days to find manually. Not a dashboard plugin; a system built on your data, running on your infrastructure, returning your categories of output.

Example use cases

Automated content gap identification across 12,000 indexed pages against a ranked competitor corpus · topic clustering system that maps SERP intent to internal CMS structure weekly · editorial calendar generation from first-party keyword and engagement data

03

Custom attribution integrations

Attribution that crosses systems that do not natively connect. Your ad platform data, your CRM, your product analytics, and your offline event data are in separate systems. A custom integration surfaces the full picture in one place, on a schedule, in the format your team actually uses.

Example use cases

GA4 plus Salesforce plus ad platform reconciliation into a daily Looker Studio report · offline conversion import pipeline from a field sales CRM into Google Ads · multi-touch attribution model that weights touchpoints against closed deal value, not just last-click

04

AI agents specific to the operator's product domain

An AI agent that takes action inside your product, your CRM, or your marketing stack. Not a chatbot wrapper. A system that reads state, makes a decision, takes an action, and logs the result. No off-the-shelf agent product covers your specific combination of inputs and actions.

Example use cases

Lead scoring agent that reads inbound form submissions, cross-references CRM history, and routes to the correct sales queue with a scored summary · campaign budget reallocation agent that reads daily performance data and adjusts spend across accounts against a target CPA

05

Compliance-constrained AI systems

Standard AI tools cannot meet your compliance or security requirements. Data cannot leave your cloud environment. Model outputs require a human review layer before they reach customers. Your security team has audit trail requirements that SaaS tools do not support. These constraints require engineering, not configuration.

Example use cases

HIPAA-compliant content generation system with PHI isolation and output review queue · SOC 2-compliant AI pipeline with complete access logging and data residency in a specific AWS region · GDPR-scoped inference system that does not pass EU user data to US-hosted model APIs

06

Systems that replace a manual analyst function

A recurring analysis task that currently consumes 8 to 20 hours of an analyst's week. A system that runs it on a defined schedule, produces a structured output, and flags anomalies for human review. The analyst's judgment is encoded; the grunt work is automated.

Example use cases

Weekly competitive pricing report from 14 tracked domains, structured into category-level tables · monthly paid media performance summary across 9 ad accounts, reconciled against CRM-attributed revenue · automated tagging of inbound sales call transcripts by deal stage, objection type, and product mentioned

04

What you receive

Five deliverables. All of them yours.

The engagement ends with a working system, not a slide deck or a strategy document. Every deliverable listed below transfers to the operator at handoff.

01

The built and tested proprietary AI system

A working system built to the specification agreed at the scoping call. Functional testing is completed before handoff; integration testing is completed in the operator's environment during the testing phase. What ships is what was specified.

Primary deliverable

02

Full source documentation

A written technical document covering system architecture, data flow, configuration options, dependency list, and maintenance procedures. Written for the internal team that will operate and update the system. Not the kind of document that requires a follow-up call to interpret.

Operator-maintained

03

Testing protocol

A documented testing protocol the operator's team can run independently. Covers functional tests (does the system produce correct output for defined inputs), integration tests (does the system interact correctly with connected systems), and, where applicable, compliance tests (does output meet the defined review criteria). Runs in the operator's environment.

Operator-runnable

04

Onboarding session for the internal team

One structured session with the operator's team that will maintain the system. Covers system walkthrough, documentation review, testing protocol execution, and how to update and extend the system after handoff. Recorded at the operator's request. This is the last scheduled touchpoint; the system is the operator's from this point.

Live session · recorded on request

05

Post-handoff support window

A short window after the onboarding session (typically one to two weeks) for clarification questions via written communication. This covers edge cases the testing phase did not surface and documentation questions from the internal team. Ongoing operations after the support window are a separate engagement under the AI Stack Retainer.

1–2 weeks · written

05

Ownership structure

The system is yours. Full stop.

AI consultancy work sometimes delivers a system the consultant retains access to, or a workflow that depends on ongoing licensing from the consulting firm. This engagement does not work that way.

On handoff, the operator owns the code, the model weights (where fine-tuned), the training data (where operator-supplied), and the documentation. Stan Consulting retains no access, no license rights, and no ongoing claim on the system. There are no recurring fees tied to the system itself.

The operator's ability to operate, update, and extend the system after handoff is what the engineering effort and the documentation are designed to support. Independence is the outcome, not a licensing arrangement.

What transfers at handoff

All source code · model weights and fine-tuned adapters · training datasets (where operator-supplied) · system configuration files · technical architecture document · testing protocol · operator-facing documentation

What does not persist

No license fees tied to the system · no SaaS subscription requirement · no consulting retainer required to run the system · no access retained by Stan Consulting post-handoff

Scope of the post-handoff window

Clarification questions on documentation and edge cases only. The support window is not an operations engagement. Ongoing operations, monitoring, and optimization are available under the AI Stack Retainer (F6.4) if the operator chooses that path.

Price range reminder

$10,000 to $50,000. Scoped after the AI Marketing Audit and a dedicated scoping call. No quote without that call.

06

How scope and price are set

$10,000 to $50,000 is a wide range. Here is why it must be.

A fine-tuned model for a single content generation use case and a compliance-constrained AI pipeline with data residency requirements are both custom builds. They are not the same build. The range reflects that difference. Scope is not estimated from a description; it is set after the AI Marketing Audit establishes what the operator's system actually requires, and after a dedicated scoping call maps the requirement to an engineering approach.

The AI Marketing Audit (F6.1) is the diagnostic that makes the scoping call productive. It establishes the operator's current AI layer, identifies the specific gap, and produces the documentation a scoping call uses to define the build. Operators who arrive at a Custom AI Build without that audit typically find the scoping call takes longer and produces a less precise specification.

The scoping call is a working session, not a sales call. By the end of it, there is a written scope, a price, a timeline, and a clear boundary between what is in and what is out. Stan Consulting does not quote a Custom AI Build without that call.

Operators who have already completed the AI Marketing Audit and are ready for a scoping conversation should use the intake form at the bottom of this page. Operators who have not yet completed the audit should begin there; the Custom AI Build price accounts for operators who arrive with audit output in hand.

$10K–$18K

Contained single-purpose build

A single fine-tuned model or a single-pipeline integration with well-defined inputs and outputs. Operator's engineering team is available during build. Compliance requirements are standard or none.

$18K–$32K

Multi-component system

Two or more interacting components. May include fine-tuning, orchestration logic, and a data pipeline. Internal team involvement is partial. Standard compliance environment.

$32K–$50K

Full proprietary system with compliance layer

Complex architecture with compliance constraints (SOC 2, HIPAA, GDPR, internal security review). Data residency requirements. Formal security team review process. Multiple integrated components. Full documentation package.

07

6–12 week phase breakdown

Five phases. One working system at the end.

The timeline below reflects a standard build. Compliance-constrained builds and builds with formal security review processes add time in the architecture phase; that extension is accounted for in the scoping call timeline, not discovered mid-build.

Weeks 1–2

Discovery

Review of AI Marketing Audit output, scoping call documentation, and system requirements. Data sources mapped. Access provisioned. Architecture options outlined before the design phase begins.

Weeks 2–4

Architecture design

Written technical specification. Architecture document covering system design, data flow, component interactions, dependency decisions, and compliance accommodations. Operator review and sign-off before build begins. Security team review window sits here if required.

Weeks 3–9

Build

Iterative development with weekly written check-ins. Operator is not excluded from the build; progress is visible. Scope changes go through the change-order process, not informal additions. This is the longest phase; timeline variance lives here, not after it.

Weeks 9–11

Testing

Functional testing against the specification. Integration testing in the operator's environment. Compliance testing where applicable. The testing protocol document is produced in this phase; it is what the operator's team runs independently after handoff.

Week 11–12

Handoff

Documentation review, source transfer, and the structured onboarding session with the operator's team. Post-handoff support window opens at the end of this phase. Engagement closes when the support window closes.

08

Compliance and security

For operators whose AI requirements come with a compliance layer.

Regulated operators and operators with internal security review processes are not edge cases at this tier. Compliance requirements are flagged at intake and scoped explicitly, not accommodated as an afterthought.

SOC 2

Access logging, audit trail requirements, and data handling constraints are built into the architecture specification. The system does not go to build without a plan that the operator's compliance team has reviewed. Access to the build environment follows the operator's access control policy.

HIPAA

PHI isolation is specified before architecture design begins. Model inference pipelines that would pass PHI to external APIs are redesigned to run on operator-controlled infrastructure. Output review queues that require a human approval step before patient-facing delivery are built into the system, not appended later.

GDPR

EU user data that cannot pass to US-hosted model APIs is a design constraint, not a post-build fix. The architecture document specifies data residency for every component in the pipeline. Inference that must stay in the EU runs on EU infrastructure; that is scoped and priced at the architecture phase.

Internal data residency requirements

Operators with internal policies requiring all data to remain in a specific cloud environment or region have those requirements documented at intake. The architecture is designed around them. Systems that require data never to leave a specific AWS region or Azure tenant are a known scope type; the build accommodates them.

Model output review requirements

Where the operator requires a human approval step before AI-generated content reaches customers, that review layer is an engineered component of the system, not a workflow note. The review queue, the approval interface, and the audit trail for approved and rejected outputs are all in scope.

Security team review process

The architecture document is written to support a formal security review. If the operator's security team needs to approve the architecture before build begins, that review window is built into the timeline. Stan Consulting's team participates in the review session. The build does not proceed without sign-off where that requirement exists.

09

Explicit scope boundary

What this engagement is not.

The F6 lane has five products. Each does a specific job. Arriving at this tier with the wrong requirement wastes the scoping call and the operator's time. These clarifications exist to prevent that.

Not an AI strategy advisory

If the operator needs help deciding where AI fits in the marketing operation and does not yet have a specific engineering requirement, that is not this engagement. The AI Marketing Audit (F6.1) produces that analysis. The Custom AI Build begins after the requirement is identified.

The right engagement → F6.1 AI Marketing Audit

Not off-the-shelf tool integration

If the requirement can be met by configuring existing AI tools, connecting them via an API, and building workflows around them, that is the AI Workflow Build (F6.2). It is a faster engagement at a lower price point. If the workflow build cannot solve it, the custom build is the right next step.

The right engagement → F6.2 AI Workflow Build

Not team training

If the operator's team needs to understand how to use AI tools in their day-to-day workflow, that is the AI Operator Training (F6.3). That engagement covers tool selection, workflow protocols, and practical application. It does not include building custom systems.

The right engagement → F6.3 AI Operator Training

Not ongoing AI operations

Ongoing monitoring, optimization, and updating of an AI stack is the AI Stack Retainer (F6.4). That engagement picks up where a build ends. The Custom AI Build delivers the system and documentation; the retainer runs and maintains it. One does not require the other, but they are designed to connect.

The right engagement → F6.4 AI Stack Retainer

10

Fit sample

What a Custom AI Build engagement looks like.

The operator's situation

A Series B e-commerce operator in a regulated category. Catalog of approximately 28,000 SKUs. The content team was spending 60 to 70 hours per week writing and reviewing product descriptions, with a compliance review step required before any description reached the site. The company had evaluated three AI writing tools; all produced output that required the same review workload because the models did not know the category's compliance constraints or the brand's approved vocabulary list.

The engineering gap

No off-the-shelf tool could be fine-tuned on the operator's approved description library or constrained by the category's compliance ruleset. The compliance review step also needed to be an engineered output filter, not a manual bottleneck. A workflow build using existing tools would have produced faster first drafts with the same review load; it would not have reduced the review load.

What was built

A fine-tuned generation model trained on 4,200 approved product descriptions from the operator's catalog, constrained by a vocabulary and claim ruleset derived from the compliance team's style guide. An output review queue that scored each generated description against the compliance ruleset before routing it to a human reviewer. Reviewers saw pre-scored output with flagged phrases highlighted, not raw AI text. The full pipeline ran on the operator's AWS infrastructure; no product or description data left their environment.

What shipped

A working system that reduced the content team's weekly description workload from 65 hours to approximately 18 hours. The compliance review step remained, but reviewers were evaluating pre-scored output rather than generating it. The operator's team took ownership of the system at handoff. Documentation covered retraining the model on new approved descriptions as the catalog evolved. The engagement ran 10 weeks from scoping call to handoff session.

Anonymised · NDA protected · Specific identifying details altered

11

Fit check

Is this the right engagement.

This is

Custom AI Build is

  • For funded operators (Series B and above, or comparable) with a specific AI requirement that existing tools cannot meet
  • An engineering engagement producing a proprietary system built to specification
  • Priced from $10,000 to $50,000, scoped after the AI Marketing Audit and a dedicated scoping call
  • A 6 to 12 week engagement from scoping to handoff
  • Full ownership transfer: code, weights, documentation, testing protocol
  • Appropriate for compliance-constrained environments: SOC 2, HIPAA, GDPR, internal security review
  • The highest-effort, highest-investment engagement in the AI Operator Lane

This is not

Custom AI Build is not

  • A strategy advisory or an AI assessment (that is the AI Marketing Audit)
  • Off-the-shelf tool configuration or workflow integration (that is the AI Workflow Build)
  • Team training on AI tools (that is the AI Operator Training)
  • Ongoing AI stack management after handoff (that is the AI Stack Retainer)
  • The right entry point for operators who have not yet identified their specific AI gap
  • Available without a prior audit and scoping call
  • A retainer, a subscription, or an ongoing engagement by default

12

Direct answers

Questions about the build, the handoff, and what happens after.

Do you build with our existing engineering team?

Yes. Most engagements at this tier involve the operator's internal engineers at least during the architecture and handoff phases. The working model is defined in the scoping call: some operators want a full build handed over, others want their team involved in the build itself so they are ready to maintain it from day one. Either approach works. The scoping call sets that boundary before work begins.

What programming languages and frameworks do you use?

The stack is determined by what the operator's system requires and what the internal team will maintain. Python is common for model work and data pipelines. Frameworks vary by use case: LangChain, LlamaIndex, or custom orchestration depending on what the system needs to do. Where the operator has an existing stack, the build fits into it rather than dictating a new one. Stack rationale is documented in the architecture specification produced in week 2.

Who owns the model weights and fine-tunes?

The operator owns them. All model weights, fine-tuned adapters, training data (where operator-supplied), and system configurations transfer to the operator at handoff. Stan Consulting retains no rights and no access. The handoff documentation covers how to retrain, update, and audit the system without outside involvement.

What happens after the handoff onboarding session?

The engagement ends at the handoff. The onboarding session is a structured walkthrough for the operator's internal team: how to run the system, how to update it, what the testing protocol covers, and how to use the documentation. A short post-handoff support window (typically one to two weeks) covers clarification questions from the team. Ongoing operations after that window are a separate engagement under the AI Stack Retainer (F6.4).

Can we add scope mid-build?

Scope changes mid-build go through a written change-order process, not informal additions. The architecture specification produced in week 2 is the scope anchor. A change that is genuinely necessary goes through review and either gets added with an adjusted timeline and budget, deferred to a follow-on engagement, or formally removed from consideration. This protects both the delivery timeline and the operator's budget.

How do you handle our SOC 2 or HIPAA review process?

Compliance review processes are flagged at intake and built into the project timeline. If the operator's security team needs to review the architecture document before build begins, that review window sits in the architecture phase. If model output requires a human review layer to meet HIPAA requirements, that layer is specified and built in. Compliance constraints are not retrofitted; they are part of the initial specification.

Do you work with our security team?

Yes. For operators with internal security review processes, the security team is a stakeholder from the architecture phase onward. The architecture specification document is written to support their review. Data residency requirements, model output logging, access control, and audit trail requirements are scoped explicitly. The build does not proceed past architecture without security sign-off where that requirement exists.

What is the difference between this and an AI Workflow Build at the upper end of $15,000?

The AI Workflow Build (F6.2, up to $15,000) integrates off-the-shelf AI tools into existing marketing operations. It is software configuration, prompt engineering, and workflow design using available products. The Custom AI Build is engineering work: custom model fine-tuning, proprietary data pipelines, AI agents that do not exist as products, or systems with compliance constraints that standard tools cannot meet. If the requirement can be solved with available tools well-configured, the workflow build is the right engagement. If it cannot, this one is.

F6.5 · Custom AI Build · $10,000–$50,000

The scoping call is where the price and the timeline become specific.

Request the Scoping Conversation

Scope is set after the AI Marketing Audit and a dedicated scoping call. No quote without that call. Response within two business days.

Section 14 · Request the scoping conversation

Tell us about the requirement.

This is a fit conversation request, not a sales form. The intake is reviewed before any call is scheduled. If the requirement is clearly outside scope or not yet specific enough to scope, that is the response. If it fits, a scoping call is the next step. No quote without that call.

$10K–$50Kprice range, scoped after call
6–12 wkbuild duration
Yoursoperator owns the system outright
Price range · $10,000–$50,000 Scoped after audit + scoping call · 6–12 weeks