Data rules before model selection
Data governance starts with source classification, retention needs, model access, and logging rules before choosing a vendor or building an agent.
AI security and compliance
Secure and compliant AI adoption for teams that cannot afford black boxes
Prioritize one practical use case, confirm data and policy constraints, and deploy agents, copilots, or automation with guardrails, audit trails, and human review built into the operating model.
30-minute technical discussion about your AI goals, data boundaries, and controls
- Data governance and guardrails before model selection
- Controls mapped to SOC 2, HIPAA, CMMC, or your internal framework
- Deploy in your environment when sensitive workloads require it
- Knowledge transfer so your team owns the system
Quick Answer
What is secure AI adoption?
Secure AI adoption means choosing one useful workflow, defining data boundaries and review rules first, then building the agent, copilot, or automation with guardrails, audit trails, rollback paths, and team ownership in place.
Who this applies to
Security-conscious product, engineering, legal, and operations teams
Timeline
Start with one scoped workflow before expanding to a broader AI program
Investment
Depends on data access, model choice, integrations, review risk, and ownership
Start here
Decide which AI use case can pass security review and still ship.
The first conversion path is a technical scoping call for one workflow. The secondary path is the phased plan if security, legal, engineering, and finance need the same view before booking.
AI pilot planner
Use the starter engagement to sort use case, data risk, and owner fit.
Lower-intent teams can review the phased pilot plan first. High-intent teams can book the same AI pilot scoping path from here.
Security and compliance spine
Govern AI before it touches sensitive work
A useful pilot needs clear data boundaries, review paths, and ownership. We turn those decisions into controls your security, legal, and engineering teams can inspect before broader adoption.
Guardrails and human-in-the-loop
Use prompt policies, approval steps, abuse checks, and rollback paths for workflows where an AI output can affect a customer, employee, or compliance record.
Auditability and responsible AI
Capture decisions, review checkpoints, evaluation results, and traceable audit trails so stakeholders can see how the system behaves and where its limits are.
Deploy in your environment
For sensitive workloads, model traffic and storage can stay in your VPC or controlled environment, subject to final architecture and operations choices.
Controls mapped to your framework
Map the pilot to SOC 2, HIPAA, internal security policy, procurement review, or another framework your team already uses. CMMC can be included when it applies, but this page is for broad secure AI adoption, not a defence-only compliance project.
What We Deliver
Production support under the security model
Once the controls are clear, we build the AI pilot around real workflows, vendor-flexible architecture, and operating practices your team can sustain.
AI Copilots & Agents
Internal copilots for support, RevOps, compliance, or engineering workflows. RAG pipelines, guardrails, and human-in-the-loop checkpoints so outputs stay accurate and auditable.
Workflow Integration
Embed AI into CRM, ticketing, ERP, and DevOps pipelines. We train staff, update runbooks, and measure adoption so the automation sticks.
Model Operations
Evaluation harnesses, monitoring, and observability around public APIs or self-hosted models. Built to swap providers as the market shifts.
Responsible AI & Ethics
Bias detection, prompt safety, data governance, and transparent decision-making. Every deployment includes ethical guardrails aligned to your industry and values.
Architecture That Lasts
Vendor-neutral architecture your team can own
Models and frameworks change quickly. We architect for portability, responsible operation, and vendor flexibility so you can capture value now without locking into one provider.
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Vendor-Flexible Architecture
Abstraction layers let you swap between OpenAI, Anthropic, open-source, or self-hosted models as pricing, capabilities, and regulations shift.
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On-Premise & Private Deployment
Deploy Llama, Mistral, Phi, Gemma, or Granite in your VPC for sensitive workloads. Full control over data residency and access.
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Ethics & Governance Built In
Bias detection, prompt safety policies, human-in-the-loop controls, and transparent audit trails are planned into each deployment.
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Team Enablement & Transfer
Role-based training, governance policies, prompt libraries, and adoption dashboards so your team can iterate without us.
Not Sure What's Worth Investing In?
Walk through your use cases, data posture, and constraints with an AI architect. We will help you evaluate use cases by expected impact, risk, and implementation effort.
From our consulting clients
Workflow has been great. We generally hold a few meetings as needed and communicate via Slack otherwise.
Craig Lathrop
Managing Partner
Dedication and willingness to go the extra mile even when challenges came up on our end.
Christian Manco
Former Director
Nelson did a great job at figuring out numerous things specific to our setup, resolving unforeseen problems as they arose. He provided further guidance and advice on things outside of the original scope as well.
Gregory Sparrow
Lead, Software Engineering
The attention to detail and commitment to the process is admirable.
Craig Lathrop
Managing Partner
Their understanding and experience with the AWS suite of products and solutions were impressive.
Tony La
CTO
Pilotcore made a number of suggestions about architecture which greatly improved security and redundancy.
Craig Lathrop
Managing Partner
Nelson was awesome to work with. He came in and became a great partner to our lead engineer, helped architect a sustainable solution, and then handed over everything smoothly. Great communicator and his senior experience helps get things done right the first time.
Trevor Wolfe
CEO, Founder
Nelson quickly understood our requirements and made it extremely easy to get started with the project. He delivered the project on time and with excellent documentation.
Kristian Lunde
CTO
The project was delivered on time, and the agreed-upon scope was implemented fully.
Gregory Sparrow
Lead, Software Engineering
The level of competence was obvious after just a single meeting.
Craig Lathrop
Managing Partner
All of our VMs and databases have been deployed without issue. The structured setup has been very robust.
Craig Lathrop
Managing Partner
Our staging environment was set up in its entirety in AWS, including ECS, CloudFront, load balancing, Fargate, cron jobs, etc. Our app was 100% functional in the new infrastructure.
Gregory Sparrow
Lead, Software Engineering
The cloud migration was a success and did not impact production operations. Infrastructure is now managed via code, and the internal development team was empowered to extend and add to the code base.
Tony La
CTO
A project manager was assigned to the project and put in charge of monitoring deliverables and communication. Pilotcore always delivered on time on the items assigned to them and was always responsive to inquiries and requests.
Christian Manco
Former Director
Workflow has been great. We generally hold a few meetings as needed and communicate via Slack otherwise.
Craig Lathrop
Managing Partner
Dedication and willingness to go the extra mile even when challenges came up on our end.
Christian Manco
Former Director
Nelson did a great job at figuring out numerous things specific to our setup, resolving unforeseen problems as they arose. He provided further guidance and advice on things outside of the original scope as well.
Gregory Sparrow
Lead, Software Engineering
The attention to detail and commitment to the process is admirable.
Craig Lathrop
Managing Partner
Their understanding and experience with the AWS suite of products and solutions were impressive.
Tony La
CTO
Pilotcore made a number of suggestions about architecture which greatly improved security and redundancy.
Craig Lathrop
Managing Partner
Nelson was awesome to work with. He came in and became a great partner to our lead engineer, helped architect a sustainable solution, and then handed over everything smoothly. Great communicator and his senior experience helps get things done right the first time.
Trevor Wolfe
CEO, Founder
Nelson quickly understood our requirements and made it extremely easy to get started with the project. He delivered the project on time and with excellent documentation.
Kristian Lunde
CTO
The project was delivered on time, and the agreed-upon scope was implemented fully.
Gregory Sparrow
Lead, Software Engineering
The level of competence was obvious after just a single meeting.
Craig Lathrop
Managing Partner
All of our VMs and databases have been deployed without issue. The structured setup has been very robust.
Craig Lathrop
Managing Partner
Our staging environment was set up in its entirety in AWS, including ECS, CloudFront, load balancing, Fargate, cron jobs, etc. Our app was 100% functional in the new infrastructure.
Gregory Sparrow
Lead, Software Engineering
The cloud migration was a success and did not impact production operations. Infrastructure is now managed via code, and the internal development team was empowered to extend and add to the code base.
Tony La
CTO
A project manager was assigned to the project and put in charge of monitoring deliverables and communication. Pilotcore always delivered on time on the items assigned to them and was always responsive to inquiries and requests.
Christian Manco
Former Director
Workflow has been great. We generally hold a few meetings as needed and communicate via Slack otherwise.
Craig Lathrop
Managing Partner
Dedication and willingness to go the extra mile even when challenges came up on our end.
Christian Manco
Former Director
Nelson did a great job at figuring out numerous things specific to our setup, resolving unforeseen problems as they arose. He provided further guidance and advice on things outside of the original scope as well.
Gregory Sparrow
Lead, Software Engineering
The attention to detail and commitment to the process is admirable.
Craig Lathrop
Managing Partner
Their understanding and experience with the AWS suite of products and solutions were impressive.
Tony La
CTO
Pilotcore made a number of suggestions about architecture which greatly improved security and redundancy.
Craig Lathrop
Managing Partner
Nelson was awesome to work with. He came in and became a great partner to our lead engineer, helped architect a sustainable solution, and then handed over everything smoothly. Great communicator and his senior experience helps get things done right the first time.
Trevor Wolfe
CEO, Founder
Nelson quickly understood our requirements and made it extremely easy to get started with the project. He delivered the project on time and with excellent documentation.
Kristian Lunde
CTO
The project was delivered on time, and the agreed-upon scope was implemented fully.
Gregory Sparrow
Lead, Software Engineering
The level of competence was obvious after just a single meeting.
Craig Lathrop
Managing Partner
All of our VMs and databases have been deployed without issue. The structured setup has been very robust.
Craig Lathrop
Managing Partner
Our staging environment was set up in its entirety in AWS, including ECS, CloudFront, load balancing, Fargate, cron jobs, etc. Our app was 100% functional in the new infrastructure.
Gregory Sparrow
Lead, Software Engineering
The cloud migration was a success and did not impact production operations. Infrastructure is now managed via code, and the internal development team was empowered to extend and add to the code base.
Tony La
CTO
A project manager was assigned to the project and put in charge of monitoring deliverables and communication. Pilotcore always delivered on time on the items assigned to them and was always responsive to inquiries and requests.
Christian Manco
Former Director
Phased Enablement
From Exploration to Production in Three Phases
Technical build + workflow integration + people readiness. Share this with finance, legal, and engineering so everyone has the same view of effort and deliverables. Duration varies based on scope, data readiness, and compliance requirements.
PHASE 1
Discover & validate
- Data readiness, privacy, and policy review
- Agent/automation proofs-of-concept with real data
- Evaluation harness + success metrics defined
- Executive briefing with KPIs
PHASE 2
Build & integrate
- APIs, automations, and guardrails deployed
- RAG pipelines and observability operational
- Ethics review + compliance narrative
- Incident response playbooks delivered
PHASE 3
Train & transfer
- Role-based training, SOPs, and prompt libraries
- Feedback loops + model evaluation cadence
- Adoption dashboard + backlog handed to owners
- Hypercare period begins
Want to Start Small? Try a Discovery Sprint
Not ready for a full program? Start with a focused 2-4 week discovery sprint to validate a single use case and build internal confidence before expanding scope.
Plan Your AI PilotStakeholder Confidence
What Every Stakeholder Needs to Know About Your AI Pilot
Each group cares about different risks. Here is how each role can evaluate the pilot on its own terms.
Executive & Board
Invest wisely in a shifting landscape
- Executive-ready roadmap with phase gates before scaling spend.
- Vendor-flexible architecture that avoids lock-in to any single model or platform.
- Responsible AI policy, privacy documentation, and ethics framework packaged up.
- Monthly adoption dashboards + quarterly business reviews.
Engineering & Ops
Ship safely with the stack you own
- Python/TypeScript reference repos + IaC modules delivered.
- Incident + change management playbooks aligned with SRE.
- Handoff for RAG pipelines, eval harnesses, and monitoring.
- Pairing / enablement so teams can extend automations solo.
Security, Legal & Ethics
No surprises in audits, contracts, or ethics reviews
- Data residency + retention matrix for each model choice.
- Human-in-the-loop controls mapped to SOC 2 / HIPAA / CMMC.
- Bias detection, prompt safety, and transparent decision audit trails.
- Procurement packages for third-party or self-hosted models.
Common buyer questions
AI adoption questions we answer every week
What AI models do you work with?
We work across the full spectrum: public APIs (OpenAI, Anthropic Claude, Grok), open-source models (Llama, Mistral, Phi, Gemma, Granite) via Ollama or self-hosted inference, and domain-specific fine-tuned models. We recommend the right model for each use case based on security, latency, cost, and compliance requirements -- and architect for portability so you can switch as the market evolves.
AI is changing so fast -- how do you avoid building something that's obsolete in six months?
This is exactly what we help enterprises navigate. We architect abstraction layers so you're not locked into any single model or vendor. When a better model launches or pricing shifts, your system adapts without a rewrite. We focus on durable patterns -- evaluation harnesses, governance frameworks, and integration architecture -- that outlast any specific model generation.
How do you approach AI ethics and responsible AI?
Ethics and safety controls are scoped at kickoff and revisited through delivery, with documented review checkpoints. We implement bias detection, prompt safety policies, human-in-the-loop controls for high-stakes decisions, and transparent audit trails.
Do we need our own data science team to work with you?
No. We bring the AI engineering expertise and pair with your existing engineering, product, or ops teams. Our goal is knowledge transfer -- by the end of the engagement, your team has the runbooks, prompt libraries, and evaluation harnesses to maintain and extend the system without us.
How do you handle data privacy and security with AI models?
Every engagement starts with a data governance review. For sensitive workloads, we deploy models in your VPC using self-hosted inference -- for self-hosted deployments, model traffic and storage can remain inside your controlled environment, subject to final architecture and operations choices. For public API integrations, we implement guardrails, prompt logging, and abuse detection. All architectures are mapped to your compliance framework (SOC 2, HIPAA, CMMC).
Can you integrate AI into our existing enterprise tools and workflows?
Yes -- that is where most of the value comes from. We embed AI into CRM, ticketing, ERP, and DevOps pipelines using Python, TypeScript, or bespoke APIs. The automation sticks because we update runbooks, train staff, and measure adoption as part of the project plan.
Can we start with a smaller engagement before committing to a full program?
Absolutely. Our discovery phase is designed as a standalone deliverable: you get use-case scoring, a working proof-of-concept, and an executive briefing with KPIs. Many organisations use this to build internal confidence and secure stakeholder buy-in before expanding scope.
Before you book
What the AI pilot conversation should settle.
Fit, implementation effort, next step, and proof limits all depend on the use case, data access, policy risk, and team ownership model.
Fit
One workflow with a real owner.
Best fit when a leader can name the workflow, data source, review risk, and business decision the pilot should improve.
Effort
Data and controls come first.
Implementation effort depends on data quality, privacy constraints, model choice, integration points, and who owns review and rollback decisions.
Next step
Leave with a pilot scope.
The scoping call should decide whether to run discovery, build a proof of concept, or pause until policy and data access are ready.
Proof limits
A pilot is not an enterprise rollout.
AI adoption outcomes depend on baseline process, data access, user behavior, governance, and operational follow-through.
Self-Assessment
Navigating the AI Landscape?
The AI space moves fast. Talk through your use cases, constraints, and concerns with an architect who can help you invest wisely -- no commitment required.
Schedule a Discovery Call30-minute technical discussion with a senior architect
Ready to Integrate AI Into Your Operations?
Start with a focused discovery sprint or dive into a full program. Either way, you get hands-on architects who document decisions, transfer ownership, and define measurable checkpoints for adoption.