Two-Week AI and Systems Discovery Phase — $21,000

The purpose of the two-week discovery phase is to reduce uncertainty before implementation begins.

Rather than immediately building an AI solution, integration, automation, or data product, I first work with the client to understand the business problem, current systems, available data, user workflows, technical constraints, risks, and expected outcomes.

At the end of the two weeks, the client receives a practical implementation blueprint that clearly explains what should be built, how it should work, what it will cost, what risks need to be addressed, and how success will be measured.

The discovery phase is not a series of general strategy meetings. It is a structured working engagement that produces concrete technical, operational, and commercial deliverables.

Week One: Understand the business, systems, and data

Day 1: Executive kickoff and alignment

The engagement begins with a kickoff session involving the project sponsor and key stakeholders.

The goal is to align on:

• The business problem

• Why the problem matters now

• Who is affected by it

• What the organization has already tried

• What a successful outcome would look like

• What decisions need to be made at the end of discovery

• Who owns the implementation and business results

This session also establishes scope, communication cadence, access requirements, and stakeholder responsibilities.

Deliverables:

• Confirmed discovery scope

• Stakeholder map

• Project objectives

• Initial assumptions

• Open-question log

• Discovery schedule

Day 2: Current-state workflow review

I review how the process works today from beginning to end.

This includes understanding:

• Which teams are involved

• Which steps are manual

• Where delays occur

• Where information is re-entered

• Where errors or handoff problems happen

• Which decisions require human review

• Which systems are used at each step

• Which workarounds employees have created

The goal is to identify the real operational problem, not just the technical symptom.

For example, the client may initially believe they need an AI chatbot, but the deeper issue may be fragmented knowledge, inconsistent data, or unclear escalation rules.

Deliverables:

• Current-state workflow map

• Manual-step inventory

• Pain-point analysis

• Bottleneck summary

• Initial automation opportunities

Day 3: Systems and integration assessment

I review the applications, platforms, databases, APIs, middleware, and external services involved in the proposed solution.

This assessment looks at:

• Source and target systems

• Existing APIs

• Webhooks and event capabilities

• Authentication methods

• Middleware already in use

• Data ownership

• API limits

• Legacy-system restrictions

• Batch versus real-time requirements

• Existing cloud architecture

• Security and networking constraints

The purpose is to determine what can be integrated easily, what requires custom work, and where technical limitations may affect scope or budget.

Deliverables:

• System inventory

• Integration dependency map

• API readiness assessment

• Technical constraint log

• Preliminary integration approach

Day 4: Data discovery and quality review

The solution can only be as reliable as the data supporting it.

During this step, I review:

• What data is available

• Where it is stored

• Who owns it

• How often it is updated

• Whether identifiers are consistent

• Whether important fields are missing

• Whether duplicate records exist

• Whether historical data is available

• Whether the data can support the intended AI or automation use case

• Whether sensitive or regulated information is involved

For AI use cases, I also assess whether the organization has enough usable examples, labels, documents, events, or historical outcomes to support the proposed approach.

Deliverables:

• Data-source inventory

• Data-readiness assessment

• Data-quality findings

• Missing-data and access-gap report

• Preliminary data model

• Privacy and sensitivity classification

Day 5: User and stakeholder interviews

I interview the people who will use, manage, approve, or be affected by the solution.

Depending on the project, this may include:

• Operations leaders

• Frontline employees

• Care teams

• Sales teams

• Customer-service representatives

• IT leaders

• Security teams

• Data owners

• Compliance stakeholders

• Executive sponsors

These interviews help identify user needs, adoption risks, workflow realities, and requirements that may not appear in system documentation.

Deliverables:

• User-needs summary

• Role and permission requirements

• Adoption-risk findings

• User stories

• Prioritized pain points

• Human-in-the-loop requirements

Week Two: Design the future solution and implementation plan

Day 6: Future-state workflow design

Using the findings from week one, I design the proposed future workflow.

This includes showing:

• What becomes automated

• What remains human-controlled

• Where AI is used

• Where approvals are required

• How exceptions are handled

• How users receive alerts or recommendations

• How data moves between systems

• How outcomes are captured

The future-state workflow is designed around business value and usability, not just technical capability.

Deliverables:

• Future-state workflow map

• Proposed automation points

• Human-review checkpoints

• Exception-handling process

• Recommended user experience

Day 7: Solution architecture

I create the technical architecture for the proposed implementation.

Depending on the solution, the architecture may include:

• Source systems

• APIs

• Middleware

• Event flows

• Data storage

• Data pipelines

• Feature store

• AI models

• Large language models

• Retrieval-augmented generation

• Vector databases

• Workflow engines

• Dashboards

• Authentication

• Monitoring

• Governance

• Audit logging

The architecture shows how the major components work together and where each responsibility sits.

Deliverables:

• High-level architecture diagram

• Component description

• Data-flow diagram

• Integration pattern

• Environment strategy

• Security boundaries

Day 8: Use-case prioritization and MVP scope

Most organizations identify more opportunities than can reasonably be implemented in the first phase.

I help the client prioritize use cases based on:

• Business impact

• Technical feasibility

• Data readiness

• Implementation complexity

• Risk

• User adoption

• Time to value

• Cost

• Reusability

The result is a clearly defined minimum viable implementation that is large enough to create business value but focused enough to deliver successfully.

Deliverables:

• Prioritized use-case list

• Impact-versus-effort matrix

• Recommended MVP scope

• Deferred-use-case list

• Phase-one acceptance criteria

Day 9: Security, governance, and risk review

Before implementation, I identify the controls needed to operate the solution responsibly.

This may include:

• Role-based access

• Data minimization

• Encryption

• Audit trails

• Model explainability

• Human oversight

• Data retention

• Vendor risks

• Privacy requirements

• Bias monitoring

• Approval workflows

• Rollback procedures

• Business continuity

• Incident response

For healthcare or regulated environments, this step also considers how sensitive information should be handled and which uses may require additional legal, compliance, or security review.

Deliverables:

• Risk register

• Security-control recommendations

• Governance requirements

• Compliance considerations

• Human-oversight model

• Approval and escalation process

Day 10: Roadmap, estimate, and executive readout

The final day brings all findings together into a clear implementation plan.

I present:

• What should be built

• Why it should be built

• How it will work

• What is included

• What is not included

• What dependencies must be resolved

• What the implementation phases should be

• How long each phase is expected to take

• What resources are required

• What the estimated implementation cost will be

• How success will be measured

The goal is to give the client enough clarity to approve, budget, and begin implementation with fewer surprises.

Deliverables:

• Executive discovery report

• Recommended solution architecture

• Final MVP scope

• Implementation roadmap

• Workstream plan

• Resource requirements

• Cost estimate

• Risk and dependency register

• KPI framework

• Executive presentation

Final discovery deliverables

At the end of the two weeks, the client receives:

• Executive summary

• Business-problem definition

• Current-state workflow

• Future-state workflow

• Stakeholder and user-needs analysis

• Systems inventory

• Integration assessment

• Data-readiness assessment

• Data-quality findings

• Use-case prioritization matrix

• MVP definition

• Solution architecture diagram

• Data-flow diagram

• Security and governance recommendations

• Risk register

• Implementation roadmap

• Estimated timeline

• Implementation cost range

• KPI and success-measurement plan

• Executive readout session

Pricing breakdown

Why the discovery phase costs $21,000

The client is not paying for ten days of meetings.

The $21,000 fee covers a concentrated architecture and strategy engagement that combines business analysis, process design, systems integration, data assessment, AI solution design, security, governance, cost planning, and executive decision support.

Without discovery, organizations often begin implementation with unclear requirements, incomplete data, hidden integration problems, unrealistic timelines, and no agreement on success metrics. These issues can cause expensive rework, delays, scope disputes, and failed AI initiatives.

The discovery phase reduces those risks before the client commits to a larger implementation budget.

A $75,000 or $150,000 implementation can easily waste more than $21,000 if the wrong use case is selected, data is unavailable, systems cannot connect as expected, or users are not prepared to adopt the solution.

The discovery engagement gives the client a clear answer to four important questions:

1. Is the solution feasible?

2. Is the data ready?

3. What exactly should be built?

4. What will it take to implement successfully?

The strongest way to explain the price is:

The $21,000 discovery phase turns an AI or integration idea into an implementation-ready plan. It gives the client a validated business case, future-state workflow, technical architecture, data assessment, risk analysis, implementation roadmap, and reliable cost estimate before larger development begins.

Recommended payment structure

• 50% at kickoff: $10,500

• 25% at the end of week one: $5,250

• 25% upon delivery of the final discovery package: $5,250

Recommended scope boundaries

The $21,000 discovery phase should include:

• One primary business use case

• Up to five stakeholder interview sessions

• Review of up to five major systems

• Initial assessment of available data sources

• One future-state architecture

• One MVP recommendation

• One executive readout

The discovery phase should not include:

• Production development

• Full data migration

• Custom API development

• Production model training

• Dashboard implementation

• Enterprise-wide security certification

• Detailed legal or regulatory advice

• Ongoing program management

• Vendor licensing costs

These activities can be estimated and proposed after discovery is complete.