Frontier model APIs
Use managed models when capability, multimodal support, and speed to value matter most. I help scope the provider, guardrails, routing, and operational shape around it.
Consulting engagements
AI infrastructure and automation, built around the right model strategy.
I help teams design and ship AI systems using frontier model APIs, private or local LLM clusters, or hybrid architectures that combine both, based on the constraints that actually matter to the business.
- Review your goals, current environment, and delivery constraints at a high level.
- Identify whether the next step should be a proposal, architecture assessment, or implementation engagement.
- If deeper analysis is needed, scope the assessment before substantive architecture work begins.
Private and local clusters
Use self-hosted or VPC-isolated inference when ownership, compliance, air-gapped access, latency control, or cost at scale pushes the workload private.
Hybrid AI architectures
Route sensitive workloads to private models and general workloads to frontier APIs. Hybrid is often the practical answer when requirements conflict.
How engagements start
Free fit call first. Paid architecture work only after the scope is clear.
The initial consultation is for fit and scoping. Proposal conversations are part of the sales process. Substantive technical discovery and architecture work starts only after we agree on the paid scope.
01
Free initial consultation
A 30-minute conversation to understand the problem, constraints, fit, and likely next step. It is intentionally a scoping conversation, not a full architecture assessment.
02
Proposal and scope review
If there is a fit, we can review the proposal, assumptions, scope, and engagement shape without turning the sales conversation into a separate billable consulting session.
03
Paid assessment or delivery
Detailed discovery, architecture diagrams, target-state design, roadmaps, estimates, workshops, and advisory work are scoped as paid consulting—often as a fixed-price assessment—before implementation.
Pricing philosophy
Reduced pricing is available for selected case-study engagements.
For selected engagements, an agreed anonymized case study can reduce the project fee by about 20%. Case-study scope and publication approval are documented separately in writing; fully private engagements use the standard rate.
What the case study includes
Architecture decisions, implementation approach, and supportable outcomes. Client-identifying details, confidential information, and measurable claims are published only within the separately agreed approval scope.
Why the pricing is lower
The discount reflects the value of a publishable, approved case study without giving away half the engagement fee. The current package rates are generally about 20% below the corresponding standard rate.
When custom scoping applies
If the work cuts across multiple packages or needs phased delivery, the free consultation can lead to a scoped architecture assessment before implementation rather than forcing the work into a mismatched template.
Packages
Five ways to engage, depending on what you need to prove or ship.
These are paid outcome-oriented engagements, not hourly call bundles. When the architecture itself needs to be resolved first, a standalone fixed-price assessment can precede implementation.
Diagnostic & entry point1 week
AI Automation Audit
A deep-dive technical audit of your existing automation flows, LLM prompts, tool integrations, and data boundaries to identify immediate optimizations.
- Case study rate
- $1,200-$1,600
- Standard rate
- $1,500-$2,000
Best for
Teams with existing automation or AI workflows that want to identify bottlenecks, security risks, and scaling opportunities.
- Review of existing n8n/Make workflows, scripts, or agent prompts
- Data leakage, credentials, and security posture inspection
- Latency and cost-reduction opportunities report
- Prioritized roadmap of high-value automation opportunities
Fastest path to value3-4 weeks
AI Ops Starter
A working AI automation stack built around n8n and an LLM backbone that fits your requirements, whether that means frontier APIs, private inference, or a hybrid route.
- Case study rate
- $3,600-$4,800
- Standard rate
- $4,500-$6,000
Best for
Teams that want one real workflow in production with the right model layer behind it.
- One business tool integration
- One agentic workflow mapped to a real process
- Deployment to cloud, self-managed infrastructure, or on-prem
- Scoped data-handling recommendations for your environment
Advanced engagement4-6 weeks
Agent Mesh
A multi-agent system using MCP patterns so models can inspect and act on infrastructure safely, with explicit approvals for mutating operations and a clean audit trail.
- Case study rate
- $6,400-$9,600
- Standard rate
- $8,000-$12,000
Best for
Platform and DevOps teams that want AI agents with scoped, auditable access to real infrastructure.
- Scoped tool access for AWS, Azure, Kubernetes, Terraform, and internal APIs
- Dry-run-first mutating workflows
- Audit logging and approval gates
- Model-agnostic routing across frontier, local, or hybrid stacks
Ownership first6-8 weeks
LLM Private Cloud
A full private deployment on client-owned or isolated infrastructure with model selection, inference endpoints, observability, and retrieval over internal knowledge sources.
- Case study rate
- $11,200-$16,000
- Standard rate
- $14,000-$20,000
Best for
Teams that need to own the model runtime, data path, and deployment environment end to end.
- Air-gapped or VPC-isolated deployment patterns
- Inference endpoint and runtime configuration
- RAG over internal knowledge sources
- Observability, CI/CD integration, and documentation
Tailored engagementScoped after consultation or assessment
Custom AI Architecture
Custom-scoped engagements for frontier integrations, private infrastructure, hybrid routing, platform hardening, workflow automation, or existing AI systems that need production architecture.
- Case study rate
- Custom
- Standard rate
- Custom
Best for
Teams with unclear architecture direction, migrations in flight, or requirements that cross multiple systems.
- Scoping for mixed frontier and private workloads
- Security, observability, and handoff planning
- Support for existing Kubernetes, cloud, or internal platforms
- A delivery shape aligned to your team and constraints
At a glance
Package comparison table
A quick scan of deployment shape, timeline, and pricing range.
| Package | Best deployment shape | Timeline | Case study rate | Standard rate |
|---|---|---|---|---|
| AI Automation Audit | Diagnostic audit of existing automation flows, prompts, and APIs | 1 week | $1,200-$1,600 | $1,500-$2,000 |
| AI Ops Starter | Cloud, self-managed, or on-prem with frontier, private, or hybrid model routing | 3-4 weeks | $3,600-$4,800 | $4,500-$6,000 |
| Agent Mesh | Model-agnostic agent layer across cloud or hybrid environments | 4-6 weeks | $6,400-$9,600 | $8,000-$12,000 |
| LLM Private Cloud | Air-gapped or VPC-isolated inference and retrieval stack | 6-8 weeks | $11,200-$16,000 | $14,000-$20,000 |
| Custom AI Architecture | Frontier, private, hybrid, or existing client infrastructure | Scoped after consultation or assessment | Custom | Custom |
Not a fit
This is not the right engagement for every team.
I would rather say no early than sell you the wrong shape of work.
You need full-time staff augmentation rather than a scoped consulting engagement.
You want a long-term operator instead of a team handoff and internal ownership plan.
Your timeline is under three weeks and there is no room for discovery or production hardening.
Selected experience
Representative delivery work from prior professional experience.
These anonymized examples reflect prior engineering delivery. Claims are limited to what can be supported without exposing client-confidential details.
These examples come from prior professional work. New independent consulting engagements are presented separately and only with the client's approval.
On-Premise LLM for Financial Services
40% lower inference cost
Challenge
A financial services organization needed LLM capability without exposing proprietary data to commercial APIs under strict compliance constraints.
Approach
Designed and deployed an air-gapped LLM environment on client-owned infrastructure, including a custom retrieval layer over internal documents and a scoped tool-access pattern.
Outcome
Compared with commercial API usage, the deployment reduced inference cost by 40% while keeping full data residency compliance intact.
AWS Platform Consolidation for Multi-Tenant Operations
Lower per-site cost and less operational overhead
Challenge
Roughly 12 independently hosted WordPress properties were each running on separate EC2 instances, driving up cost and creating inconsistent security and maintenance practices.
Approach
Migrated the portfolio into a consolidated AWS ECS Fargate platform with Redis, EFS, ALB, CloudFront, WAF, Shield Pro, and New Relic observability.
Outcome
The result reduced per-site infrastructure cost and operational overhead while improving availability and security posture across all properties.
See how I build
A look at the implementation mindset behind the consulting.
Watch how I approach local inference, infrastructure setup, and developer-facing AI workflows in practice. The point is transparency, not theater.
Book the free initial consultationProcess
A scoped path from assessment to handoff.
After the free consultation and proposal stage, substantive technical work starts inside an agreed paid scope. When architecture needs to be resolved first, the assessment is a standalone outcome before proof-of-concept or implementation.
01
Architecture assessment (1-2 weeks)
When deeper analysis is needed, this paid phase reviews your stack, data boundaries, security constraints, target state, implementation path, and rough cost shape. The output is a written recommendation and architecture roadmap, not a sales recap.
02
Proof of concept (2-3 weeks)
A working deployment using your infrastructure, data, and integrations so you can evaluate the recommendation in a real environment before wider rollout.
03
Production handoff (1-3 weeks)
Runbooks, monitoring, CI/CD, and team transfer so the system can live with your engineers instead of staying dependent on the consultant.
Engineering principles
The consulting stance is simple: build what the requirement actually needs.
That includes saying when a frontier API is the better answer, when a private cluster is justified, and when hybrid routing is the cleanest compromise.
Right model, right boundary
The architecture follows the requirements, not my preference. Frontier when capability wins. Private when ownership wins. Hybrid when the real world needs both.
Auditability by default
Tool calls, pipeline actions, and delivery decisions should be inspectable by your team regardless of where the model runs.
Ownership at handoff
Code, config, workflows, and documentation belong to you. The goal is a usable system with a clean internal operator path.
No consultant lock-in
I do not optimize for dependency. I optimize for a production system your team can continue without me.
FAQ
Common questions before booking the call.
If you are still unsure whether this is a frontier, private, or hybrid problem, that uncertainty is normal and part of the early consulting value.
Can you build with OpenAI, Anthropic, Gemini, Azure OpenAI, or Bedrock?
Yes. The engagement can use frontier model APIs where they are the best fit. I help evaluate privacy, retention, vendor constraints, and routing so the provider choice matches the business and compliance context.
When should we use a private or local model instead?
When ownership, air-gapped access, compliance, latency control, or cost at scale dominate the tradeoff. Private inference is a requirement-driven choice, not a brand preference.
Can you design a hybrid setup that uses both?
Yes. Hybrid routing is often the best answer for teams with mixed workloads, where some requests belong on frontier models and others must stay private.
What if we do not know which architecture we need yet?
The free initial consultation is used to understand the problem and determine the right next step. If choosing the architecture requires deeper analysis, I scope a paid assessment before implementation; detailed comparisons, target-state design, diagrams, roadmaps, and estimates belong in that assessment rather than the free call.
Is the initial consultation free?
Yes. The first 30-minute consultation is free. Proposal review and scope clarification are not billed separately. Additional technical discovery, architecture workshops, advisory sessions, or working meetings are paid consulting unless they are already included in an accepted fixed-price engagement; scope is confirmed before that work begins.
Do we need GPU hardware already?
Not unless private inference is definitely part of the engagement. For many teams, the fastest starting point is a frontier API while private requirements are validated during a scoped assessment or implementation engagement.
Can you work with our existing Kubernetes or cloud environment?
Yes. Existing infrastructure is usually the preferred starting point. If new platform work is needed, that gets scoped directly into the engagement.
Next step
Book the free initial consultation to choose the right engagement.
We will review goals and constraints, confirm fit, and decide whether the next step is a proposal, a fixed-price architecture assessment, or implementation. Detailed architecture work begins after the paid scope is agreed.