A direction you can explain
Identify a useful starting point, compare the approaches, and make the costs and tradeoffs clear. You have a recommendation you can take to leadership and a plan colleagues can help shape.
AI strategy / Architecture / Implementation
Redwood City, CaliforniaYour people
know the business.
Build AI around them.
The starting point
Your knowledge.
Your judgment.
Your next chapter.
Your team brings the judgment, relationships, and experience that make your business work. I help you put AI alongside that expertise—connecting it to the right knowledge, choosing where it can act, and building systems your people can confidently use and direct.
A clear plan for leadership. Practical support for your team.More time for the work that matters.
Try the idea / An illustrative workflow
Same question.
A better starting point.
“Help me prepare for this customer’s visit.”
Select the information the assistant may use. See what changes in the preparation brief.
AI assistanceOrganize a preparation briefUse selected sources. Leave missing details open.
Scripted illustration using fictional details. No customer data, model calls, or messages are sent.
2. Inspect the differenceExample brief
Before the visit
No business sources selected. Start with a general checklist.
Prepare
Find the approved procedure for the customer’s equipment.
Source not connectedUnderstand
Ask about previous work and any unresolved questions.
Source not connectedCoordinate
Confirm the visit window and who is attending.
Source not connected3. Keep the person in the decision
The assistant can prepare a follow-up. Sending it is a separate permission.
- Prepare draft
- Human review
- Simulated handoff
“Thanks for arranging a service visit. Our team will review the relevant service information and confirm the appointment details with you.”
In a real workflow, a colleague checks the facts, recipient, and wording before approving.
Draft only. A person must review before a handoff.
01 / A useful perspective
The model is a starting point.
Your business gives it context.
Useful AI connects a defined job to the right information, familiar tools, and clear responsibility.
Explore the parts below. The right combination depends on your team, your information, and the work ahead.
What sits behind a useful assistant?
Select a part of the system. Each has a job—and a decision worth making deliberately.
01Your peopleSet the goal. Apply judgment.
People define useful outcomes and decide when the system needs help.
Ask: Who owns the workflow and reviews difficult cases?
02Familiar toolsMeet the team where work happens.
A chat tool, service desk, or existing application can be the interface. A new portal is not always necessary.
Ask: Where would this help fit naturally into the day?
03AI assistancePrepare, find, organize, suggest.
The model helps with a defined job. Its output still needs evaluation, and sometimes human review.
Ask: What is it allowed to do, and how will we test quality?
Connected throughout the workflow
↔Approved knowledgeRelevant information, within reach.
Connect the sources the user is allowed to access. Keep source material available so answers can be checked.
Ask: Which information is current, useful, and permitted here?
↔Permissions & reviewKeep responsibility explicit.
Define access, approved actions, escalation, and activity records. Controls support the workflow rather than being bolted on afterward.
Ask: What requires a person’s approval before it happens?
An illustrative architecture—not a prescribed vendor stack. Existing products, private infrastructure, and hybrid approaches each have a place.
02 / A partner in the work
A direction you can explain.
A team ready to move.
You know the priorities and the people. I bring the technical judgment and hands-on support to help you make a recommendation, build confidence, and put the plan into practice.
A system shaped around your business
Connect the right information and tools to a defined workflow. Decide what stays private, who has access, and where people remain involved. The architecture follows your needs, not a preference for a particular provider.
A team ready to use it
Test the workflow with the people who will rely on it. Document how it works, how to recognize problems, and who takes responsibility after handoff. Adoption and day-to-day operation are part of the work.
03 / Selected experience
Technical depth.
Practical application.
My work as co-founder and Chief AI Officer of Mill Pond Research spanned AI product development, organizational workflows, and the controls that connect the two.
The common task: make a technical capability useful in the setting where people actually work.
Co-inventor / Granted 2025U.S. Patent 12,332,878 B1 ↗Secure cross-platform orchestration and knowledge management of machine-learning models.Opens in a new tabXilos & WorkBench
AI governance and orchestration alongside a workspace for building and using agents. The work connected model access, policy controls, and how teams use AI in practice.
Working within an established process
Worked on AI-assisted email content for a banking client using established templates. The goal was to help the content team prepare and adapt copy within its existing design process, rather than introduce a separate workflow to manage.
Coordinating AI assistants
Designed an internal system for assigning work to AI assistants across research, content, sales, and engineering. Task tracking and escalation kept people involved in decisions that required judgment.
Preparing for a rigorous review
Developed NIST-aligned AI safety analysis and documentation mapping federal acquisition requirements. Work focused on preparing materials for evaluation, not claiming a certification or guaranteed approval.
Designing around sensitive information
Scoped workflows with boundaries around protected health information, activity records, and human approval points. This was architecture work, not a production deployment or a claim of compliance certification.
Organizing the work behind outreach
Built prospect research, outreach sequences, and resources for answering buyer questions. The work explored how AI could support preparation and coordination within a sales process.
The most valuable thing technology can give people is time to do what only they can do.
Pete Shimshock04 / A personal perspective
Technology should
expand what people can do.
I have spent the past 15 years building technology with that purpose in mind.
Born in New York, raised in Silicon Valley, and educated in the South, I bring a belief that useful technology should give people more agency over their work and their time. That is the standard I bring to both client engagements and my own experiments.
Ideas tested in practice
Personal projects in development are a place to test approaches before recommending them elsewhere.
Research & market intelligence
Exploring how AI can gather new information and organize it into useful briefs, with source material available for review. The practical question: how can a team spend less time collecting updates and more time deciding what matters?
Voice-first interaction
Working on local transcription and ways to direct software through speech. The practical question: where can a simpler interface reduce friction without giving up control of sensitive information?
Organizations I’ve worked with
Client, partner, reseller, and consortium work through Mill Pond Research, 2023–2026. Logos indicate organizations engaged in that work; they are not endorsements.
- NISTAI Risk Management Framework · U.S. AI Safety Institute Consortium member
- U.S. PatentCo-inventor — Secure cross-platform orchestration and knowledge management of machine-learning models
- TechCrunch DisruptLaunch — Xilos on stage at Disrupt
- DoDStrategic agreement — defense-channel deployment of governed AI
05 / Working together
Support for the
decision ahead.
You do not need a finished technical brief to begin. Bring the priorities, questions, and constraints you are working with. We can define a useful starting point and agree on scope, fees, and responsibilities before work begins.
AI strategy review
Build a shared direction.
Compare realistic approaches with your stakeholders. Make costs, dependencies, and the next decision clear.
You take a recommendation to leadership—not another list of tools.
Discuss a review ↗Illustrative deliverable / Not a client document
Decision brief
- Opportunity
- Help colleagues prepare for service visits.
- Options to compare
- Extend an existing tool or build a focused assistant.
- Open decisions
- Information access, team capacity, and operating cost.
- Next step
- Agree on a bounded pilot and how to evaluate it.
Build sprint
Make the idea testable.
Build one agreed workflow with the people who will use it. Evaluate quality, information access, and human review before expanding.
You have evidence of what it can do—and where it still needs work.
Discuss a sprint ↗Illustrative deliverable / Not test results
Evaluation record
- Source accuracy
- Does the brief reflect the approved records?
- Missing information
- Does it flag gaps rather than invent an answer?
- Access boundaries
- Can a user retrieve only what they are permitted to see?
- Human review
- Is approval required before the handoff?
Fractional AI leadership
Keep the work moving.
Connect leadership priorities to technical decisions. Maintain the roadmap and help colleagues explain progress and tradeoffs.
A capability your team understands, with clear responsibility for what comes next.
Discuss ongoing support ↗Illustrative deliverable / Not a client document
Operating handoff
- Ownership
- Named workflow owner and escalation contact.
- Day-to-day use
- Guidance for colleagues and human review points.
- Maintenance
- Source updates, access reviews, and evaluation cadence.
- Next decisions
- A maintained roadmap and agreed expansion criteria.
A few useful questions
Do we need to build our own AI system?
Not necessarily. Existing products, hosted models, private infrastructure, and hybrid approaches each have a place. The choice depends on the work, the sensitivity of the information, operating costs, and what your team can maintain. We start with those requirements, not a predetermined stack.
What happens to our data?
That depends on the provider, product, contract, and configuration. We identify what information is needed, where it will travel and be stored, who can access it, and what retention or training terms apply. Sensitive work may call for tighter controls or private hosting. Those choices should be explicit before the system is used.
Will this replace the way our team works?
The starting point is what your team wants to improve. Some tasks may change or become automated; that needs to be considered with the people doing the work. The aim is a useful capability that colleagues understand and can direct—not another tool imposed without a plan for using it.
How do we know it is working?
Agree on an initial baseline and success criteria for the chosen workflow. Depending on the work, that might include task quality, turnaround time, review effort, or cost. Test those measures before expanding the scope.
What remains with us after the engagement?
The agreed handoff covers documentation, access, operating responsibilities, and what your team needs to maintain the system. Ownership, pre-existing IP, and third-party licensing are addressed in the written scope before work begins.
Let’s begin with what matters to you
What would you like your team to do better?
A process that takes too much time. Knowledge that is hard to find. An AI initiative that needs a clear direction. Tell me what you are working toward and the constraints that matter.
hello@peteshimshock.comBased in Redwood City.
On-site across the Peninsula. Remote collaboration.