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 current stack, data environment, and delivery constraints.
- Identify whether frontier, private, or hybrid architecture is the right direction.
- Leave with a written recommendation within 48 hours, whether or not we continue.
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.
Pricing philosophy
Below-market pricing is available when the work can become a case study.
If your engagement can be documented as an anonymized technical case study or build walkthrough, reduced pricing may apply. If you prefer the work to remain fully private, standard market pricing applies.
What the case study includes
Architecture decisions, implementation approach, and measurable outcomes, but not your company name, internal data, or anything you do not approve.
Why the pricing is lower
The trade is transparency. I get documented proof of the work. You get reduced pricing on a real production engagement.
When custom scoping applies
If the work cuts across multiple packages or needs phased delivery, I scope it after discovery rather than forcing it into a mismatched template.
Packages
Four ways to engage, depending on what you need to prove or ship.
These packages are designed to be easy to compare while still leaving room for the actual architecture recommendation to follow your constraints.
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
- $1,500-$2,500
- 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
Most requested4-6 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
- $3,000-$5,000
- 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
- $5,000-$8,000
- 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 discovery
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 Ops Starter | Cloud, self-managed, or on-prem with frontier, private, or hybrid model routing | 3-4 weeks | $1,500-$2,500 | $4,500-$6,000 |
| Agent Mesh | Model-agnostic agent layer across cloud or hybrid environments | 4-6 weeks | $3,000-$5,000 | $8,000-$12,000 |
| LLM Private Cloud | Air-gapped or VPC-isolated inference and retrieval stack | 6-8 weeks | $5,000-$8,000 | $14,000-$20,000 |
| Custom AI Architecture | Frontier, private, hybrid, or existing client infrastructure | Scoped after discovery | 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.
Case studies
Real delivery patterns, presented without invented clients or fake metrics.
These are based on actual work and presented with the level of specificity the current site can support honestly.
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.
Discuss your infrastructureProcess
A scoped path from recommendation to handoff.
The process is structured so the hard architectural decisions happen early and the production hardening is not treated as an afterthought.
01
Technical discovery (1 week)
Async review of your stack, current architecture, data sensitivity, delivery goals, and team constraints. Delivered as a written recommendation, 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?
That is exactly what the discovery phase is for. I review the constraints and deliver a written recommendation on whether frontier, private, or hybrid is the better path.
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 in discovery.
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 technical discovery call if you need the architecture decision before the build.
We will review your current stack, narrow the right engagement type, and decide whether frontier, private, or hybrid is the right direction.