AI Integration & LLM Architecture Consulting
A written AI-feature design for your next LLM build — retrieval, prompts, tool boundaries, evals, cost budget — argued out before your team commits to it.
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Outcome of this engagement
A written AI-feature design for your next LLM build — retrieval, prompts, tool boundaries, evals, cost budget — argued out before your team commits to it.
What you receive
- AI feature design document — retrieval strategy, context assembly, prompt structure, tool and agent boundaries, and where a human stays in the loop
- Evaluation plan — which dataset to build, which graders to write, and the regression gate that runs in CI before every prompt or model change
- Cost and latency budget — per-request token estimate, caching and model-tiering plan, and the thresholds that should raise an alert
- Failure-mode register — every way the feature can go wrong, the guardrail or fallback for it, and how you would notice in production
- 90-min review session with your engineers — we walk them through the design, take their pushback, and revise before it is frozen
Is this service the right fit for you?
Book this if you…
- You are adding an LLM feature to a product that already has real users, and the price of a wrong design is a rewrite
- You have a prototype that demos well and fails unpredictably, and you need to know what to fix before it becomes a product commitment
- Your team can build it — they just have not designed retrieval, evals, and failure handling for a production LLM system before
Don't book this if you…
- You want us to write the feature — this is design and review. If you need hands on the keyboard, hire engineers; we will help you brief them
- You want model training or ML research — our scope is product engineering on top of existing models, not building your own
- You still need an answer to "should we do AI at all" — that is a leadership decision, not an architecture one, and it belongs in a different conversation
Why This Service
Most AI features fail in the same few places: retrieval returns the wrong context, the agent has no stopping condition, and nobody can tell whether the last prompt change made things better or worse. We help you design the feature so those failure modes are visible to you before they are visible to your users.
Key Benefits
Retrieval That Returns the Right Context
Chunking, indexing, ranking, and context assembly designed around your data — the part that decides answer quality before the model is even called.
Evaluation Before Scale-Up
An eval suite and regression guards built on your own examples, so prompt and model changes get measured instead of argued about.
Cost and Latency as Design Constraints
Token budgets, caching, model tiering, and streaming decided at design time — not after the first invoice decides for you.
Failure Modes Handled on Purpose
Hallucination surfaces, tool-call errors, timeouts, and unsafe output get explicit fallbacks and guardrails instead of a retry loop.
What This Service Includes
- AI feature design sessions: RAG, agents, and tool-calling architecture
- Model selection and prompt / context design review
- Evaluation suite design: datasets, graders, regression gates in CI
- Cost and latency budgeting: caching, batching, model tiering
- Failure-mode and guardrail review: fallbacks, limits, data boundaries
How We Work Together
A straightforward engagement — from first call to measurable results.
Discovery Call
We discuss your goals, stack, and challenges. No commitment required — just a clear conversation.
Tailored Plan
We propose a focused engagement scope aligned with your team size, timeline, and actual needs.
Deliver & Follow Up
We execute the engagement and provide written findings, next steps, and optional follow-up support.
Who you'll be working with
Oleksii Anzhiiak
Software Architect, Senior .NET Engineer & Co-Founder
Currently leads architecture for ToyCRM.com — a multi-tenant CRM platform built on .NET by our team. The same patterns and design decisions used there appear directly in the courses: identity & auth, distributed services, code review culture. You learn from engineers actively shipping production code, not from a textbook.
Frequently Asked Questions
No. We design it with you and review what your team builds — architecture, evals, failure handling. Implementation stays with your engineers, because they are the ones who will operate it afterwards.
No. Model choice is a constraints problem: data boundaries, latency, cost, and the quality bar for your specific task. We help you compare the options against those constraints and keep the design portable enough to switch later.
Want to build this skill in-house instead?
Companies with engineering bandwidth sometimes prefer to upskill the team rather than buy the engagement. If that's you, here are the courses that cover the same ground — taught by our senior engineers in the same voice as our consulting work:
Read alongside this engagement
MCP for Engineering Teams: Connecting Your Internal APIs to AI Without Regretting It
The developer explainer told you what MCP is. This is the other conversation — the one for whoever owns the systems: what connecting internal APIs to AI actually costs, where the security boundary belongs, and when to build the integration in-house versus bringing in review.
Token Economics: The Discipline That Decides Whether Your Agent Ships or Gets Shut Down
Agent systems don't usually die because they're wrong. They die because they're wrong about money. Token economics — budgeting, caching, batching, and model routing as design decisions — is the sixth discipline of production AI engineering, and the one finance will audit.
Durable Execution for Agents: The Fifth Discipline Your .NET Background Already Prepared You For
Spec is the truth. Context is the assembly. Evals are the proof. OpenSpec is the operating system. The substrate underneath — the thing that keeps a multi-step agent alive across retries, restarts, and the human-in-the-loop who walks away from their desk at 5pm — is durable execution. Every .NET engineer who's ever shipped a MassTransit saga already understands the pattern; here's how it maps onto serious AI agents in 2026, and why it's the fifth discipline that finally closes the arc.
What's Included
- Retrieval pipeline design and review (RAG)
- Agent tool boundaries and safety review
- Model selection against latency and cost constraints
- Evaluation (evals) strategy for non-deterministic systems
- Token cost budgeting before launch
What You'll Achieve
- A written AI-feature design your team can execute
- Retrieval and context architecture that returns the right data
- Agents with explicit, reviewable tool boundaries
- An evals plan that catches regressions before users do
- A cost budget the CFO can read
Ready to Get Started?
Contact us today to learn more about how this service can help you